GPU Computing With CUDA - Efficient Shared Memory Use Pt 1 | Boston University - Edubirdie

University:
Boston University
Course:
MET CS 201 | Introduction to Programming
Academic year:

2024
Views:

386

Pages:

16
Author:

Brenden Trevino

GPU Computing with CUDA - Efficient Shared Memory Use Outline of lecture ‣ Recap of Lecture 2 ‣ Shared memory in detail ‣ Tiling ‣ Bank conflicts ‣ Thread synchronization and atomic operations Recap ‣ Thread hierarchy - Thread are grouped in thread blocks - Threads of the same block are executed on the same SM at the same time ‣ Threads can communicate with shared memory ‣ An SM can have up to 8 blocks at the same time - Thread blocks are divided sequentially into warps of 32 threads each - Threads of the same warp are scheduled together - SM implements a zero-overhead warp scheduling Recap ‣ Memory hierarchy Smart use of memory hierarchy! Recap ‣ Programming model: Finite Difference case - One node per thread - Node indexing automatically groups into thread blocks! Recap ‣ Programming model: Finite Difference case - One node per thread - Node indexing automatically groups into thread blocks! Thread = node Thread block Shared Memory ‣ Small (48kB per SM) ‣ Fast (~4 cycles): On-chip ‣ Private to each block - Allows thread communication ‣ How can we use it? Shared Memory - Making use of it ‣ Looking at a 1D FDM example (similar to lab)

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GPU Computing With CUDA - Efficient Shared Memory Use Pt 1

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