cloudvoyant/hpc

hpc · experimental

Context skill for high-performance and distributed computing in Python and C++.

Philosophy

Performance work is measurement-driven. Every recipe follows the same pattern: establish a baseline, apply the technique, measure the improvement. Techniques that can't be measured shouldn't be applied. Recipes cover threading, SIMD, GPU compute, MPI clustering, and distributed Python with Ray — from laptop to multi-node cluster.

Recipes

• C++ Threading Primitives — std::thread, std::async, std::jthread; thread-safe patterns
• OpenMP — pragma-based loop/section parallelism; reduction patterns
• Intel TBB — task graphs, parallel_for, pipeline, flow graph
• SIMD with AVX2/NEON — intrinsics, auto-vectorization, measurement
• GPU Compute with CUDA — kernels, memory hierarchy, streams, Nsight
• GPU Compute with SYCL/oneAPI — write-once GPU code for Intel/NVIDIA/AMD
• MPI Fundamentals — point-to-point, collectives, MPI+OpenMP hybrid
• Python Parallelism — multiprocessing, threading, asyncio; GIL
• Distributed Compute with Ray — tasks, actors, placement groups, clusters
• HPC Project Conventions — CMake setup, profiling tools, benchmarking
• Thrust: GPU Parallel Algorithms — STL-style sort/reduce/scan/transform on GPU, device_vector, fancy iterators, stream-async
• Kokkos: Performance Portability — write-once C++ for CUDA/HIP/OpenMP/SYCL, Views, parallel_for/reduce/scan, thread teams
• NVIDIA Warp (Python GPU) — Python kernels compiled to PTX, tiles, streams, autodiff, simulation loops

References

• OpenMP specification
• Intel TBB documentation
• CUDA Programming Guide
• Ray documentation
• MPI Forum