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Memory access efficiency is a key factor in fully utilizing the computationalpower of graphics processing units (GPUs). However, many details of the GPUmemory hierarchy are not released by GPU vendors. In this paper, we propose anovel fine-grained microbenchmarking approach and apply it to three generationsof NVIDIA GPUs, namely Fermi, Kepler and Maxwell, to expose the previouslyunknown characteristics of their memory hierarchies. Specifically, weinvestigate the structures of different GPU cache systems, such as the datacache, the texture cache and the translation look-aside buffer (TLB). We alsoinvestigate the throughput and access latency of GPU global memory and sharedmemory. Our microbenchmark results offer a better understanding of themysterious GPU memory hierarchy, which will facilitate the softwareoptimization and modelling of GPU architectures. To the best of our knowledge,this is the first study to reveal the cache properties of Kepler and MaxwellGPUs, and the superiority of Maxwell in shared memory performance under bankconflict.

Dissecting GPU Memory Hierarchy through Microbenchmarking