Performance Improvements in .NET Core 3.0

Back when we were getting ready to ship .NET Core 2.0, I wrote a blog post exploring some of the many performance improvements that had gone into it. I enjoyed putting it together so much and received such a positive response to the post that I did it again for .NET Core 2.1, a version for which performance was also a significant focus. With //build last week and .NET Core 3.0‘s release now on the horizon, I’m thrilled to have an opportunity to do it again.

.NET Core 3.0 has a ton to offer, from Windows Forms and WPF, to single-file executables, to async enumerables, to platform intrinsics, to HTTP/2, to fast JSON reading and writing, to assembly unloadability, to enhanced cryptography, and on and on and on… there is a wealth of new functionality to get excited about. For me, however, performance is the primary feature that makes me excited to go to work in the morning, and there’s a staggering amount of performance goodness in .NET Core 3.0.

In this post, we’ll take a tour through some of the many improvements, big and small, that have gone into the .NET Core runtime and core libraries in order to make your apps and services leaner and faster.

Setup

Benchmark.NET has become the preeminent tool for doing benchmarking of .NET libraries, and so as I did in my 2.1 post, I’ll use Benchmark.NET to demonstrate the improvements. Throughout the post, I’ll include the individual snippets of benchmarks that highlight the particular improvement being discussed. To be able to execute those benchmarks, you can use the following setup:

1. Create a directory named BlogPostBenchmarks.
2. In that directory, run dotnet new console.
3. Replace the contents of BlogPostBenchmarks.csproj with the following:

   <Project Sdk="Microsoft.NET.Sdk">
   
     <PropertyGroup>
       <OutputType>Exe</OutputType>
       <AllowUnsafeBlocks>true</AllowUnsafeBlocks>
       <TargetFrameworks>netcoreapp2.1;netcoreapp3.0</TargetFrameworks>
     </PropertyGroup>
   
     <ItemGroup>
       <PackageReference Include="BenchmarkDotNet" Version="0.11.5" />
       <PackageReference Include="System.Drawing.Common" Version="4.5.0" />
       <PackageReference Include="System.IO.Pipelines" Version="4.5.0" />
       <PackageReference Include="System.Threading.Channels" Version="4.5.0" />
     </ItemGroup>
   
   </Project>

4. Replace the contents of Program.cs with the following:

   using BenchmarkDotNet.Attributes;
   using BenchmarkDotNet.Configs;
   using BenchmarkDotNet.Jobs;
   using BenchmarkDotNet.Running;
   using BenchmarkDotNet.Toolchains.CsProj;
   using Microsoft.Win32.SafeHandles;
   using System;
   using System.Buffers;
   using System.Collections;
   using System.Collections.Concurrent;
   using System.Collections.Generic;
   using System.Collections.Immutable;
   using System.Diagnostics;
   using System.Drawing;
   using System.Drawing.Drawing2D;
   using System.Globalization;
   using System.IO;
   using System.IO.Compression;
   using System.IO.Pipelines;
   using System.Linq;
   using System.Net;
   using System.Net.Http;
   using System.Net.NetworkInformation;
   using System.Net.Security;
   using System.Net.Sockets;
   using System.Runtime.CompilerServices;
   using System.Runtime.InteropServices;
   using System.Security.Authentication;
   using System.Security.Cryptography.X509Certificates;
   using System.Text;
   using System.Text.RegularExpressions;
   using System.Threading;
   using System.Threading.Channels;
   using System.Threading.Tasks;
   using System.Xml;
   
   [MemoryDiagnoser]
   public class Program
   {
       static void Main(string[] args) => BenchmarkSwitcher.FromTypes(new[] { typeof(Program) }).Run(args);
   
       // ... paste benchmark code here
   }

To execute a particular benchmark, unless otherwise noted, copy and paste the relevant code to replace the // ...above, and execute dotnet run -c Release -f netcoreapp2.1 --runtimes netcoreapp2.1 netcoreapp3.0 --filter "*Program*". This will compile and run the tests in release builds, on both .NET Core 2.1 and .NET Core 3.0, and print out the results for comparison in a table.

Caveats

A few caveats before we get started:

1. Any discussion involving microbenchmark results deserves a caveat that measurements can and do vary from machine to machine. I’ve tried to pick stable examples to share (and have run these tests on multiple machines in multiple configurations to help validate that), but don’t be too surprised if your numbers differ from the ones I’ve shown; hopefully, however, the magnitude of the improvements demonstrated carries through. All of the shown results are from a nightly Preview 6 build for .NET Core 3.0. Here’s my configuration, as summarized by Benchmark.NET, on my Windows configuration and on my Linux configuration:

   BenchmarkDotNet=v0.11.5, OS=Windows 10.0.17763.437 (1809/October2018Update/Redstone5)
   Intel Core i7-7660U CPU 2.50GHz (Kaby Lake), 1 CPU, 4 logical and 2 physical cores
   .NET Core SDK=3.0.100-preview6-011854
     [Host]     : .NET Core 2.1.9 (CoreCLR 4.6.27414.06, CoreFX 4.6.27415.01), 64bit RyuJIT
     Job-RODBZD : .NET Core 2.1.9 (CoreCLR 4.6.27414.06, CoreFX 4.6.27415.01), 64bit RyuJIT
     Job-TVOWAH : .NET Core 3.0.0-preview6-27712-03 (CoreCLR 3.0.19.26071, CoreFX 4.700.19.26005), 64bit RyuJIT
   
   BenchmarkDotNet=v0.11.5, OS=ubuntu 18.04
   Intel Xeon CPU E5-2673 v4 2.30GHz, 1 CPU, 4 logical and 2 physical cores
   .NET Core SDK=3.0.100-preview6-011877
     [Host]     : .NET Core 2.1.10 (CoreCLR 4.6.27514.02, CoreFX 4.6.27514.02), 64bit RyuJIT
     Job-SSHMNT : .NET Core 2.1.10 (CoreCLR 4.6.27514.02, CoreFX 4.6.27514.02), 64bit RyuJIT
     Job-CHXNFO : .NET Core 3.0.0-preview6-27713-12 (CoreCLR 3.0.19.26071, CoreFX 4.700.19.26307), 64bit RyuJIT

2. Unless otherwise mentioned, benchmarks were executed on Windows. In many cases, performance is equivalent between Windows and Unix, but in others, there can be non-trivial discrepancies between them, in particular in places where .NET relies on OS functionality, and the OS itself has different performance characteristics.
3. I mentioned posts on .NET Core 2.0 and .NET Core 2.1, but I didn’t mention .NET Core 2.2. .NET Core 2.2 was primarily focused on ASP.NET, and while there were terrific performance improvements at the ASP.NET layer in 2.2, the release was primarily focused on servicing for the runtime and core libraries, with most improvements post-2.1 skipping 2.2 and going into 3.0.

With that out of the way, let’s have some fun.

Span and Friends

One of the more notable features introduced in .NET Core 2.1 was Span<T>, along with its friends ReadOnlySpan<T>, Memory<T>, and ReadOnlyMemory<T>. The introduction of these new types came with hundreds of new methods for interacting with them, some on new types and some with overloaded functionality on existing types, as well as optimizations in the just-in-time compiler (JIT) for making working with them very efficient. The release also included some internal usage of Span<T> to make existing operations leaner and faster while still enjoying maintainable and safe code. In .NET Core 3.0, much additional work has gone into further improving all such aspects of these types: making the runtime better at generating code for them, increasing the use of them internally to help improve many other operations, and improving the various library utilities that interact with them to make consumption of these operations faster.

To work with a span, one first needs to get a span, and several PRs have made doing so faster. In particular, passing around a Memory<T> and then getting a Span<T> from it is a very common way of creating a span; this is, for example, how the various Stream.WriteAsync and ReadAsync methods work, accepting a {ReadOnly}Memory<T> (so that it can be stored on the heap) and then accessing its Span property once the actual bytes need to be read or written. PR dotnet/coreclr#20771 improved this by removing an argument validation branch (both for {ReadOnly}Memory<T>.Span and for {ReadOnly}Span<T>.Slice), and while removing a branch is a small thing, in span-heavy code (such as when doing formatting and parsing), small things done over and over and over again add up. More impactful, PR dotnet/coreclr#20386 plays tricks at the runtime level to safely eliminate some of the runtime checked casting and bit masking logic that had been used to enable {ReadOnly}Memory<T> to wrap various types, like string, T[], and MemoryManager<T>, providing a seamless veneer over all of them. The net result of these PRs is a nice speed-up when fishing a Span<T> out of a Memory<T>, which in turn improves all other operations that do so.

private ReadOnlyMemory<byte> _mem = new byte[1];

[Benchmark]
public ReadOnlySpan<byte> GetSpan() => _mem.Span;

Of course, once you get a span, you want to use it, and there are a myriad of ways to use one, many of which have also been optimized further in .NET Core 3.0.

For example, just as with arrays, to pass the data from a span to native code via a P/Invoke, the data needs to be pinned (unless it’s already immovable, such as if the span were created to wrap some natively allocated memory not on the GC heap or if it were created for some data on the stack). To pin a span, the easiest way is to simply rely on the C# language’s support added in C# 7.3 that supports a pattern-based way to use any type with the fixed keyword. All a type need do is expose a GetPinnableReference method (or extension method) that returns a ref T to the data stored in that instance, and that type can be used with fixed. {ReadOnly}Span<T> does exactly this. However, even though {ReadOnly}Span<T>.GetPinnableReference generally gets inlined, a call it makes internally to Unsafe.AsRef was getting blocked from inlining; PR dotnet/coreclr#18274 fixed this, enabling the whole operation to be inlined. Further, the aforementioned code was actually tweaked in PR dotnet/coreclr#20428 to eliminate one branch on the hot path. Both of these combine to result in a measurable boost when pinning a span:

private readonly byte[] _bytes = new byte[10_000];

[Benchmark(OperationsPerInvoke = 10_000)]
public unsafe int PinSpan()
{
    Span<byte> s = _bytes;
    int total = 0;

    for (int i = 0; i < s.Length; i++)
        fixed (byte* p = s) // equivalent to `fixed (byte* p = &s.GetPinnableReference())`
            total += *p;

    return total;
}

It’s worth noting, as well, that if you’re interested in these kinds of micro-optimizations, you might also want to avoid using the default pinning at all, at least on super hot paths. The {ReadOnly}Span<T>.GetPinnableReference method was designed to behave just like pinning of arrays and strings, where null or empty inputs result in a null pointer. This behavior requires an additional check to be performed to see whether the length of the span is zero:

// https://github.com/dotnet/coreclr/blob/52aff202cd382c233d903d432da06deffaa21868/src/System.Private.CoreLib/shared/System/Span.Fast.cs#L168-L174

[EditorBrowsable(EditorBrowsableState.Never)]
public unsafe ref T GetPinnableReference()
{
    // Ensure that the native code has just one forward branch that is predicted-not-taken.
    ref T ret = ref Unsafe.AsRef<T>(null);
    if (_length != 0) ret = ref _pointer.Value;
    return ref ret;
}

If in your code by construction you know that the span will not be empty, you can choose to instead use MemoryMarshal.GetReference, which performs the same operation but without the length check:

// https://github.com/dotnet/coreclr/blob/52aff202cd382c233d903d432da06deffaa21868/src/System.Private.CoreLib/shared/System/Runtime/InteropServices/MemoryMarshal.Fast.cs#L79

public static ref T GetReference<T>(Span<T> span) => ref span._pointer.Value;

Again, while a single check adds minor overhead, when executed over and over and over, that can add up:

private readonly byte[] _bytes = new byte[10_000];

