Using SIMD for Parallel Processing in Rust
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.
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SIMD (Single Instruction, Multiple Data) is a crucial tool for enhancing performance in data-intensive operations. In Rust, various avenues exist for SIMD development, including auto-vectorization by the Rust compiler, platform-specific intrinsics through std::arch, and the experimental SIMD module in std::simd. These approaches offer trade-offs in performance, portability, and ease of use. Practical SIMD techniques in stable Rust involve leveraging compiler auto-vectorization and platform-specific intrinsics for performance gains. SIMD operations in Rust are beneficial for high-performance computing, multimedia processing, systems programming, embedded systems, and cryptography applications. While auto-vectorization simplifies SIMD usage, platform-specific intrinsics provide direct control for maximum performance. Developers should consider factors like data alignment, portability, complexity, and testing when implementing SIMD in Rust projects. Auto-vectorization in Rust optimizes code by transforming loops into SIMD instructions, but developers should focus on clear coding practices and benchmarking for performance validation. Platform-specific intrinsics in Rust, like ARM NEON for ARM architectures, offer direct access to SIMD instructions for specific CPU optimizations, enhancing performance in targeted applications.
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9 commentshttps://blog.habets.se/2024/04/Rust-is-faster-than-C.html and code at https://github.com/ThomasHabets/zipbrute/blob/master/rust/sr... showed me getting 3x faster using portable SIMD, on my first attempt.
One of my goals of writing these articles is to learn so feedback is more than welcome!
There is also a traditional SIMD extension (P I think?) but it isn't finished. Most focus has been on the vector extension.
I am wondering how and if Rust will support these vector processing extensions.
https://github.com/dotnet/runtime/blob/main/docs/coding-guid...
Here's an example of "checked" sum over a span of integers that uses platform-specific vector width:
https://github.com/dotnet/runtime/blob/main/src/libraries/Sy...
Other examples:
CRC64 https://github.com/dotnet/runtime/blob/main/src/libraries/Sy...
Hamming distance https://github.com/dotnet/runtime/blob/main/src/libraries/Sy...
Default syntax is a bit ugly in my opinion, but it can be significantly improved with helper methods like here where the code is a port of simdutf's UTF-8 code point counting: https://github.com/U8String/U8String/blob/main/Sources/U8Str...
There are more advanced scenarios. Bepuphysics2 engine heavily leverages SIMD to perform as fast as PhysX's CPU back-end: https://github.com/bepu/bepuphysics2/blob/master/BepuPhysics...
Note that practically none of these need to reach out to platform-specific intrinsics (except for replacing movemask emulation with efficient ARM64 alternative) and use the same path for all platforms, varied by vector width rather than specific ISA.
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