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SLC: Memory Access Granularity Aware Selective Lossy Compression for GPUs
Publikationstyp
Conference Paper
Date Issued
2019-03
Sprache
English
Author(s)
Start Page
1184
End Page
1189
Article Number
8714810
Citation
22nd Design, Automation and Test in Europe Conference and Exhibition (DATE 2019)
Contribution to Conference
Publisher DOI
Scopus ID
Memory compression is a promising approach for reducing memory bandwidth requirements and increasing performance, however, memory compression techniques often result in a low effective compression ratio due to large memory access granularity (MAG) exhibited by GPUs. Our analysis of the distribution of compressed blocks shows that a significant percentage of blocks are compressed to a size that is only a few bytes above a multiple of MAG, but a whole burst is fetched from memory. These few extra bytes significantly reduce the compression ratio and the performance gain that otherwise could result from a higher raw compression ratio. To increase the effective compression ratio, we propose a novel MAG aware Selective Lossy Compression (SLC) technique for GPUs. The key idea of SLC is that when lossless compression yields a compressed size with few bytes above a multiple of MAG, we approximate these extra bytes such that the compressed size is a multiple of MAG. This way, SLC mostly retains the quality of a lossless compression and occasionally trades small accuracy for higher performance. We show a speedup of up to 35% normalized to a state-of-the-art lossless compression technique with a low loss in accuracy. Furthermore, average energy consumption and energy-delay-product are reduced by 8.3% and 17.5%, respectively.