[Benchmark(OperationsPerInvoke = 10_000, Baseline = true)]
public unsafe int PinSpan()
{
    Span<byte> s = _bytes;
    int total = 0;

    for (int i = 0; i < s.Length; i++)
        fixed (byte* p = s) // equivalent to `fixed (byte* p = &s.GetPinnableReference())`
            total += *p;

    return total;
}

[Benchmark(OperationsPerInvoke = 10_000)]
public unsafe int PinSpanExplicit()
{
    Span<byte> s = _bytes;
    int total = 0;

    for (int i = 0; i < s.Length; i++)
        fixed (byte* p = &MemoryMarshal.GetReference(s))
            total += *p;

    return total;
}

Of course, there are many other (and generally preferred) ways to operate over a span’s data than to use fixed. For example, it’s a bit surprising that until Span<T> came along, .NET didn’t have a built-in equivalent of memcmp, but nevertheless, Span<T>‘s SequenceEqual and SequenceCompareTo methods have become go-to methods for comparing in-memory data in .NET. In .NET Core 2.1, both SequenceEqual and SequenceCompareTo were optimized to utilize System.Numerics.Vector for vectorization, but the nature of SequenceEqual made it more amenable to best take advantage. In PR dotnet/coreclr#22127, @benaadams updated SequenceCompareTo to take advantage of the new hardware instrinsics APIs available in .NET Core 3.0 to specifically target AVX2 and SSE2, resulting in significant improvements when comparing both small and large spans. (For more information on hardware intrinsics in .NET Core 3.0, see  and .)

private byte[] _orig, _same, _differFirst, _differLast;

[Params(16, 256)]
public int Length { get; set; }

[GlobalSetup]
public void Setup()
{
    _orig = Enumerable.Range(0, Length).Select(i => (byte)i).ToArray();
    _same = (byte[])_orig.Clone();

    _differFirst = (byte[])_orig.Clone();
    _differFirst[0] = (byte)(_orig[0] + 1);

    _differLast = (byte[])_orig.Clone();
    _differLast[_differLast.Length - 1] = (byte)(_orig[_orig.Length - 1] + 1);
}

[Benchmark]
public int CompareSame() => _orig.AsSpan().SequenceCompareTo(_same);

[Benchmark]
public int CompareDifferFirst() => _orig.AsSpan().SequenceCompareTo(_differFirst);

[Benchmark]
public int CompareDifferLast() => _orig.AsSpan().SequenceCompareTo(_differLast);

As background, “vectorization” is an approach to parallelization that performs multiple operations as part of individual instructions on a single core. Some optimizing compilers can perform automatic vectorization, whereby the compiler analyzes loops to determine whether it can generate functionally equivalent code that would utilize such instructions to run faster. The .NET JIT compiler does not currently perform auto-vectorization, but it is possible to manually vectorize loops, and the options for doing so have significantly improved in .NET Core 3.0. Just as a simple example of what vectorization can look like, imagine having an array of bytes and wanting to search it for the first non-zero byte, returning the position of that byte. The simple solution is to just iterate through all of the bytes:

private byte[] _buffer = new byte[10_000].Concat(new byte[] { 42 }).ToArray();

[Benchmark(Baseline = true)]
public int LoopBytes()
{
    byte[] buffer = _buffer;
    for (int i = 0; i < buffer.Length; i++)
    {
        if (buffer[i] != 0)
            return i;
    }
    return -1;
}

That of course works functionally, and for very small arrays it’s fine. But for larger arrays, we end up doing significantly more work than is actually necessary. Consider instead in a 64-bit process re-interpreting the array of bytes as an array of longs, which Span<T> nicely supports. We then effectively compare 8 bytes at a time rather than 1 byte at a time, at the expense of added code complexity: once we find a non-zero long, we then need to look at each byte it contains to determine the position of the first non-zero one (though there are ways to improve that, too). Similarly, the array’s length may not evenly divide by 8, so we need to be able to handle the overflow.

[Benchmark]
public int LoopLongs()
{
    byte[] buffer = _buffer;
    int remainingStart = 0;

    if (IntPtr.Size == sizeof(long))
    {
        Span<long> longBuffer = MemoryMarshal.Cast<byte, long>(buffer);
        remainingStart = longBuffer.Length * sizeof(long);

        for (int i = 0; i < longBuffer.Length; i++)
        {
            if (longBuffer[i] != 0)
            {
                remainingStart = i * sizeof(long);
                break;
            }
        }
    }

    for (int i = remainingStart; i < buffer.Length; i++)
    {
        if (buffer[i] != 0)
            return i;
    }

    return -1;
}

For longer arrays, this yields really nice wins:

I’ve glossed over some details here, but it should convey the core idea. .NET includes additional mechanisms for vectorizing as well. In particular, the aforementioned System.Numerics.Vector type allows for a developer to write code using Vector and then have the JIT compiler translate that into the best instructions available on the current platform.

[Benchmark]
public int LoopVectors()
{
    byte[] buffer = _buffer;
    int remainingStart = 0;

	if (Vector.IsHardwareAccelerated)
	{
		while (remainingStart <= buffer.Length - Vector<byte>.Count)
		{
			var vector = new Vector<byte>(buffer, remainingStart);
			if (!Vector.EqualsAll(vector, default))
			{
				break;
			}
			remainingStart += Vector<byte>.Count;
		}
	}

    for (int i = remainingStart; i < buffer.Length; i++)
    {
        if (buffer[i] != 0)
            return i;
    }

    return -1;
}

Further, .NET Core 3.0 includes new hardware intrinsics that allow a properly-motivated developer to eke out the best possible performance on supporting hardware, utilizing extensions like AVX or SSE that can compare well more than 8 bytes at a time. Many of the improvements in .NET Core 3.0 come from utilizing these techniques.

Back to examples, copying spans has also improved, thanks to PRs dotnet/coreclr#18006 from @benaadams and dotnet/coreclr#17889, in particular for relatively small spans…

private byte[] _from = new byte[] { 1, 2, 3, 4 };
private byte[] _to = new byte[4];

[Benchmark]
public void CopySpan() => _from.AsSpan().CopyTo(_to);

Searching is one of the most commonly performed operations in any program, and searches with spans are generally performed with IndexOf and its variants (e.g. IndexOfAny and Contains) In PR dotnet/coreclr#20738, @benaadams again utilized vectorization, this time to improve the performance of IndexOfAny when operating over bytes, a particularly common case in many networking-related scenarios (e.g. parsing bytes off the wire as part of an HTTP stack). You can see the effects of this in the following microbenchmark:

private byte[] _arr = Encoding.UTF8.GetBytes("This is a test to see improvements to IndexOfAny.  How'd they work?");
[Benchmark] public int IndexOf() => new Span<byte>(_arr).IndexOfAny((byte)'.', (byte)'?');

I love these kinds of improvements, because they’re low-enough in the stack that they end up having multiplicative effects across so much code. The above change only affected byte, but subsequent PRs were submitted to cover char as well, and then PR dotnet/coreclr#20855 made a nice change that brought these same changes to other primitives of the same sizes. For example, we can recast the previous benchmark to use sbyte instead of byte, and as of that PR, a similar improvement applies:

private sbyte[] _arr = Encoding.UTF8.GetBytes("This is a test to see improvements to IndexOfAny.  How'd they work?").Select(b => (sbyte)b).ToArray();

[Benchmark]
public int IndexOf() => new Span<sbyte>(_arr).IndexOfAny((sbyte)'.', (sbyte)'?');

As another example, consider PR dotnet/coreclr#20275. That change similarly utilized vectorization to improve the performance of To{Upper/Lower}{Invariant}.

private string _src = "This is a source string that needs to be capitalized.";
private char[] _dst = new char[1024];
[Benchmark] public int ToUpperInvariant() => _src.AsSpan().ToUpperInvariant(_dst);

PR dotnet/coreclr#19959 optimizes the Trim{Start/End} helpers on ReadOnlySpan<char>, another very commonly-applied method, with equally exciting results (it’s hard to see with the white space in the results, but the results in the table go in order of the arguments in the Params attribute):

[Params("", " abcdefg ", "abcdefg")]
public string Data;

[Benchmark]
public ReadOnlySpan<char> Trim() => Data.AsSpan().Trim();

Sometimes optimizations are just about being smarter about code management. PR dotnet/coreclr#17890 removed an unnecessary layer of functions that were on many globalization-related code paths, and just removing those extra unnecessary method invocations results in measurable speed-ups when working with small spans, e.g.

[Benchmark]
public bool EndsWith() => "Hello world".AsSpan().EndsWith("world", StringComparison.OrdinalIgnoreCase);

Of course, one of the great things about span is that it is a reusable building-block that enables many higher-level operations. That includes operations on both arrays and strings…

Arrays and Strings

As a theme that’s emerged within .NET Core, wherever possible, new performance-focused functionality should not only be exposed for public use but also be used internally; after all, given the depth and breadth of functionality within .NET Core, if some performance-focused feature doesn’t meet the needs of .NET Core itself, there’s a reasonable chance it also won’t meet the public need. As such, internal usage of new features is a key benchmark as to whether the design is adequate, and in the process of evaluating such criteria, many additional code paths benefit, and these improvements have a multiplicative effect.

This isn’t just about new APIs. Many of the language features introduced in C# 7.2, 7.3, and 8.0 are influenced by the needs of .NET Core itself and have been used to improve things that we couldn’t reasonably improve before (other than dropping down to unsafe code, which we try to avoid when possible). For example, PR dotnet/coreclr#17891 speeds up Array.Reverse by taking advantage of the C# 7.2 ref locals feature and the 7.3 ref local reassignment feature. Using the new feature allows for the code to be expressed in a way that lets the JIT generate better code for the inner loop, and in turn results in a measurable speed-up:

private int[] _arr = Enumerable.Range(0, 256).ToArray();

[Benchmark]
public void Reverse() => Array.Reverse(_arr);

Another example for arrays, the Clear method improved in PR dotnet/coreclr#24302, which works around an alignment issue that results in the underlying memset used to implement the operation being up to 2x slower. The change manually clears up to a few bytes one by one, such that the pointer we then hand off to memset is properly aligned. If you got “lucky” previously and the array happened to be aligned, performance was fine, but if it wasn’t aligned, there was a non-trivial performance hit incurred. This benchmark simulates the unlucky case:

[GlobalSetup]
public void Setup()
{
    while (true)
    {
        var buffer = new byte[8192];
        GCHandle handle = GCHandle.Alloc(buffer, GCHandleType.Pinned);
        if (((long)handle.AddrOfPinnedObject()) % 32 != 0)
        {
            _handle = handle;
            _buffer = buffer;
            return;
        }
        handle.Free();
    }
}

[GlobalCleanup]
public void Cleanup() => _handle.Free();

private GCHandle _handle;
private byte[] _buffer;

[Benchmark] public void Clear() => Array.Clear(_buffer, 0, _buffer.Length);

That said, many of the improvements are in fact based on new APIs. Span is a great example of this. It was introduced in .NET Core 2.1, and the initial push was to get it to be usable and expose sufficient surface area to allow it to be used meaningfully. But at the same time, we started utilizing it internally in order to both vet the design and benefit from the improvements it enables. Some of this was done in .NET Core 2.1, but the effort continues in .NET Core 3.0. Arrays and strings are both prime candidates for such optimizations.

