Can it help with memory-bound applications?

Absolutely. It focuses on cache-friendly layouts (SoA), cache line alignment to prevent false sharing, and NUMA-aware allocation for multi-socket systems.

Does it support explicit SIMD intrinsics?

Yes, it includes patterns for both compiler auto-vectorization hints (OpenMP SIMD) and explicit AVX2/AVX-512 intrinsics when manual control is required.

How does this skill improve C++ performance?

It provides patterns for cache locality, SIMD vectorization, and efficient threading to maximize hardware utilization and minimize memory bottlenecks.

Is it compatible with modern C++ standards?

Yes, the patterns are specifically tailored for C++20, utilizing modern features like std::span, std::jthread, and enhanced atomic operations.

HPC Patterns for C++20

Name: HPC Patterns for C++20
Author: ysyecust

byysyecust

Developer Tools

Optimizes C++20 applications using high-performance computing patterns like SIMD, cache-friendly data structures, and NUMA-aware memory management.

About

This skill provides a comprehensive library of domain-specific patterns and best practices for building high-performance C++20 applications. It enables Claude to implement cache-optimized data structures like Structure of Arrays (SoA), leverage SIMD vectorization with AVX intrinsics, and manage memory via aligned allocators and pools. By providing guidance on thread parallelism, lock-free structures, and NUMA-aware allocation, this skill helps developers maximize hardware utilization for scientific simulations, real-time systems, and performance-critical backends.

Key Features

Cache-friendly data structures (SoA vs AoS and Tiling)
Custom Aligned Memory Management and efficient Memory Pools
Lock-free data structures and NUMA-aware memory allocation
0 GitHub stars
Advanced Threading with Work Stealing and Parallel For loops
SIMD Vectorization via compiler hints and AVX intrinsics

Use Cases

Optimizing data processing pipelines for maximum cache and SIMD efficiency
Implementing low-latency, thread-safe communication in multi-core systems
Accelerating numerical simulations and scientific computing workloads

About

Key Features

Cache-friendly data structures (SoA vs AoS and Tiling)
Custom Aligned Memory Management and efficient Memory Pools
Lock-free data structures and NUMA-aware memory allocation
0 GitHub stars
Advanced Threading with Work Stealing and Parallel For loops
SIMD Vectorization via compiler hints and AVX intrinsics

Use Cases

Optimizing data processing pipelines for maximum cache and SIMD efficiency
Implementing low-latency, thread-safe communication in multi-core systems
Accelerating numerical simulations and scientific computing workloads