GPU Warps Explained: How SIMT Really Works Under the Hood (Visual Deep Dive) | M2L3 скачать в хорошем качестве

GPU Warps Explained: How SIMT Really Works Under the Hood (Visual Deep Dive) | M2L3

How can a GPU execute thousands of threads at once—and why does a single instruction control all of them? In Module 2 · Lesson 3, this video explains SIMT execution (Single Instruction, Multiple Threads) from first principles, connecting the programming model to real GPU hardware. We begin with scalar execution, build intuition through SIMD parallelism, and then show how GPUs scale this model using warps, batching, and streaming multiprocessors. Using step-by-step visuals, we trace how a simple kernel: Maps threads to data Computes global indices Loads from memory Executes arithmetic in lockstep Writes results back to global memory We then move below the abstraction layer into the Streaming Multiprocessor (SM), examining warp scheduling, execution partitions, register files, load/store units, and how memory latency is hidden through massive parallelism. This lesson builds the mental model needed to understand: Why GPUs require thousands of threads How SIMD becomes SIMT Why do warps stall on memory How hardware, not software, manages execution complexity 📺 Related videos GPU Memory Hierarchy Explained: Registers, Shared Memory, L2, HBM, and PCIe (Visual) | M2L2    • GPU Memory Hierarchy Explained: Registers,...   GPU Memory Coalescing Explained (Visual) Why GPU Shared Memory Becomes Slow | Bank Conflicts Explained ⏱️ Timeline Overview 00:00 — How GPUs execute thousands of threads 00:18 — Scalar execution: the sequential baseline 00:39 — SIMD parallelism and execution lanes 00:55 — SIMD masking and inactive lanes 01:45 — Batching data into parallel execution groups 02:04 — Kernel programming model intuition 03:44 — Warps, scheduling, and SM execution 04:08 — SM partitions and execution units 04:55 — Register files and per-thread state 05:15 — Load/store units and memory flow 05:31 — PTX, SASS, and instruction expansion 06:16 — Stepping through real instruction execution 07:37 — Memory latency and warp stalling 08:45 — Arithmetic execution in lockstep 09:18 — Stores and warp completion 09:32 — Final SIMT execution takeaways 📌 Final Takeaway GPUs do not execute “many instructions at once.” They execute one instruction across many threads, repeatedly, at a massive scale. SIMT is the architectural trick that turns simple SIMD execution into the engine behind graphics, HPC, and AI. Understanding this model is the key to reasoning about GPU performance. #GPUArchitecture #SIMT #Warps #CUDA #ParallelComputing #ComputerArchitecture #HighPerformanceComputing