For example, many of the same vectorization optimizations applied to spans are similarly applied to arrays. PR dotnet/coreclr#21116 from @benaadams optimized Array.{Last}IndexOf for both bytes and chars, utilizing the same internal helpers that were written to enable spans, and to similar effect:

private char[] _arr = "This is a test to see improvements to IndexOf.  How'd they work?".ToCharArray();

[Benchmark]
public int IndexOf() => Array.IndexOf(_arr, '.');

And as with spans, thanks to PR dotnet/coreclr#24293 from @dschinde, these IndexOfoptimizations also now apply to other primitives of the same size.

private short[] _arr = "This is a test to see improvements to IndexOf.  How'd they work?".Select(c => (short)c).ToArray();

[Benchmark]
public int IndexOf() => Array.IndexOf(_arr, (short)'.');

Vectorization optimizations have been applied to strings, too. You can see the effect of PR dotnet/coreclr#21076 from @benaadams in this microbenchmark:

[Benchmark]
public int IndexOf() => "Let's see how fast we can find the period towards the end of this string.  Pretty fast?".IndexOf('.', StringComparison.Ordinal);

Also note in the above that the .NET Core 2.1 operation allocates (due to converting the search character into a string), whereas the .NET Core 3.0 implementation does not. That’s thanks to PR dotnet/coreclr#19788 from @benaadams.

There are of course pieces of functionality that are more unique to strings (albeit also applicable to new functionality exposed on spans), such as hash code computation with various string comparison methods. For example, PR dotnet/coreclr#20309/ improved the performance of String.GetHashCode when performing OrdinalIgnoreCase operations, which along with Ordinal (the default) represent the two most common modes.

[Benchmark]
public int GetHashCodeIgnoreCase() => "Some string".GetHashCode(StringComparison.OrdinalIgnoreCase);

OrdinalsIgnoreCase has been improved for other uses as well. For example, PR dotnet/coreclr#20734 improved String.Equals when using StringComparer.OrdinalIgnoreCaseby both vectorizing (checking two chars at a time instead of one) and removing branches from an inner loop:

[Benchmark]
public bool EqualsIC() => "Some string".Equals("sOME sTrinG", StringComparison.OrdinalIgnoreCase);

The previous cases are examples of functionality in String‘s implementation, but there are lots of ancillary string-related functionality that have seen improvements as well. For example, various operations on Char have been improved, such as Char.GetUnicodeCategory via PRs dotnet/coreclr#20983 and dotnet/coreclr#20864:

[Params('.', 'a', 'x05D0')]
public char Char { get; set; }

[Benchmark]
public UnicodeCategory GetCategory() => char.GetUnicodeCategory(Char);

Those PRs also highlight another case of benefiting from a language improvement. As of C# 7.3, the C# compiler is able to optimize properties of the form:

static ReadOnlySpan<byte> s_byteData => new byte[] { … /* constant bytes */ }

// Run with: dotnet run -c Release -f netcoreapp2.1 --filter *Program* --runtimes netcoreapp3.0

private static byte[] ArrayProp { get; } = new byte[] { 1, 2, 3 };

[Benchmark(Baseline = true)]
public ReadOnlySpan<byte> GetArrayProp() => ArrayProp;

private static ReadOnlySpan<byte> SpanProp => new byte[] { 1, 2, 3 };

[Benchmark]
public ReadOnlySpan<byte> GetSpanProp() => SpanProp;

Another string-related area that’s gotten some attention is StringBuilder (not necessarily improvements to StringBuilder itself, although it has received some of those, for example a new overload in PR dotnet/coreclr#20773 from @Wraith2 that helps avoid accidentally boxing and creating a string from a ReadOnlyMemory<char> appended to the builder). Rather, in many situations StringBuilders have been used for convenience but added cost, and with just a little work (and in some cases the new String.Create method introduced in .NET Core 2.1), we can eliminate that overhead, in both CPU usage and allocation. Here a few examples…

[Benchmark]
public IPHostEntry GetHostEntry() => Dns.GetHostEntry("34.206.253.53");

private static CultureInfo CreateCulture()
{
    var c = new CultureInfo("he-IL");
    c.DateTimeFormat.Calendar = new HebrewCalendar();
    return c;
}

private CultureInfo _hebrewIsrael = CreateCulture();

[Benchmark]
public string FormatHebrew() => new DateTime(2018, 11, 20).ToString(_hebrewIsrael);

private readonly PhysicalAddress _short = new PhysicalAddress(new byte[1] { 42 });
private readonly PhysicalAddress _long = new PhysicalAddress(Enumerable.Range(0, 256).Select(i => (byte)i).ToArray());

[Benchmark]
public void PAShort() => _short.ToString();

[Benchmark]
public void PALong() => _long.ToString();

private X509Certificate2 _cert = GetCert();

private static X509Certificate2 GetCert()
{
    using (var client = new Socket(AddressFamily.InterNetwork, SocketType.Stream, ProtocolType.Tcp))
    {
        client.Connect("microsoft.com", 443);
        using (var ssl = new SslStream(new NetworkStream(client)))
        {
            ssl.AuthenticateAsClient("microsoft.com", null, SslProtocols.None, false);
            return new X509Certificate2(ssl.RemoteCertificate);
        }
    }
}

[Benchmark]
public string CertProp() => _cert.Thumbprint;

and so on. These PRs demonstrate that good gains can be had simply by making small tweaks that make existing code paths cheaper, and that expands well beyond StringBuilder. There are lots of places within .NET Core, for example, where String.Substring is used, and many of those cases can be replaced with use of AsSpan and Slice, for example as was done in PR dotnet/corefx#29402 by @juliushardt, or PRs dotnet/coreclr#17916 and dotnet/corefx#29539, or as was done in PRs dotnet/corefx#29227 and dotnet/corefx#29721 to remove string allocations from FileSystemWatcher, delaying the creation of such strings until only when it was known they were absolutely necessary.

[Benchmark]
public void HtmlDecode() => WebUtility.HtmlDecode("水水水水水水水");

Another example of using new APIs to improve existing functionality is with String.Concat. .NET Core 3.0 has several new String.Concat overloads, ones that accept ReadOnlySpan<char> instead of string. These make it easy to avoid allocations/copies of substrings in cases where concatenating pieces of other strings: instead of using String.Concat with String.Substring, it’s used instead with String.AsSpan(...) or Slice. In fact, the PRs dotnet/coreclr#21766 and dotnet/corefx#34451 that implemented, exposed, and added tests for these new overloads also added tens of call sites to the new overloads across .NET Core. Here’s an example of the impact one of those has, improving the performance of accessing Uri.DnsSafeHost:

[Benchmark]
public string DnsSafeHost() => new Uri("http://[fe80::3]%1").DnsSafeHost;

Another example, using Path.ChangeExtension to change from one non-null extension to another:

[Benchmark]
public string ChangeExtension() => Path.ChangeExtension("filename.txt", ".dat");

Finally, a very closely related area is that of encoding. A bunch of improvements were made in .NET Core 3.0 around Encoding, both in general and for specific encodings, such as PR dotnet/coreclr#18263 that allowed an existing corner-case optimization to be applied for Encoding.Unicode.GetString in many more cases, or dotnet/coreclr#18487 that removed a bunch of unnecessary virtual indirections from various encoding implementations, or PR dotnet/coreclr#20768 that improved the performance of Encoding.Preamble by taking advantage of the same metadata-blob span support discussed earlier, or PRs dotnet/coreclr#21948 and dotnet/coreclr#23098 that overhauled and streamlined the implementions of UTF8Encoding and AsciiEncoding.

private byte[] _data = Encoding.ASCII.GetBytes("This is a test of ASCII encoding. It's faster now.");

[Benchmark]
public string ASCII() => Encoding.ASCII.GetString(_data);

These examples all served to highlight improvements made in and around strings. That’s all well and good, but where the improvements related to strings really start to shine is when looking at improvements around formatting and parsing.

Parsing/Formatting

Parsing and formatting are the lifeblood of any modern web app or service: take data off the wire, parse it, manipulate it, format it back out. As such, in .NET Core 2.1 along with bringing up Span<T>, we invested in the formatting and parsing of primitives, from Int32 to DateTime. Many of those changes can be read about in my previous blog posts, but one of the key factors in enabling those performance improvements was in moving a lot of native code to managed. That may be counter-intuitive, in that it’s “common knowledge” that C code is faster than C# code. However, in addition to the gap between them narrowing, having (mostly) safe C# code has made the code base easier to experiment in, so whereas we may have been skittish about tweaking the native implementations, the community-at-large has dived head first into optimizing these implementations wherever possible. That effort continues in full force in .NET Core 3.0, with some very nice rewards reaped.

Let’s start with core integer primitives. PR dotnet/coreclr#18897 added a variety of special paths for the parsing of Integer-style signed values (e.g. Int32 and Int64), PR dotnet/coreclr#18930 added similar support for unsigned (e.g. UInt32 and UInt64), and PR dotnet/coreclr#18952 did a similar pass for hex. On top of those, PR dotnet/coreclr#21365 layered in additional optimizations, for example utilizing those changes for primitives like byte, skipping unnecessary layers of functions, streamlining some calls to improve inlining, and further reducing branching. The net impact here are some significant improvements to the performance of parsing integer primitive types in this release.

[Benchmark]
public int ParseInt32Dec() => int.Parse("12345678");

[Benchmark]
public int ParseInt32Hex() => int.Parse("BC614E", NumberStyles.HexNumber);

Formatting of such types was also improved, even though it had already been improved significantly between .NET Core 2.0 and .NET Core 2.1. PR dotnet/coreclr#19551 tweaked the structure of the code to avoid needing to access the current culture number formatting data if it wouldn’t be needed (e.g. when formatting a value as hex, there’s no customization based on current culture), and PR dotnet/coreclr#18935 improved decimal formatting performance, in large part by optimizing how data is passed around (or not passed at all).

[Benchmark]
public string DecimalToString() => 12345.6789m.ToString();

In fact, System.Decimal itself has been overhauled in .NET Core 3.0, as of PR dotnet/coreclr#18948 now with an entirely managed implementation, and with additional performance work in PRs like dotnet/coreclr#20305.

private decimal _a = 67891.2345m;
private decimal _b = 12345.6789m;

[Benchmark]
public decimal Add() => _a + _b;

[Benchmark]
public decimal Subtract() => _a - _b;

[Benchmark]
public decimal Multiply() => _a * _b;

[Benchmark]
public decimal Divide() => _a / _b;

[Benchmark]
public decimal Mod() => _a % _b;

[Benchmark]
public decimal Floor() => decimal.Floor(_a);

[Benchmark]
public decimal Round() => decimal.Round(_a);

Back to formatting and parsing, there are even some new formatting special-cases that might look silly at first, but that represent optimizations targeting real-world cases. In some sizeable web applications, we found that a large number of strings on the managed heap were simple integral values like “0” and “1”. And since the fastest code is code you don’t need to execute at all, why bother allocating and formatting these small numbers over and over when we can instead just cache and reuse the results (effectively our own string interning pool)? That’s what PR dotnet/coreclr#18383 does, creating a small, specialized cache of the strings for “0” through “9”, and any time we now find ourselves formatting a single-digit integer primitive, we instead just grab the relevant string from this cache.

private int _digit = 4;

[Benchmark]
public string SingleDigitToString() => _digit.ToString();

Enums have also seen sizable parsing and formatting improvements in .NET Core 3.0. PR dotnet/coreclr#21214 improved the handling of Enum.Parse and Enum.TryParse, for both the generic and non-generic variants. PR dotnet/coreclr#21254 improved the performance of ToString when dealing with [Flags] enums, and PR dotnet/coreclr#21284 further improved other ToString cases. The net effect of these changes is a sizeable improvement in Enum-related performance:

