Automatic GPU Data Compression and Address Swizzling for CPUs via Modified Virtual Address Translation
PubDate: May, 2020
Teams: Facebook Reality Labs, University of Utah
Writers: Larry Seiler, Daqi Lin, Cem Yuksel
Abstract
We describe how to modify hardware page translation to enable CPU software access to compressed and swizzled GPU data arrays as if they were decompressed and stored in row-major order. In a shared memory system, this allows CPU to directly access the GPU data without copying the data or losing the performance and bandwidth benefits of using compression and swizzling on the GPU.
Our method is flexible enough to support a wide variety of existing and future swizzling and compression schemes, including block-based lossless compression that requires per-block meta-data.
Providing automatic compression can improve performance, even without considering the cost of copying data. In our experiments, we observed up to 33% reduction in CPU/memory energy use and up to 35% reduction in CPU computation time.
