This post details the end-to-end optimization of CUDA Graphs for sparse matrix multiplication across GPU clusters, covering architecture, implementation patterns, and performance benchmarks.
A hands‑on guide to training a tiny GPT model end‑to‑end with gradient checkpointing, FP16 mixed precision, and a hand‑rolled optimizer, perfect for a CV‑worthy portfolio.
Sparse matrix multiplication is a bottleneck in many GPU workloads. This guide covers storage formats, kernel-level optimization strategies, and profiling techniques to extract maximum performance from your CUDA kernels.
CUDA Graphs capture and replay GPU workloads, reducing kernel launch overhead and cutting inference latency by up to 40% in production systems.
CUDA graphs reduce kernel launch overhead by batching operations. They can deliver up to 40% speedups in real-world inference pipelines.