Compacted CPU/GPU Data Compression via Modified Virtual Address Translation

PubDate: August 15, 2020

Teams: Facebook Reality Labs；University of Utah

Writers: Larry Seiler, Daqi Lin, Cem Yuksel

PDF: Compacted CPU/GPU Data Compression via Modified Virtual Address Translation

Abstract

We propose a method to reduce the footprint of compressed data by using modified virtual address translation to permit random access to the data. This extends our prior work on using page translation to perform automatic decompression and deswizzling upon accesses to fixed rate lossy or lossless compressed data.

Our compaction method allows a virtual address space the size of the uncompressed data to be used to efficiently access variable-size blocks of compressed data. Compression and decompression take place between the first and second level caches, which allows fast access to uncompressed data in the first level cache and provides data compaction at all other levels of the memory hierarchy. This improves performance and reduces power relative to compressed but uncompacted data.

An important property of our method is that compression, decompression, and reallocation are automatically managed by the new hardware without operating system intervention and without storing compression data in the page tables. As a result, although some changes are required in the page manager, it does not need to know the specific compression algorithm and can use a single memory allocation unit size.

We tested our method with two sample CPU algorithms. When performing depth buffer occlusion tests, our method reduces the memory footprint by 3.1?. When rendering into textures, our method reduces the footprint by 1.69? before rendering and 1.63? after. In both cases, the power and cycle time are better than for uncompacted compressed data, and significantly better than for accessing uncompressed data.