[Benchmark]
public DayOfWeek EnumParse() => Enum.Parse<DayOfWeek>("Thursday");

[Benchmark]
public string EnumToString() => NumberStyles.Integer.ToString();

In .NET Core 2.1, DateTime.TryFormat and ToString were optimized for the commonly-used “o” and “r” formats; in .NET Core 3.0, the parsing equivalents get a similar treatment. PR dotnet/coreclr#18800 significantly improves the performance of parsing DateTime{Offset}s formatted with the Roundtrip “o” format, and PR dotnet/coreclr#18771 does the same for the RFC1123 “r” format. For any serialization formats heavy in DateTimes, these improvements can make a substantial impact:

private string _r = DateTime.Now.ToString("r");
private string _o = DateTime.Now.ToString("o");

[Benchmark]
public DateTime ParseR() => DateTime.ParseExact(_r, "r", null);

[Benchmark]
public DateTime ParseO() => DateTime.ParseExact(_o, "o", null);

Tying back to the StringBuilder discussion from earlier, default DateTime formatting was also improved by PR dotnet/coreclr#22111, tweaking how DateTime internally interacts with a StringBuilder that’s used to build up the resulting state.

private DateTime _now = DateTime.Now;

[Benchmark]
public string DateTimeToString() => _now.ToString();

TimeSpan formatting was also significantly improved, via PR dotnet/coreclr#18990:

private TimeSpan _ts = new TimeSpan(3, 10, 2, 34, 567);

[Benchmark]
public string TimeSpanToString() => _ts.ToString();

Guid parsing also got in the perf-optimization game, with PR dotnet/coreclr#20183 improved parsing performance of Guid, primarily by avoiding overhead in helper routines, as well as by avoiding some searches used to determine which parsing routines to employ.

private string _guid = Guid.NewGuid().ToString("D");

[Benchmark]
public Guid ParseGuid() => Guid.ParseExact(_guid, "D");

Related, PR dotnet/coreclr#21336 again takes advantage of vectorization to improve Guid‘s construction and formatting to and from byte arrays and spans:

private Guid _guid = Guid.NewGuid();
private byte[] _buffer = new byte[16];

[Benchmark]
public void GuidToFromBytes()
{
    _guid.TryWriteBytes(_buffer);
    _guid = new Guid(_buffer);
}

Regular Expressions

Often related to parsing is the area of regular expressions. A bit of work was done on System.Text.RegularExpressions in .NET Core 3.0. PR dotnet/corefx#30474 replaced some usage of an internal StringBuilder cache with a ref struct-based builder that takes advantage of stack-allocated space and pooled buffers. And PR dotnet/corefx#30632 continued the effort by taking further advantage of spans. But the biggest improvement came in PR dotnet/corefx#32899 from @Alois-xx, which tweaks the code generated for a RegexOptions.Compiled Regex to avoid gratuitous thread-local accesses to look up the current culture. This is particularly impactful when also using RegexOptions.IgnoreCase. To see the impact, I found a complicated Regex that used both Compiled and IgnoreCase, and put it into a benchmark:

// Pattern and options copied from https://github.com/microsoft/referencesource/blob/aaca53b025f41ab638466b1efe569df314f689ea/System.ComponentModel.DataAnnotations/DataAnnotations/EmailAddressAttribute.cs#L54-L55
private Regex _regex = new Regex(
    @"^((([a-z]|d|[!#$%&'*+-/=?^_`{|}~]|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF])+(.([a-z]|d|[!#$%&'*+-/=?^_`{|}~]|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF])+)*)|((x22)((((x20|x09)*(x0dx0a))?(x20|x09)+)?(([x01-x08x0bx0cx0e-x1fx7f]|x21|[x23-x5b]|[x5d-x7e]|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF])|(\([x01-x09x0bx0cx0d-x7f]|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF]))))*(((x20|x09)*(x0dx0a))?(x20|x09)+)?(x22)))@((([a-z]|d|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF])|(([a-z]|d|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF])([a-z]|d|-|.|_|~|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF])*([a-z]|d|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF]))).)+(([a-z]|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF])|(([a-z]|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF])([a-z]|d|-|.|_|~|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF])*([a-z]|[u00A0-uD7FFuF900-uFDCFuFDF0-uFFEF]))).?$",
    RegexOptions.Compiled | RegexOptions.IgnoreCase | RegexOptions.ExplicitCapture);

[Benchmark]
public bool RegexCompiled() => _regex.IsMatch("[email protected]");

Threading

Threading is one of those things that’s ever-present and yet most apps and libraries don’t need to explicitly interact with most of the time. That makes it an area ripe for runtime performance improvements to drive down overhead as much as possible, so that user code just gets faster. Previous releases of .NET Core saw a lot of investment in this area, and .NET Core 3.0 continues the trend. This is another area where new APIs have been exposed and then also used in .NET Core itself for further gain.

For example, historically the only work item types that could be queued to the ThreadPool were ones implemented in the runtime, namely those created by ThreadPool.QueueUserWorkItem and friends, by Task, by Timer, and other such core types. But in .NET Core 3.0, the ThreadPool has an UnsafeQueueUserWorkItem overload that accepts the newly public IThreadPoolWorkItem interface. This interface is very simple, with a single method that just Executes work, and that means that any object that implements this interface can be queued directly to the thread pool. This is advanced; most code is just fine using the existing work item types. But this additional option affords a lot of flexibility, in particular in being able to implement the interface on a reusable object that can be queued over and over again to the pool. This is now used in a bunch of additional places in .NET Core 3.0.

One such place is in System.Threading.Channels. The Channels library introduced in .NET Core 2.1 already had a fairly low allocation profile, but there were still times it would allocate. For example, one of the options when creating a channel is whether continuations created by the library should run synchronously or asynchronously as part of a task completing (e.g. when a TryWrite call on a channel wakes up a corresponding ReadAsync, whether the continuation from that ReadAsync invoked synchronously or queued by the TryWrite call). The default is that continuations are never invoked synchronously, but that also then requires allocating an object as part of queueing the continuation to the thread pool. With PR dotnet/corefx#33080, the reusable IValueTaskSource implementation that already backs the ValueTasks returned from ReadAsync calls also implements IThreadPoolWorkItem and can thus itself be queued, avoiding that allocation. This can have a measurable impact on throughput.

// Run with: dotnet run -c Release -f netcoreapp2.1 --filter *Program*

private sealed class Config : ManualConfig // also add [Config(typeof(Config))] to the Program class
{
    public Config()
    {
        Add(Job.MediumRun.With(CsProjCoreToolchain.NetCoreApp21).WithNuGet("System.Threading.Channels", "4.5.0").WithId("4.5.0"));
        Add(Job.MediumRun.With(CsProjCoreToolchain.NetCoreApp30).WithNuGet("System.Threading.Channels", "4.6.0-preview5.19224.8").WithId("4.6.0-preview5.19224.8"));
    }
}

private Channel<int> _channel1 = Channel.CreateUnbounded<int>();
private Channel<int> _channel2 = Channel.CreateUnbounded<int>();

[GlobalSetup]
public void Setup()
{
    Task.Run(async () =>
    {
        var reader = _channel1.Reader;
        var writer = _channel2.Writer;
        while (true)
        {
            writer.TryWrite(await reader.ReadAsync());
        }
    });
}

[Benchmark]
public async Task PingPong()
{
    var writer = _channel1.Writer;
    var reader = _channel2.Reader;
    for (int i = 0; i < 10_000; i++)
    {
        writer.TryWrite(i);
        await reader.ReadAsync();
    }
}

IThreadPoolWorkItem is now also utilized in other places, like in ConcurrentExclusiveSchedulerPair (a little known but useful type that provides an exclusive scheduler that limits execution to only one task at a time, a concurrent scheduler that limits a user-defined number of tasks to run at a time, and that coordinate with each other so that no concurrent tasks may run while an exclusive task is running, ala a reader-writer lock), which now implements IThreadPoolWorkItem on an internally reusable work item object such that it also can avoid allocations when queueing its own processors. It’s also used in ASP.NET Core, and is one of the reasons key ASP.NET benchmarks are ammortized to 0 allocations per request. But by far the most impactful new implementer is in the async/await infrastructure.

In .NET Core 2.1, the runtime’s support for async/await was overhauled, drastically reducing the overheads involved in async methods. Previously when an async method awaited for the first time an awaitable that wasn’t yet complete, the struct-based state machine for the async method would be boxed (literally a runtime box) to the heap. With .NET Core 2.1, we changed that to instead use a generic object that stores the struct as a field on it. This has a myriad of benefits, but one of these benefits is that it now enables us to implement additional interfaces on that object, such as implementing IThreadPoolWorkItem. PR dotnet/coreclr#20159 does exactly that, and it enables another large swath of scenarios to have further reduced allocations, in particular situations where TaskCreationOptions.RunContinuationsAsynchronously was used with a TaskCompletionSource<T>. This can be seen in a benchmark like the following.

// Run with: dotnet run -c Release -f netcoreapp2.1 --filter *Program*

private sealed class Config : ManualConfig // also add [Config(typeof(Config))] to the Program class
{
    public Config()
    {
        Add(Job.MediumRun.With(CsProjCoreToolchain.NetCoreApp21).WithNuGet("System.Threading.Channels", "4.5.0").WithId("4.5.0"));
        Add(Job.MediumRun.With(CsProjCoreToolchain.NetCoreApp30).WithNuGet("System.Threading.Channels", "4.6.0-preview5.19224.8").WithId("4.6.0-preview5.19224.8"));
    }
}

private Channel<TaskCompletionSource<bool>> _channel = Channel.CreateUnbounded<TaskCompletionSource<bool>>();

[GlobalSetup]
public void Setup()
{
    Task.Run(async () =>
    {
        var reader = _channel.Reader;
        while (true) (await reader.ReadAsync()).TrySetResult(true);
    });
}

[Benchmark]
public async Task AsyncAllocs()
{
    var writer = _channel.Writer;
    for (int i = 0; i < 1_000_000; i++)
    {
        var tcs = new TaskCompletionSource<bool>(TaskCreationOptions.RunContinuationsAsynchronously);
        writer.TryWrite(tcs);
        await tcs.Task;
    }
}

That change allowed subsequent optimizations, such as PR dotnet/coreclr#20186 using it to make await Task.Yield(); allocation-free:

[Benchmark]
public async Task Yield()
{
    for (int i = 0; i < 1_000_000; i++)
    {
        await Task.Yield();
    }
}

It’s even utilized further in Task itself. There’s an interesting race condition that has to be handled in awaitables: what happens if the awaited operation completes after the call to IsCompleted but before the call to OnCompleted? As a reminder, the code:

compiles down to code along the lines of:

var $awaiter = something.GetAwaiter();
if (!$awaiter.IsCompleted)
{
    _state = 42;
    AwaitOnCompleted(ref $awaiter);
    return;
}
Label42:
$awaiter.GetResult();

Once we go down the path of IsCompleted having returned false, we’re going to call AwaitOnCompleted and return. If the operation has completed by the time we call AwaitOnCompleted, we don’t want to synchronously invoke the continuation that re-enters this state machine, as we’ll be doing so further down the stack, and if that happened repeatedly, we’d “stack dive” and could end up overflowing the stack. Instead, we’re forced to queue the continuation. This case isn’t the common case, but it happens more often than you might expect, as it simply requires an operation that completes asynchronously very quickly (various networking operations often fall into this category). As of PR dotnet/coreclr#22373, the runtime now takes advantage of the async state machine box object implementing IThreadPoolWorkItem to avoid the allocations in this case as well!

