Yuanji Ye, M.Sc.

Prefetching is a widely used technique in modern processors to mitigate the memory wall problem by fetching data into faster memory levels before it is actually needed. While prefetching has been extensively studied and deployed in CPUs, where hierarchical cache designs (L1, L2, and off-chip DRAM) dominate, GPUs present a very different challenge.

GPUs are now central to diverse applications ranging from artificial intelligence to scientific computing. Their massively parallel architecture and SIMT execution model create distinct memory access behaviors compared to CPUs. Consequently, conventional CPU prefetching mechanisms are often ineffective or even harmful when applied to GPUs. This has led to the development of GPU-specific prefetching strategies that account for the unique architectural features and execution patterns of GPUs.

The goal of this seminar is to study and compare different GPU prefetching mechanisms. By reviewing recent research papers, participants will gain an understanding of how these mechanisms work, their advantages and limitations, and under what conditions they can improve GPU performance.

Prerequisites