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Main Title: E²MC: Entropy Encoding Based Memory Compression for GPUs
Author(s): Lal, Sohan
Lucas, Jan
Juurlink, Ben
Type: Conference Object
Language Code: en
Abstract: Modern Graphics Processing Units (GPUs) provide much higher off-chip memory bandwidth than CPUs, but many GPU applications are still limited by memory bandwidth. Unfortunately, off-chip memory bandwidth is growing slower than the number of cores and has become a performance bottleneck. Thus, optimizations of effective memory bandwidth play a significant role for scaling the performance of GPUs. Memory compression is a promising approach for improving memory bandwidth which can translate into higher performance and energy efficiency. However, compression is not free and its challenges need to be addressed, otherwise the benefits of compression may be offset by its overhead. We propose an entropy encoding based memory compression (E2MC) technique for GPUs, which is based on the well-known Huffman encoding. We study the feasibility of entropy encoding for GPUs and show that it achieves higher compression ratios than state-of-the-art GPU compression techniques. Furthermore, we address the key challenges of probability estimation, choosing an appropriate symbol length for encoding, and decompression with low latency. The average compression ratio of E2MC is 53% higher than the state of the art. This translates into an average speedup of 20% compared to no compression and 8% higher compared to the state of the art. Energy consumption and energy-delayproduct are reduced by 13% and 27%, respectively. Moreover, the compression ratio achieved by E2MC is close to the optimal compression ratio given by Shannon's source coding theorem.
Issue Date: 2017
Date Available: 5-Jun-2018
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): memory compression
Huffman compression
memory bandwidth
energy efficiency
Sponsor/Funder: EC/H2020/688759/EU/Low-Power Parallel Computing on GPUs 2/LPGPU2
Proceedings Title: 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Publisher: IEEE
Publisher Place: New York
Publisher DOI: 10.1109/IPDPS.2017.101
Page Start: 1119
Page End: 1128
EISSN: 1530-2075
ISBN: 978-1-5386-3914-6
Appears in Collections:FG Architektur eingebetteter Systeme » Publications

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