GPU Memory Hierarchy Explained: Registers, Shared Memory, L2, HBM, and PCIe (Visual) | M2L2
Why does GPU performance depend more on where data lives than on how fast the cores are?
In Module 2 · Lesson 2, this video builds a clear, hardware-level understanding of the GPU memory hierarchy — from registers inside an SM to shared memory, L2 cache, HBM3, and finally CPU system memory over PCIe.
Using visual explanations, we show how each memory level fits into the architecture, what it’s optimized for, and why access patterns determine real performance. Concepts like memory coalescing and shared memory bank conflicts are referenced where they matter.
This lesson gives you the mental model needed to reason about GPU performance before writing or optimizing kernels.
📺 Related videos:
• Shared Memory Bank Conflicts — Explained Visually: • Why GPU Shared Memory Becomes Slow | Bank ...
• Global Memory Coalescing — Explained Visually: • GPU Memory Coalescing Explained: Warp-Leve...
⏱️ Timeline Overview
00:00 — Why memory hierarchy defines GPU performance
00:13 — Isolating the Streaming Multiprocessor (SM)
00:22 — The GPU memory pyramid
00:35 — CPU system memory and PCIe constraints
00:49 — HBM3: bandwidth vs latency
01:07 — L2 cache and reuse
01:21 — L1 cache vs shared memory
01:36 — Registers and per-thread allocation
01:52 — Full memory hierarchy overview
01:58 — CPU memory access details
02:20 — HBM3 access and coalescing
02:43 — L2 cache behavior
03:05 — Shared memory banks and conflicts
03:24 — Register files and warp switching
03:59 — Final performance takeaways
📌 Final takeaway
GPU performance is governed by data locality.
If your data is close, computation flows.
If it is far, no amount of compute can save you.
Understanding the GPU memory hierarchy turns optimization from guesswork into engineering.
Hashtags
#GPUProgramming #CUDA #GPUMemory #MemoryHierarchy #HighPerformanceComputing
#ParallelComputing #HBM #SharedMemory #Registers #L2Cache #Optimization