SIMD Matters: Graph Coloring
The article discusses SIMD's role in enhancing CPU performance in game development, particularly in Box2D, highlighting challenges, the use of graph coloring, and benchmarks showing significant performance improvements over scalar implementations.
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The article discusses the implementation of SIMD (Single Instruction, Multiple Data) in game development, particularly in the Box2D physics engine. While SIMD is often seen as a key to enhancing CPU performance, its practical benefits can be elusive, especially in scenarios where operations are not easily parallelizable. The author highlights challenges in game physics, such as the need for piecemeal vector math and the random access of bodies, which complicate the use of SIMD. To address these issues, the author introduces graph coloring as a method to group contact constraints that can be solved simultaneously without race conditions. This technique allows for efficient processing of multiple contact pairs, significantly improving performance. Benchmarks conducted on different hardware show that SIMD implementations (AVX2, SSE2, and Neon) outperform scalar implementations, with AVX2 yielding the best results. The author concludes that while implementing SIMD requires considerable effort, the performance gains justify the investment, enabling games to run faster and manage more rigid bodies. Additionally, the article touches on the limited effectiveness of compiler vectorization compared to hand-optimized SIMD code.
- SIMD can significantly enhance CPU performance in game physics.
- Graph coloring allows for efficient grouping of contact constraints for simultaneous processing.
- Benchmarks show SIMD implementations outperform scalar implementations by a substantial margin.
- Implementing SIMD requires considerable effort but yields worthwhile performance improvements.
- Compiler vectorization may not match the efficiency of hand-optimized SIMD code.
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SIMD Matters
The article discusses SIMD's role in enhancing CPU performance in game development, particularly through graph coloring in Box2D, which improves processing efficiency and significantly outperforms scalar methods.
13 comments<TANGENT> This hits me, like a ton of bricks, as one of the most elegant ways to describe why I add 2 phases of clocking ("like colors on a chessboard" is the phrase I've been using) to my BitGrid[1] hobby project.
I wonder what other classes of problems this could solve. This feels oddly parallel, like a mapping of the Langlands program into computer science.[2]
This has been my experience often times I misunderstood how much can be gained by using SIMD, and preparing the data to be "eaten" by SIMD instructions is not trivial. I have many times attempted to use just to profile and see it didn't improve things at all and made the code really hard to understand.
Kudos for Erin here this is really hard work and it's great it paid off well and gave good results here!
I will keep this example in mind the next time somebody trots out the line that you should just trust the compiler.
I saw a small typo:
// wide float
typedef b2FloatW __m128;
I don't understand this statement at the end of the article? Can anyone explain? TIA.
Intuitively, this feels like a narrower version of using Z-order curve.
Caveats: my knowledge is mostly theoretical (eg proving np-hardness or algorithm existence results) but I'm very good at thinking algorithmically. I have only hobbyist programming skills but I am a fast learner. Thanks!
I was about this surprised when I made a jupyter notebook with a few gigs of numbers shuffled around and xgboosted and after I was done prototyping on an M1 Air and ran it on my serious box (a 12700k) it was actually slower, and noticeably.
And for 80% of the cases by the point there is enough vectorizable data for a programmer to look into simd, a gpu can provide 1000%+ of perf AND a certain level of portability.
So right now simd is a niche tool for super low-level things: certain decompression algos, bits of math here and there, solvers, etc.
And it also takes a lot of space on your cpu die. Like, A LOT.
Related
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SIMD is vital for performance in Rust. Options include auto-vectorization, platform-specific intrinsics, and std::simd module. Balancing performance, portability, and ease of use is key. Leveraging auto-vectorization and intrinsics optimizes Rust projects for high-performance computing, multimedia, systems programming, and cryptography.
Scan HTML faster with SIMD instructions: .NET/C# Edition
WebKit and Chromium enhance HTML content scanning with fast SIMD routines, boosting performance significantly. .NET8 now supports speedy SIMD instructions for C#, achieving impressive speeds comparable to C/C++ implementations.
An SVE backend for astcenc (Adaptive Scalable Texture Compression Encoder)
The implementation of a 256-bit SVE backend for astcenc shows performance improvements of 14% to 63%, utilizing predicated operations and scatter/gather instructions, with future work planned for SVE2.
CPU Dispatching: Make your code both portable and fast (2020)
CPU dispatching improves software performance and portability by allowing binaries to select code versions based on CPU features at runtime, with manual and compiler-assisted approaches enhancing efficiency, especially using SIMD instructions.
SIMD Matters
The article discusses SIMD's role in enhancing CPU performance in game development, particularly through graph coloring in Box2D, which improves processing efficiency and significantly outperforms scalar methods.