This article breaks down NVIDIA GPU fundamentals—contrasting GPU with CPU design, tracing CUDA’s evolution, detailing the hardware hierarchy from chips to SM units, explaining memory tiers, and presenting a step‑by‑step performance‑optimization methodology for AI training and inference workloads.
This article explains why GPUs have become the preferred platform for high‑performance computing, covering Dennard scaling, GPU speed advantages, theoretical FLOPS calculations, CUDA programming examples like SAXPY, the SIMT execution model, instruction pipelines, and modern techniques for handling branch divergence and register bank conflicts.
The article explains GPU fundamentals—from the end of Dennard scaling and why GPUs excel in parallel throughput, through CUDA programming basics like the SAXPY kernel and SIMT versus SIMD execution, to the evolution of the SIMT stack, modern scheduling, and a three‑step core architecture design.
This article provides a comprehensive technical overview of modern GPU architecture, covering memory hierarchy, compute units, shader execution, rendering pipelines, and performance‑optimisation techniques such as unified shaders, SIMT, warp scheduling, and tile‑based rendering strategies.
This article provides a detailed technical overview of the Stream Multi‑processor (SM) in modern GPUs, explaining its micro‑architecture, instruction fetch‑decode pipeline, warp scheduling, SIMT stack handling, scoreboard mechanisms, and strategies for hiding memory latency to maximize parallel execution efficiency.
This article provides a comprehensive overview of GPU stream multiprocessors, detailing their micro‑architecture, instruction fetch‑decode‑execute pipeline, SIMT/ SIMD organization, warp scheduling, scoreboard mechanisms, and techniques for handling thread divergence and deadlock in GPGPU designs.