In addition to IThreadPoolWorkItem being used with async/await to allow the async implementation to queue work items to the thread pool in a more allocation-friendly manner just as any other code can, changes were also made that give the ThreadPool 1st-hand knowledge of the state machine box in order to help it optimize additional cases. PR dotnet/coreclr#21159 from @benaadams teaches the ThreadPool to re-route some UnsafeQueueUserWorkItem(Action<object>, object, bool) calls to instead use UnsafeQueueUserWorkItem(IAsyncStateMachineBox, bool) under the covers, so that higher-level libraries can get these allocation benefits without having to be aware of the box machinery.

Another async-related area that’s seen measurable improvements are Timers. In .NET Core 2.1, some important improvements were made to System.Threading.Timers to help improve throughput and minimize contention for a common case where timers aren’t firing, but instead are quickly created and destroyed. And while those changes help a bit with the case when timers do actually fire, they didn’t help with the majority costs and sources of contention in that case, which is that potentially a lot of work (proportional to the number of timers registered) was done while holding locks. .NET Core 3.0 makes some big improvements here. PR dotnet/coreclr#20302 partitions the internal list of registered timers into two lists: one with timers that will soon fire and one with timers that won’t fire for a while. In most workloads that have a lot of registered timers, the majority of timers fall into the latter bucket at any given point in time, and this partitioning scheme enables the runtime to only consider the small bucket when firing timers most of the time. In doing so, it can significantly reduce the costs involved in firing timers, and as a result, also significantly reduce contention on the lock held while manipulating those lists. One customer who tried out these changes after having experienced issues due to tons of active timers had this to say about the impact:

“We got the change in production yesterday and the results are amazing, with 99% reduction in lock contention. We have also measured 4-5% CPU gains, and more importantly 0.15% improvement in reliability for our service (which is huge!).”

The nature of the scenario makes it a little difficult to see the impact in a Benchmark.NET benchmark, so we’ll do something a little different. Rather than measuring the thing that was actually changed, we’ll measure something else that’s indirectly impacted. In particular, these changes didn’t directly impact the performance of creating and destroying timers; in fact, one of the goals was to avoid doing so (in particular to avoid harming that important path). But by reducing the costs of firing timers, we reduce how long locks are held, which then also reduces the contention that the creating/destroying of timers faces. So, our benchmark creates a bunch of timers, ranging in when and how often they fire, and then we time how long it takes to create and destroy a bunch of additional timers.

private Timer[] _timers;

[GlobalSetup]
public void Setup()
{
    _timers = new Timer[1_000_000];
    for (int i = 0; i < _timers.Length; i++)
    {
        _timers[i] = new Timer(_ => { }, null, i, i);
    }
    Thread.Sleep(1000);
}

[Benchmark]
public void CreateDestroy()
{
    for (int i = 0; i < 1_000; i++)
    {
        new Timer(_ => { }, 0, 100, 100).Dispose();
    }
}

Timer improvements have also taken other forms. For example, PR dotnet/coreclr#22233 from @benaadams shrinks the allocation involved in Task.Delay when used without a CancellationToken by 24 bytes, and PR dotnet/coreclr#20509 reduces the timer-related allocations involved in creating timed CancellationTokenSources, which also has a nice effect on throughput:

[Benchmark]
public void CTSTimer()
{
    using (var cts = new CancellationTokenSource())
        cts.CancelAfter(1_000_000);
}

There are other even lower-level improvements that have gone into the release. For example, PR dotnet/coreclr#21328 from @benaadams improved Thread.CurrentThread by changing the implementation to store the relevant Thread in a [ThreadStatic] field rather than forcing CurrentThread to make an InternalCall into the native portions of the runtime.

[Benchmark]
public Thread CurrentThread() => Thread.CurrentThread;

As other examples, PR dotnet/coreclr#23747 taught the runtime to better respect Docker –cpu limits, PRs dotnet/coreclr#21722 and dotnet/coreclr#21586 improved spinning behavior when contention was encountered across a variety of synchronization sites, PR dotnet/coreclr#22686 improved performance of SemaphoreSlim when consumers of an instance were mixing both synchronous Waits and asynchronous WaitAsyncs, and PR dotnet/coreclr#18098 from @Quogu special-cased CancellationTokenSource created with a timeout of 0 to avoid Timer-related costs.

Collections

Moving on from threading, let’s explore some of the performance improvements that have gone into collections. Collections are so commonly used in pretty much every program that they’ve received a lot of performance-focused attention in previous .NET Core releases. Even so, there continues to be areas for improvement. Here are some example such improvements in .NET Core 3.0.

ConcurrentDictionary<TKey, TValue> has an IsEmpty property that states whether the dictionary is empty at that moment-in-time. In previous releases, it took all of the dictionary’s locks in order to get a proper moment-in-time answer. But as it turns out, those locks only need to be held if we think the collection might be empty: if we see anything in any of the dictionary’s internals buckets, the locks aren’t needed, as we’d stop looking at additional buckets anyway the moment we found one bucket to contain anything. Thus, PR dotnet/corefx#30098 from @drewnoakes added a fast path that first checks each bucket without the locks, in order to optimize for the common case where the dictionary isn’t empty (the impact on the case where the dictionary is empty is minimal).

private ConcurrentDictionary<int, int> _cd;

[GlobalSetup]
public void Setup()
{
    _cd = new ConcurrentDictionary<int, int>();
    _cd.TryAdd(1, 1);
}

[Benchmark] public bool IsEmpty() => _cd.IsEmpty;

ConcurrentDictionary wasn’t the only concurrent collection to get some attention. An improvement came to ConcurrentQueue<T> in dotnet/coreclr#18035, and it’s an interesting example in how performance optimization often is a trade-off between scenarios. In .NET Core 2.0, we overhauled the ConcurrentQueue implementation in a way that significantly improved throughput while also significantly reducing memory allocations, turning the ConcurrentQueue into a linked list of circular arrays. However, the change involved a concession: because of the producer/consumer nature of the arrays, if any operation needed to observe data in-place in a segment (rather than dequeueing it), the segment that was observed would be “frozen” for any further enqueues… this was to avoid problems where, for example, one thread was enumerating the contents of the segment while another thread was enqueueing and dequeueing. When there were multiple segments in the queue, accessing Count ended up being treated as an observation, but that meant that simply accessing the ConcurrentQueue‘s Count would render all of the multiple segments in the queue dead for further enqueues. The theory at the time was that such a trade-off was fine, because no one should be accessing the Count of the queue frequently enough for this to matter. That theory was wrong, and several customers reported significant slowdowns in their workloads because they were accessing the Count on every enqueue or dequeue. While the right solution is in general to avoid doing that, we wanted to fix this, and as it turns out, the fix was relatively straightforward, such that we could have our performance cake and eat it, too. The results are very obvious in the following benchmark.

private ConcurrentQueue<int> _cq;

[GlobalSetup]
public void Setup()
{
    _cq = new ConcurrentQueue<int>();
    for (int i = 0; i < 100; i++)
    {
        _cq.Enqueue(i);
    }
}

[Benchmark]
public void EnqueueCountDequeue()
{
    _cq.Enqueue(42);
    _ = _cq.Count;
    _cq.TryDequeue(out _);
}

ImmutableDictionary<TKey, TValue> also got some attention. A customer reported that they’d compared ImmutableDictionary<TKey, TValue> and Dictionary<TKey, TValue> and found the former to be measurably slower for lookups. This is to be expected, as the types use very different data structures, with ImmutableDictionary optimized for being able to inexpensively create a copy of the dictionary with a mutation, something that’s quite expensive to do with Dictionary; the trade-off is that it ends up being slower for lookups. Still, it caused us to take a look at the costs involved in ImmutableDictionary lookups, and PR dotnet/corefx#35759 includes several tweaks to improve it, changing a recursive call to be non-recursive and inlinable and avoiding some unnecessary struct wrapping. While this doesn’t make ImmutableDictionary and Dictionary lookups equivalent, it does improve ImmutableDictionary measurably, especially when it contains just a few elements.

private ImmutableDictionary<int, int> _hundredInts;

[GlobalSetup]
public void Setup()
{
    _hundredInts = ImmutableDictionary.Create<int, int>();
    for (int i = 0; i < 100; i++)
    {
        _hundredInts = _hundredInts.Add(i, i);
    }
}

[Benchmark]
public int Lookup()
{
    int count = 0;
    {
        for (int i = 0; i < 100; i++)
        {
            for (int j = 0; j < 100; j++)
            {
                if (_hundredInts.TryGetValue(j, out _))
                {
                    count++;
                }
            }
        }
    }
    return count;
}

Another collection that’s seen measurable improvements in .NET Core 3.0 is BitArray. Lots of operations, including construction, were optimized in PR dotnet/corefx#33367.

private byte[] _bytes = Enumerable.Range(0, 100).Select(i => (byte)i).ToArray();

[Benchmark]
public BitArray BitArrayCtor() => new BitArray(_bytes);

Core operations like Set and Get were further improved in PR dotnet/corefx#35364 from @omariom by streamlining the relevant methods and making them inlineable

private BitArray _ba = new BitArray(Enumerable.Range(0, 1000).Select(i => i % 2 == 0).ToArray());

[Benchmark]
public void GetSet()
{
    BitArray ba = _ba;
    for (int i = 0; i < 1000; i++)
    {
        ba.Set(i, !ba.Get(i));
    }
}

while other operations like Or, And, and Xor were vectorized in PR dotnet/corefx#33781. This benchmark highlights some of the wins.

private BitArray _ba1 = new BitArray(Enumerable.Range(0, 1000).Select(i => i % 2 == 0).ToArray());
private BitArray _ba2 = new BitArray(Enumerable.Range(0, 1000).Select(i => i % 2 == 1).ToArray());

[Benchmark]
public void Xor() => _ba1.Xor(_ba2);

Another example: SortedSet<T>. PR dotnet/corefx#30921 from @acerbusace tweaks how GetViewBetween changes how counts of the overall set and subset are managed, resulting in a nice performance boost.

private SortedSet<int> _set = new SortedSet<int>(Enumerable.Range(0, 1000));

[Benchmark]
public int EnumerateViewBetween()
{
    int count = 0;
    foreach (int item in _set.GetViewBetween(100, 200)) count++;
    return count;
}

Comparers have also seen some nice improvements in .NET Core 3.0. For example, PR dotnet/coreclr#21604 overhauled how comparers for enums are implemented in the runtime, borrowing the approach used in CoreRT. It’s often the case that performance optimizations involve adding code; this is one of those fortuitous cases where the better approach is not only faster, it’s also simpler and smaller.

private enum ExampleEnum : byte { A, B }

[Benchmark]
public void CompareEnums()
{
    var comparer = Comparer<ExampleEnum>.Default;
    for (int i = 0; i < 100_000_000; i++)
    {
        comparer.Compare(ExampleEnum.A, ExampleEnum.B);
    }
}

Networking

From the Kestrel web server running on System.Net.Sockets and System.Net.Security to applications accessing web services via HttpClient, System.Net now more than ever is critical path for many applications. It received a lot of attention in .NET Core 2.1, and continues to in .NET Core 3.0.

