Generate platform-specific SIMD intrinsics for x86 and ARM Rust code.
npx skills add https://github.com/ahrav/scratch-scanner-rs --skill simd-optimize
Please help me install this Agent Skill. Skill: simd-optimize Source: https://github.com/ahrav/scratch-scanner-rs/tree/main/.claude/skills/simd-optimize Command: npx skills add https://github.com/ahrav/scratch-scanner-rs --skill simd-optimize
This Skill optimizes Rust code by automatically identifying and implementing Single Instruction, Multiple Data (SIMD) vectorization, significantly boosting performance for data-parallel computations.
Use the simd-optimize skill to vectorize the byte search loop in src/scanner/byte_search.rs.
High-intent search queries and answers about installing and using this skill.
SIMD vectorization optimizes Rust code by identifying data-parallel patterns and generating platform-specific intrinsics for x86 (SSE, AVX) and ARM (NEON, SVE) architectures. This Skill automates detection, intrinsics generation, and performance validation for critical processing loops.
Optimizing Rust array processing requires generating platform-specific intrinsics like x86 AVX-512 and ARM SVE. This Skill detects hardware ISA features and provides tiered research with fallback mechanisms to ensure broad architectural compatibility and maximum throughput.
Yes, this SIMD optimization approach supports both x86 and ARM platforms. It generates specific intrinsics for x86 architectures including SSE, AVX, and AVX-512, as well as ARM architectures featuring NEON and SVE instruction sets.
Validate SIMD intrinsics in Rust by using property-based testing and benchmarking to compare vectorized output against scalar code. This Skill includes performance validation mechanisms to ensure the optimized intrinsics maintain correctness while delivering measurable speed improvements.
Yes, cargo-show-asm is a required dependency to inspect assembly output and verify SIMD vectorization in Rust. It allows you to analyze the generated intrinsics and ensure the compiler effectively applies x86 or ARM instructions to your scalar code.
Manual SIMD intrinsics may not be suitable for Rust code lacking data-parallel patterns or when cross-platform portability outweighs raw speed. If the computation cannot be structured into large arrays of numbers, the complex intrinsics implementation will not yield meaningful throughput improvements.
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