Tag: #parallel-computing
Writing on GPUs, LLMs, MLOps, Kubernetes — and mindset · 7 posts
Real GPU Compute in the Browser — A Hands-On Guide to WebGPU Compute Shaders and WGSL in 2026
WebGPU's real attraction isn't 3D — it's compute. We run matrix math, ML inference, image filters and parallel reductions inside the browser, in WGSL, without CUDA and without wasm. Workgroups, dispatch, bind groups, sto
2026-05-14 · 17 min read #webgpu#compute-shader#wgsl#gpu-computing#browserCUDA GPU Programming Model Deep Dive — SIMT, Memory Hierarchy, Tensor Core, Kernel Optimization (2025)
The engine behind ChatGPT, Stable Diffusion, and Sora — NVIDIA GPUs and CUDA. This post dissects the CUDA programming model from scratch: GPU hardware architecture (SM, Warp, CUDA Core), SIMT vs SIMD, thread hierarchy (G
2026-04-15 · 15 min read #cuda#gpu#nvidia#ai#machine-learningNVIDIA GPU and CUDA Architecture Deep Dive: Why GPUs Dominate AI
From H100 hardware specs to Tensor Core WMMA API. A complete breakdown of the SIMT execution model, shared memory tiling, warp divergence, and why GPUs are purpose-built for AI workloads.
2026-03-18 · 14 min read #cuda#gpu#nvidia#matrix-multiplication#parallel-computingHow Matrices Fly on GPU: Complete Deep Dive from GEMM to FlashAttention
Matrix multiplication accounts for over 80% of deep learning compute. This post dissects every layer of optimization from naive O(n³) code to cache blocking, cuBLAS, Tensor Cores, and the IO-aware revolution of FlashAtte
2026-03-18 · 15 min read #gemm#matrix-multiplication#flash-attention#gpu#parallel-computingCUDA Programming Complete Guide: GPU Parallel Computing Zero to Hero
A comprehensive guide to mastering CUDA programming from fundamentals to advanced optimization. Covers GPU architecture, kernel writing, memory optimization, mixed precision training, and cuDNN/cuBLAS usage with practica
2026-03-17 · 30 min read #cuda#gpu#gpu-cuda#parallel-computing#nvidiaCUDA Hands-on Complete Guide: Everything About GPU Computing
A comprehensive guide covering NVIDIA CUDA from fundamental concepts to GPU architecture, C/C++ kernel programming, memory optimization, Python integration (PyTorch, Numba, CuPy), Multi-GPU training, profiling, and troub
2026-03-01 · 51 min read #cuda#gpu#nvidia#deep-learning#machine-learningCUDA Programming Fundamentals: Complete Guide to GPU Parallel Computing
Systematically analyze the core concepts of CUDA programming including thread hierarchy, memory model, and kernel development based on NVIDIA official documentation.
2026-03-01 · 24 min read #gpu#cuda#parallel-computing#nvidia