Let’s start with HttpClient. One improvement made in PR dotnet/corefx#32820 was around how buffering is handled, and in particular better respecting larger buffer size requests made as part of copying the response data when a content length was provided by the server. On a fast connection and with a large response body (such as the 10MB in this example), this can make a sizeable difference in throughput due to reduced syscalls to transfer data.

private HttpClient _client = new HttpClient();
private Socket _listener;
private Uri _uri;

[GlobalSetup]
public void Setup()
{
    _listener = new Socket(AddressFamily.InterNetwork, SocketType.Stream, ProtocolType.Tcp);
    _listener.Bind(new IPEndPoint(IPAddress.Loopback, 0));
    _listener.Listen(int.MaxValue);
    var ep = (IPEndPoint)_listener.LocalEndPoint;
    _uri = new Uri($"http://{ep.Address}:{ep.Port}");

    Task.Run(async () =>
    {
        while (true)
        {
            Socket s = await _listener.AcceptAsync();
            var ignored = Task.Run(async () =>
            {
                ReadOnlyMemory<byte> headers = Encoding.ASCII.GetBytes("HTTP/1.1 200 OKrnContent-Length: 10485760rnrn");
                ReadOnlyMemory<byte> data = new byte[10*1024*1024]; // 10485760

                using (var serverStream = new NetworkStream(s, true))
                using (var reader = new StreamReader(serverStream))
                {
                    while (true)
                    {
                        while (!string.IsNullOrEmpty(await reader.ReadLineAsync())) ;
                        await s.SendAsync(headers, SocketFlags.None);
                        await s.SendAsync(data, SocketFlags.None);
                    }
                }
            });
        }
    });
}

[Benchmark]
public async Task HttpDownload()
{
    using (HttpResponseMessage r = await _client.GetAsync(_uri, HttpCompletionOption.ResponseHeadersRead))
    using (Stream s = await r.Content.ReadAsStreamAsync())
    {
        await s.CopyToAsync(Stream.Null);
    }
}

Now consider SslStream. Previous releases saw work done to make reads and writes on SslStream much more efficient, but additional work was done in .NET Core 3.0 as part of PRs dotnet/corefx#35091 and dotnet/corefx#35209 (and dotnet/corefx#35367 on Unix) to make initiating the connection more efficient, in particular in terms of allocations.

private NetworkStream _client;
private NetworkStream _server;

private static X509Certificate2 s_cert = GetServerCertificate();

private static X509Certificate2 GetServerCertificate()
{
    var certCollection = new X509Certificate2Collection();
    byte[] testCertBytes = Convert.FromBase64String(@"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");
    certCollection.Import(testCertBytes, "testcertificate", X509KeyStorageFlags.DefaultKeySet);
    return certCollection.Cast<X509Certificate2>().First(c => c.HasPrivateKey);
}

[GlobalSetup]
public void Setup()
{
    using (var listener = new Socket(AddressFamily.InterNetwork, SocketType.Stream, ProtocolType.Tcp))
    {
        listener.Bind(new IPEndPoint(IPAddress.Loopback, 0));
        listener.Listen(1);

        var client = new Socket(AddressFamily.InterNetwork, SocketType.Stream, ProtocolType.Tcp);
        client.Connect(listener.LocalEndPoint);
        Socket server = listener.Accept();

        _client = new NetworkStream(client);
        _server = new NetworkStream(server);
    }
}

[Benchmark]
public void SslConnect()
{
    using (var sslClient = new SslStream(_client, true, delegate { return true; }))
    using (var sslServer = new SslStream(_server, true, delegate { return true; }))
    {
        Task t = sslServer.AuthenticateAsServerAsync(s_cert, false, SslProtocols.None, false);
        sslClient.AuthenticateAsClient("localhost", null, SslProtocols.None, false);
        t.Wait();
    }
}

In System.Net.Sockets there’s another example of taking advantage of the IThreadPoolWorkItem interface discussed earlier. On Windows for asynchronous operations, we utilize “overlapped I/O”, utilizing threads from the I/O thread pool to execute continuations from socket operations; Windows queues I/O completion packets that these I/O pool threads then process, including invoking the continuations. On Unix, however, the mechanism is very different. There’s no concept of “overlapped I/O” on Unix, and instead asynchrony in System.Net.Sockets is achieved by using epoll (or kqueues on macOS), with all of the sockets in the system registered with an epoll file descriptor, and then one thread monitoring that epoll for changes. Any time an asynchronous operation completes for a socket, the epoll is signaled and the thread blocking on it wakes up to process it. If that thread were to run the socket continuation action then and there, it would end up potentially running unbounded work that could stall every other socket’s handling indefinitely, and in the extreme case, deadlock. Instead, this thread queues a work item back to the thread pool and then immediately goes back to processing any other socket work. Prior to .NET Core 3.0, that queueing involved an allocation, which meant that every asynchronously completing socket operation on Unix involved at least one allocation. As of PR dotnet/corefx#32919, that number drops to zero, as a cached object already being used (and reused) to represent asynchronous operations was changed to also implement IThreadPoolWorkItem and be queueable directly to the thread pool.

Other areas of System.Net have benefited from the efforts already alluded to previously, as well. For example, Dns.GetHostName used to use StringBuilder in its marshaling, but as of PR dotnet/corefx#29594 it no longer does.

[Benchmark]
public string GetHostName() => Dns.GetHostName();

And IPAddress.HostToNetworkOrder/NetworkToHostOrder have benefiting indirectly from the intrinsics push that was mentioned previously. In .NET Core 2.1, BinaryPrimitives.ReverseEndianness was added with an optimized software implementation, and these IPAddress methods were rewritten as simple wrappers for ReverseEndianness. Now in .NET Core 3.0, PR dotnet/coreclr#18398 turned ReverseEndianness into a JIT intrinsic for which the JIT can emit a very efficient BSWAP instruction, with the resulting throughput improvements accruing to IPAddress as well.

private long _value = 1234567890123456789;

[Benchmark]
public long HostToNetworkOrder() => IPAddress.HostToNetworkOrder(_value);

System.IO

Often going hand in hand with networking is compression, which has also seen some improvements in .NET Core 3.0. Most notably is that a key dependency was updated. On Unix, System.IO.Compression just uses the zlib library available on the machine, as it’s a standard part of most any distro/version. On Windows, however, zlib is generally nowhere to be found, and so it’s built and shipped as part of .NET Core on Windows. Rather than shipping the standard zlib, .NET Core includes a version modified by Intel with additional performance improvements not yet merged upstream. In .NET Core 3.0, we’ve sync’d to the latest available version of ZLib-Intel, version 1.2.11. This brings some very measurable performance improvements, in particular around decompression.

There have also been compression-related improvements that take advantage of previous improvements elsewhere in .NET Core. For example, the synchronous Stream.CopyTo was originally non-virtual, but as gains were found by overriding the asynchronous CopyToAsync and specializing its implementation for particular concrete stream types, CopyTo was made virtual to enjoy similar improvements. PR dotnet/corefx#29751 capitalized on this to override CopyTo on DeflateStream, employing similar optimizations in the synchronous implementation as were employed in the asynchronous implementation, essentially entailing minimizing the interop costs with zlib.

private byte[] _compressed;

[GlobalSetup]
public void Setup()
{
    var ms = new MemoryStream();
    using (var ds = new DeflateStream(ms, CompressionLevel.Fastest))
    {
        ds.Write(Enumerable.Range(0, 1_000_000).Select(i => (byte)i).ToArray(), 0, 1_000_000);
    }
    _compressed = ms.ToArray();
}

[Benchmark]
public void DeflateDecompress()
{
    using (var ds = new DeflateStream(new MemoryStream(_compressed), CompressionMode.Decompress))
    {
        ds.CopyTo(Stream.Null);
    }
}

Improvements were also made to BrotliStream (which as of .NET Core 3.0 is also used by HttpClient to automatically decompress Brotli-encoded content). Previously every new BrotliStream would also allocate a large buffer, but as of PR dotnet/corefx#35492, that buffer is pooled, as it is with DeflateStream (additionally, BrotliStream now as of PR dotnet/corefx#30135 overrides ReadByte and WriteByte to avoid allocations in the base implementation).

[Benchmark]
public void BrotliWrite()
{
    using (var bs = new BrotliStream(Stream.Null, CompressionLevel.Fastest))
    {
        for (int i = 0; i < 1_000; i++)
        {
            bs.WriteByte((byte)i);
        }
    }
}

Moving on from compression, it’s worth highlighting that formatting applies in more situations than just formatting individual primitives. TextWriter, for example, has multiple methods for writing with format strings, e.g. public override void Write(string format, object arg0, arg1). PR dotnet/coreclr#19235 improved on that for StreamWriter by providing specialized overrides that take a more efficient path that reduces allocation:

private StreamWriter _writer = new StreamWriter(Stream.Null);

[Benchmark]
public void StreamWriterFormat() => _writer.Write("Writing out a value: {0}", 42);

As another example, PR dotnet/coreclr#22102 from @TomerWeisberg improved the parsing performance of various primitive types on BinaryReader by special-casing the common situation where the BinaryReader wraps a MemoryStream.

Or consider PR dotnet/corefx#30667 from @MarcoRossignoli, who added overrides to StringWriter for the Write{Line}{Async} methods that take a StringBuilder argument. StringWriter is just a wrapper around a StringBuilder, and StringBuilder knows how to append another StringBuilder to it, so these overrides on StringWriter can feed them right through.

private StringBuilder _underlying;
private StringWriter _writer;
private StringBuilder _sb;

[GlobalSetup]
public void Setup()
{
    _underlying = new StringBuilder();
    _writer = new StringWriter(_underlying);
    _sb = new StringBuilder("This is a test. This is only a test.");
}

[Benchmark]
public void Write()
{
    _underlying.Clear();
    _writer.Write(_sb);
}

System.IO.Pipelines is another IO-related library that’s received a lot of attention in .NET Core 3.0. Pipelines was introduced in .NET Core 2.1, and provides buffer-management as part of an I/O pipeline, used heavily by ASP.NET Core. A variety of PRs have gone into improving its performance. For example, PR dotnet/corefx#35171special-cases the common and default case where the Pool specified to be used by a Pipe is the default MemoryPool<byte>.Shared. Rather than go through MemoryPool<byte>.Shared in this case, the Pipe now bypasses it and goes to the underlying ArrayPool<byte>.Shared directly, which removes a layer of indirection but also the allocation of IMemoryOwner<byte> objects returned from MemoryPool<byte>.Rent. (Note that for this benchmark, since System.IO.Pipelines is part of a NuGet package rather than in the shared framework, I’ve added a Benchmark.NET config that specifies what package version to use with each run in order to show the improvements.)

// Run with: dotnet run -c Release -f netcoreapp2.1 --filter *Program*

private sealed class Config : ManualConfig // also add [Config(typeof(Config))] to the Program class
{
    public Config()
    {
        Add(Job.MediumRun.With(CsProjCoreToolchain.NetCoreApp21).WithNuGet("System.IO.Pipelines", "4.5.0").WithId("4.5.0"));
        Add(Job.MediumRun.With(CsProjCoreToolchain.NetCoreApp30).WithNuGet("System.IO.Pipelines", "4.6.0-preview5.19224.8").WithId("4.6.0-preview5.19224.8"));
    }
}

private readonly Pipe _pipe = new Pipe();
private byte[] _buffer = new byte[1024];

[Benchmark]
public async Task ReadWrite()
{
    var reader = _pipe.Reader;
    var writer = _pipe.Writer;

    for (int i = 0; i < 1000; i++)
    {
        ValueTask<ReadResult> vt = reader.ReadAsync();
        await writer.WriteAsync(_buffer);
        ReadResult rr = await vt;
        reader.AdvanceTo(rr.Buffer.End);
    }
}

PR dotnet/corefx#33658 from @benaadams allows Pipe to use the UnsafeQueueUserWorkItemboxing-related optimizations described earlier, PR dotnet/corefx#33755 avoids queueing unnecessary work items, PR dotnet/corefx#35939 tweaks the defaults used to better handle buffering in common cases, PR dotnet/corefx#35216 reduces the amount of slicing performed in various pipe operations, PR dotnet/corefx#35234 from @benaadams reduces the locking used in core operations, PR dotnet/corefx#35509 reduces argument validation (decreasing branching costs), PR dotnet/corefx#33000 focused on reducing costs associated with ReadOnlySequence<byte> that’s the main exchange type pipelines passes around, and PR dotnet/corefx#29837 further optimizes operations like GetSpan and Advance on the Pipe. The net result is to whittle away at already low CPU and allocation overheads.

