Master GPU memory hierarchy from registers to global memory, understand coalescing patterns, bank conflicts, and optimization strategies for maximum performance

Complete guide to GPU memory hierarchy, coalescing patterns, shared memory optimization, cache utilization, and performance tuning strategies

GPU Memory Hierarchy & Optimization

How a single SM actually runs work: warps of 32, cutaway of cores and memory, divergence tax, latency-hiding occupancy, and coalesced loads — instruments, not a catalog.

Streaming Multiprocessor deep dive: SIMT warps, CUDA and Tensor Cores, warp divergence, latency hiding, occupancy tradeoffs, and coalesced global memory on modern NVIDIA GPUs.

GPU Streaming Multiprocessor (SM)

Amdahl and Gustafson ceilings, strong vs weak scaling, roofline bounds, and hiding all-reduce behind compute — the levers that decide whether more GPUs actually buy science.

Parallel Computing

Concepts Related to Parallel Computing

GPU Memory Hierarchy & Optimization

GPU Streaming Multiprocessor (SM)

HPC Performance Optimization: Scaling, Profiling, and Tuning