GPU Performance Engineering Resources Curriculum

Summary

GPU Performance Engineering Resources is a comprehensive learning guide for engineers to master GPU kernel programming and optimization for high-performance AI systems. It covers fundamentals through production deployment with structured tiers of resources including architecture deep dives, matrix multiplication optimization, tensor cores, and distributed multi-GPU systems.

Tags

gpu-programming · performance-engineering · cuda · kernel-optimization · ai-infrastructure · tensor-cores

Key entities

NVIDIA (organization, 0.99) · Wafer (organization, 0.85) · DeepSeek (organization, 0.9) · PyTorch (organization, 0.95) · Colfax Research (organization, 0.85) · CUDA (technology, 0.99) · cuBLAS (technology, 0.98) · CUTLASS (technology, 0.98) · Triton (technology, 0.95) · ROCm (technology, 0.95) · FlashAttention (technology, 0.95) · Tensor Cores (technology, 0.99) · GPU kernel programming (concept, 0.99) · Matrix Multiplication Optimization (concept, 0.98) · Mixed Precision (concept, 0.95) · Distributed Multi-GPU (concept, 0.95) · Hwu (person, 0.8) · Kirk (person, 0.8) · El Hajj (person, 0.8) · Aleksa Gordić (person, 0.85) · Lei Mao (person, 0.85)

Classification

reference · language en · status final

Provenance

claude-haiku-4-5 via @stacklist/be@0.1.0, confidence 0.85, 29 Jun 2026

Full body

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