// Run with: dotnet run -c Release -f netcoreapp2.1 --filter *Program*

private sealed class Config : ManualConfig // also add [Config(typeof(Config))] to the Program class
{
    public Config()
    {
        Add(Job.MediumRun.With(CsProjCoreToolchain.NetCoreApp21).WithNuGet("System.IO.Pipelines", "4.5.0").WithId("4.5.0"));
        Add(Job.MediumRun.With(CsProjCoreToolchain.NetCoreApp30).WithNuGet("System.IO.Pipelines", "4.6.0-preview5.19224.8").WithId("4.6.0-preview5.19224.8"));
    }
}

private readonly Pipe _pipe1 = new Pipe();
private readonly Pipe _pipe2 = new Pipe();
private byte[] _buffer = new byte[1024];

[GlobalSetup]
public void Setup()
{
    Task.Run(async () =>
    {
        var reader = _pipe2.Reader;
        var writer = _pipe1.Writer;
        while (true)
        {
            ReadResult rr = await reader.ReadAsync();
            foreach (ReadOnlyMemory<byte> mem in rr.Buffer)
            {
                await writer.WriteAsync(mem);
            }
            reader.AdvanceTo(rr.Buffer.End);
        }
    });
}

[Benchmark]
public async Task ReadWrite()
{
    var reader = _pipe1.Reader;
    var writer = _pipe2.Writer;

    for (int i = 0; i < 1000; i++)
    {
        await writer.WriteAsync(_buffer);
        long count = 0;
        while (count < _buffer.Length)
        {
            ReadResult rr = await reader.ReadAsync();
            count += rr.Buffer.Length;
            reader.AdvanceTo(rr.Buffer.End);
        }
    }
}

System.Console

Console isn’t something one normally thinks of as being performance-sensitive. However, there are two changes in this release that I think are worth calling attention to here.

First, there is one area of Console about which we’ve heard numerous concerns related to performance, where the performance impact visibly impacts users. In particular, interactive console applications generally do a lot of manipulation of the cursor, which also entails asking where the cursor currently is. On Windows, both the setting and getting of the cursor are relatively fast operations, with P/Invoke calls made to functions exported from kernel32.dll. On Unix, things are more complicated. There’s no standard POSIX function for getting or setting a terminal’s cursor position. Instead, there’s a standard convention for interacting with the terminal via ANSI escape sequences. To set the cursor position, one writes a sequence of characters to stdout (e.g. “ESC [ 12 ; 34 H” to indicate 12th row, 34th column) and the terminal interprets that and reacts accordingly. Getting the cursor position is more of an ordeal. To get the current cursor position, an application writes to stdout a request (e.g. “ESC [ 6 n”), and in response the terminal writes back to the application’s stdin a response something like “ESC [ 12 ; 34 R”, to indicate the cursor is at the 12th row and 34th column. That response then needs to be read from stdin and parsed. So, in contrast to a fast interop call on Windows, on Unix we need to write, read, and parse text, and do so in a way that doesn’t cause problems with a user sitting at a keyboard using the app concurrently… not particularly cheap. When just getting the cursor position now and then, it’s not a big deal. But when getting it frequently, and when porting code originally written for Windows where the operation was so cheap the code being ported may not have been very frugal with how often it asked for the position (asking for it more than is really needed), this has resulted in visible performance problems. Thankfully, the issue has been addressed in .NET Core 3.0, by PR dotnet/corefx#36049 from @tmds. The change caches the current position and then manually handles updating that cached value based on user interactions, such as handling typing or resizing the terminal window. (Note that Benchmark.NET operates in a way that redirects standard input and output for the process running the test, and that makes Console.CursorLeft/Top return 0 immediately, so for this test, I’ve just done a simple console app with a Stopwatch, which is, as you’ll see, more than sufficient given the discrepancy between costs in versions.)

using System;
using System.Diagnostics;

public class Program
{
    static void Main()
    {
        var sw = new Stopwatch();
        for (int iter = 0; iter < 5; iter++)
        {
            sw.Restart();
            for (int i = 0; i < 1_000; i++) { _ = Console.CursorLeft; }
            sw.Stop();
            Console.WriteLine(sw.Elapsed.TotalSeconds);
        }
    }
}

~/BlogPostBenchmarks$ dotnet run -c Release -f netcoreapp2.1
18.2152636
17.9935087
18.2676408
17.7891821
17.4141348
~/BlogPostBenchmarks$ dotnet run -c Release -f netcoreapp3.0
0.0648111
0.0001539
0.00013979999999999998
0.00013529999999999998
0.0001459

Another place where Console has been improved affects both Windows and Unix. Interestingly, this change was made for functional reasons (in particular for when running on Windows), but it has performance benefits as well for all OSes. In .NET, most of the times we specify buffer sizes it’s for performance reasons and represents a trade-off: the smaller the buffer size, the less memory is used but the more times operations may need to be performed to fill that buffer, and conversely the larger the buffer size, the more memory is used but the fewer times the buffer will need to be filled. It’s rare that the buffer size has a functional impact, but it actually can in Console. On Windows to read from the console, one calls either the ReadFile or ReadConsole functions, both of which accept a buffer to store the read data into. By default on Windows, reading from the console will not return until a newline, but Windows also needs somewhere to store the typed data, and it does so into the supplied buffer. Thus, Windows won’t let the user type more characters than can fit into the buffer, which means the line length a user can type is limited by the buffer size. For whatever historical reason, .NET has used a buffer size of 256 characters, limiting the typeable line length to that amount. PR dotnet/corefx#36212 expands that to 4096 characters, which much better matches other programming environments and allows for a much more reasonable line length. However, as is the case when increasing buffer sizes, relevant throughput involving that buffer improves as well, in particular when reading from files piped to stdin. For example, reading 8K of input data from stdin previously would have required 32 calls to ReadFile; with a 4K buffer, only 2 calls are required. The impact of that can be seen in this benchmark. (Again, this is harder to test with Benchmark.NET, so I’ve again just used a simple console app.)

using System;
using System.Diagnostics;
using System.IO;

public class Program
{
    static void Main()
    {
        //using (var writer = new StreamWriter(@"tmp.dat"))
        //{
        //    for (int i = 0; i < 10_000_000; i++)
        //    {
        //        writer.WriteLine("This is a test.  This is only a test.");
        //    }
        //}

        var sw = Stopwatch.StartNew();
        while (Console.ReadLine() != null) ;
        Console.WriteLine(sw.Elapsed.TotalSeconds);
    }
}

c:BlogPostBenchmarks>dotnet run -c Release -f netcoreapp2.1 < c:BlogPostBenchmarksbinReleasenetcoreapp2.1tmp.dat
4.8151814

c:BlogPostBenchmarks>dotnet run -c Release -f netcoreapp3.0 < c:BlogPostBenchmarksbinReleasenetcoreapp2.1tmp.dat
1.3161175999999999

System.Diagnostics.Process

There have been various functional improvements to the Process class in .NET Core 3.0, in particular on Unix, but there are a couple of performance-focused improvements I want to call out.

PR dotnet/corefx#31236 is another nice example of introducing a new performance-focused API and, at the same time, using it within .NET Core to further improve the performance of core libraries. In this case, it’s a low-level API on MemoryMarshal that enables efficiently reading structs from spans, something that’s done in spades as part of the interop in System.Diagnostics.Process. I like that example, not because it makes for a massive performance improvement, but because it highlights the general pattern I like to see: adding new APIs for others to consume and in the same breath using those APIs to better the technology itself.

A more impactful example, though, comes from @joshudson in PR dotnet/corefx#33289, which changed the native code used to fork a new process from using the fork function to instead using the vfork function. The benefit of vfork is that it avoids copying the page tables of the parent process into the child process, with the assumption that the child process is then just going to overwrite everything anyway via an almost immediate exec call. fork does copy-on-write, but if the process is modifying a lot of state concurrently (e.g. with the garbage collector running), this can get expensive quickly and unnecessarily. For this benchmark, I’ve just written a nop C program in a test.c file:

int main() { return 0; }

and compiled it with GCC:

gcc -o test test.c

to give us a target for Process.Start to invoke.

[Benchmark]
public void ProcessStartWait() => Process.Start("/home/stephentoub/BlogPostBenchmarks/test").WaitForExit();

LINQ

Previous releases have seen a ton of investment in optimizing LINQ. There’s less of that in .NET Core 3.0, as a lot of the common patterns have already been covered well. However, there are still some nice improvements to be found in the release.

It’s relatively rare that new operators are added to System.Linq itself, as the very nature of extension methods makes it easy for anyone to build up and share their own library of extension methods they consider to be useful (and several well-established such libraries exist). Even so, .NET Core 2.0 saw a new TakeLast method added. In .NET Core 3.0, PR dotnet/corefx#36051 by @Romasz updated TakeLast to integrate with the internal IPartition<T> interface that enables several operators to cooperate, helping to optimize (in some situations quite heavily) various uses of the operator.

private IEnumerable<int> _enumerable = new int[1000].Select(i => i);

[Benchmark]
public int SumLast10() => _enumerable.TakeLast(10).Sum();

Just recently, PR dotnet/corefx#37410 optimized the relatively common pattern of using Enumerable.Range(...).Select(…), teaching Select about the object generated by Range and allowing for the enumeration performed by Select to skip going through IEnumerable<T> and instead just loop through the intended numerical range directly.

[Benchmark]
public int[] RangeSelectToArray() => Enumerable.Range(0, 100).Select(i => i * 2).ToArray();

Enumerable.Empty<T>() was also changed in PR dotnet/corefx#31025 to better compose with optimizations already elsewhere in .NET Core’s System.Linq implementation. While no one should be writing code that explicitly calls additional LINQ operators directly on the result of Enumerable.Empty<T>(), it is common to return the result of Empty<T>() as one possible return value from an IEnumerable<T>-returning method, and then for the caller to tack on additional operators, such that this optimization does actually have a meaningful effect.

[Benchmark]
public int[] EmptyTakeSelectToArray() => Enumerable.Empty<int>().Take(10).Select(i => i).ToArray();

Across .NET Core, we’re also paying more attention to assembly size, in particular as it can impact ahead-of-time (AOT) compilation. PRs like dotnet/corefx#35213, which employs “ThrowHelpers” in the heavily-generic LINQ, help to reduce generated code size, which has benefits in and of itself but can also help with other areas of performance.

Interop

Interop is another one of those areas that’s critically important both to customers of .NET as well as to .NET itself, as a lot of functionality in .NET is layered on top of underlying operating system functionality that requires interop to access. As such, performance improvements in interop itself end up impacting a wide array of components.

One notable improvement is in SafeHandle, and it’s another example of where moving code from native to managed helped improve performance. SafeHandle is the recommended way for managing the lifetime of native resources, whether represented by handles on Windows or by file descriptors on Unix, and it’s used in exactly that way internally in all of our managed libraries in coreclr and corefx. One of the reasons it’s the recommended solution is that it uses appropriate synchronization to ensure that these native resources aren’t closed from managed code while they’re still being used, and that means that the interop layer needs to track every time a P/Invoke call is made with a SafeHandle, invoking DangerousAddRef prior to the call, DangerousRelease after the call, and DangerousGetHandle to extract the actual pointer value to pass to the native function. In previous releases of .NET, the core pieces of those implementations were in the runtime, which meant managed code needed to make InternalCalls to native code in the runtime for each of those operations. In .NET Core 3.0 as of PR dotnet/coreclr#22564, those operations have been ported to managed code, removing the overhead associated with each of those transitions.

private SafeFileHandle _sfh = new SafeFileHandle((IntPtr)12345, ownsHandle: false);

[Benchmark]
public IntPtr SafeHandleOps()
{
    bool success = false;
    try
    {
        _sfh.DangerousAddRef(ref success);
        return _sfh.DangerousGetHandle();
    }
    finally
    {
        if (success)
        {
            _sfh.DangerousRelease();
        }
    }
}

There are also examples for improvements to marshaling. Earlier in this post, I highlighted a variety of cases where StringBuilder was used as part of marshaling and interop. For the record, I personally dislike StringBuilder being used in interop, as it adds cost and complexity for relatively little benefit, and as a result did work in PRs like dotnet/corefx#33780 and dotnet/coreclr#21120 to remove almost all use of StringBuilder marshaling in coreclr and corefx. However, there is still a lot of code built around StringBuilder, and it deserves to be as fast as possible. PR dotnet/coreclr#17928 avoids a bunch of unnecessary work and allocation that happens as part of StringBuilder marshaling, and leads to improvements like this:

private const int MAX_PATH = 260;
private StringBuilder _sb = new StringBuilder(MAX_PATH);

[DllImport("kernel32", CharSet = CharSet.Unicode, SetLastError = true)]
private static extern uint GetTempPathW(int bufferLen, [Out]StringBuilder buffer);

[Benchmark]
public void StringBuilderMarshal() => GetTempPathW(MAX_PATH, _sb);

And of course, specific uses of interop and marshaling have also improved. For example, FileSystemWatcher‘s interop on macOS had been using MarshalAs attributes, which forced the runtime to do additional marshaling work on every OS callback, including allocating arrays. PR dotnet/corefx#34715 moved FileSystemWatcher‘s interop to use a more efficient scheme that doesn’t entail additional allocations nor marshaling directives. Or consider dotnet/corefx#30099, where System.Drawing was switched to using a much more efficient scheme of marshaling and interop, with a managed array being pinned and passed directly to native code instead of allocating additional memory and copying to it.

// Run with: dotnet run -c Release -f netcoreapp2.1 --filter *Program*

private sealed class Config : ManualConfig // also add [Config(typeof(Config))] to the Program class
{
    public Config()
    {
        Add(Job.MediumRun.With(CsProjCoreToolchain.NetCoreApp21).WithNuGet("System.Drawing.Common", "4.5.1").WithId("4.5.1"));
        Add(Job.MediumRun.With(CsProjCoreToolchain.NetCoreApp30).WithNuGet("System.Drawing.Common", "4.6.0-preview5.19224.8").WithId("4.6.0-preview5.19224.8"));
    }
}

private Bitmap _image;
private Graphics _graphics;
private Point[] _points;

[GlobalSetup]
public void Setup()
{
    _image = new Bitmap(100, 100);
    _graphics = Graphics.FromImage(_image);
    _points = new[]
    {
        new Point(10, 10), new Point(20, 1), new Point(35, 5), new Point(50, 10),
        new Point(60, 15), new Point(65, 25), new Point(50, 30)
    };
}

[Benchmark]
public void TransformPoints()
{
    _graphics.TransformPoints(CoordinateSpace.World, CoordinateSpace.Page, _points);
    _graphics.TransformPoints(CoordinateSpace.Device, CoordinateSpace.World, _points);
    _graphics.TransformPoints(CoordinateSpace.Page, CoordinateSpace.Device, _points);
}

Peanut butter

In previous sections of this post, I highlighted groups of PRs that addressed various areas of .NET in an impactful way, where some piece of mainstream functionality was significantly improved. But those aren’t the only areas or kinds of PRs that matter.

In .NET we also have what we sometimes refer to as “peanut butter”. We have a ton of code that’s generally great for most applications but that has a myriad of small opportunities for improvements. Those improvements alone don’t make anything better, but they fix a smearing of performance penalties across a large swath of code, and the more of such issues we can fix, the better performance becomes overall. An allocation removed here, some unnecessary cycles eliminated there, some unnecessary code removed there. Here are just a sampling of PRs that went in to address such “peanut butter”:

GC

No blog post on performance would be complete without discussing the garbage collector. Many of the improvements cited thus far have involved reducing allocations, which is in part about reducing direct costs but more so about reducing the load placed on the garbage collector and minimizing the work it needs to do. But improving the GC itself is also a key focus, and one that’s gotten attention in this release, as it has in previous releases.

PR dotnet/coreclr#21523 includes a variety of performance improvements, from improvements to locking to better free list management. PR dotnet/coreclr#23251 from @mjsabby adds support to the GC for Large Pages (“Huge Pages” on Linux), which can be opted-into by very large applications that experience bottlenecks due to the translation lookaside buffer (TLB). And PR dotnet/coreclr#22003 further optimized the write barriers employed by the GC.

One notable piece of work is improving behavior on machines with a large number of processors, e.g. PR dotnet/coreclr#23824. Rather than trying to explain it here, I’ll simply refer to @Maoni0’s blog post on the subject: https://blogs.msdn.microsoft.com/maoni/2019/04/03/making-cpu-configuration-better-for-gc-on-machines-with-64-cpus/.

Similarly, a lot of work has gone into the release to improve the behavior and performance of the GC when operating in a containerized environment (and in particular in one that’s heavily constrained), such as in PR dotnet/coreclr#22180. Again, @Maoni0 can do a much better job than I can describing this work, and you can read all about it her two blog posts,  and .

JIT

A lot of goodness has gone into the just-in-time (JIT) compiler in .NET Core 3.0.

One of the most impactful changes is tiered compilation (this is split across many PRs, but for example PR dotnet/coreclr#23599). Tiered compilation is a solution for the problem that very good compilation from MSIL to native code takes time; the more analysis to be done, the more optimizations to be applied, the longer it takes. But with a JIT compiler that does that code generation at runtime, that time comes at the direct expense of application start-up, and so you’re left with a trade-off: do you spend more time generating better code but take longer to get going, or do you spend less time generating less-good code but get going faster? Tiered compilation is a scheme for accomplishing both. The idea is that methods are first compiled with a fast pass that applies few-to-no optimizations but that completes very quickly, and then as methods are seen to execute again and again, those methods are re-JIT’d, this time with more time spent on code quality.

Interestingly, though, tiered compilation isn’t just about start-up time. There are optimizations that the re-compilation can take advantage of that weren’t available the first time around. For example, tiered compilation can apply to ready-to-run (R2R) images, a form of precompilation employed by assemblies in the .NET Core shared framework. These assemblies contain precompiled native code, but in some ways the optimizations that can be applied during that native code generation are limited in order to aid in version resiliency, e.g. cross-module inlining doesn’t happen with R2R. So, the R2R code can help enable faster start-up, but then methods found to be used frequently can be re-compiled via tiered compilation, thereby taking advantage of such optimizations the original precompiled code was restricted from using.

Here’s an example of that. First, we can run the following benchmark.

private XmlDocument _doc = new XmlDocument();

[Benchmark]
public void LoadXml()
{
    _doc.RemoveAll();
    _doc.LoadXml("<Root><Element attrib="foo" attrib2="foo2">foo</Element><Element attrib="foo" attrib2="foo2">foo</Element><Element attrib="foo" attrib2="foo2">foo</Element><Element attrib="foo" attrib2="foo2">foo</Element><Element attrib="foo" attrib2="foo2">foo</Element><Element attrib="foo" attrib2="foo2">foo</Element><Element attrib="foo" attrib2="foo2">foo</Element><Element attrib="foo" attrib2="foo2">foo</Element></Root>");
}

Then, we can run it again, but this time with tiered compilation disabled by setting the COMPlus_TieredCompilationenvironment variable to 0.

There are a variety of environment variables that configure tiered compilation and in what situations it’s enabled. For more details, see https://github.com/dotnet/coreclr/issues/24064.

Another really cool improvement in the JIT comes in PR dotnet/coreclr#20886. In previous releases of .NET, the JIT could optimize the usage of some primitive type static readonly fields as if they were constants. For example, if a static readonly int field were initialized to the value 42 by the time some code that used that field was JIT compiled, the JIT compiler would effectively treat that field instead as a const, and do constant folding and all other forms of optimizations that would otherwise apply. In .NET Core 3.0, the JIT can now utilize the type of static readonlyfields to do additional optimizations. For example, if a static readonly field is typed as a base type but is then set to a derived type, the JIT might be able to see the actual type of the object stored in the field, and then when a virtual method is called on it, devirtualize the call and even potentially inline it.

private static readonly Base s_base;

static Program() => s_base = new Derived();

[Benchmark]
public void AccessStatic() => s_base.Method();

private sealed class Derived : Base { public override void Method() { } }
private abstract class Base { public abstract void Method(); }

That highlights some improvements that have gone into devirtualization, but there are others, such as in PRs dotnet/coreclr#20447, dotnet/coreclr#20292, and dotnet/coreclr#20640 which, when combined with PRs like dotnet/coreclr#20637 from @benaadams, help with APIs like ArrayPool<T>.Shared.

[Benchmark]
public void RentReturn() => ArrayPool<byte>.Shared.Return(ArrayPool<byte>.Shared.Rent(256));

Another nice improvement is around zeroing of locals. Even when the initlocals flag isn’t set (as of PR dotnet/corefx#34406, it’s cleared for all assemblies in coreclr and corefx), the JIT still needs to zero out references in locals so that the GC doesn’t see and misinterpret garbage, and that zero’ing can take a measurable amount of time, in particular in methods that do a lot of work with spans. PRs dotnet/coreclr#23498 and dotnet/coreclr#13868 make some nice improvements in this area.

private byte[] _bytes = new byte[1];

[Benchmark]
public void StackZero()
{
    Span<byte> a, b;
    a = _bytes;
    b = _bytes;
    Nop(a, b);
}

[MethodImpl(MethodImplOptions.NoInlining)]
private void Nop(Span<byte> a, Span<byte> b) { }

Another example relates to structs. As more and more recognition has come to .NET performance, in particular around allocation, there’s been a significant increase in the use of value types, often wrapping one another. For example, awaiting a ValueTask<T> results in calling GetAwaiter() on that value task, and that returns a ValueTaskAwaiter<T> that wraps the ValueTask<T>. PR dotnet/coreclr#19429 improves the situation by removing unnecessary copies involved in these operations.

[Benchmark]
public int WrapUnwrap() => ValueTuple.Create(ValueTuple.Create(ValueTuple.Create(42))).Item1.Item1.Item1;

What’s Next?

As I write this post, I count 29 pending performance-focused PRs in the coreclr repo and another 8  in the corefx repo. Some of those are likely to be merged in time for the .NET Core 3.0 release, as will, I’m sure, additional PRs that haven’t even been opened yet. In short, even after all of the improvements detailed in for .NET Core 2.0, .NET Core 2.1, and now in this post for .NET Core 3.0, and even with all of those improvements contributing to ASP.NET Core being one of the fastest web servers on the planet, there is still incredible opportunity for performance to keep getting better and better, and for you to help achieve that. Hopefully this post has made you excited about the potential .NET Core 3.0 holds. I look forward to reviewing your PRs as we all contribute to this exciting future together!

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