Improve HMC Based Graph Processing By Adding Compress/Decompress Unit
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Date
2022-05
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Addis Ababa University
Abstract
Graphs play an important role in various practical application areas from social science to
machine learning. However, due to the irregular data access pattern of graph computation,
there is a major challenge in graph processing.
The emergence of the technology called Hybrid memory cube(HMC) has helped graph processing
accelerators to overcome this issue. This hardware provides e cient bandwidth to
the graph computation, however, the communication tra c between memory cubes limits
the performance. To overcome this issue we proposed a new approach for HMCs based
accelerators by adding a packet compression/ decompression unit. We used Message Fussion
and Tesseract as our baseline system. In our approach, the data sent between the
memory cubes will be compressed before being sent into the network. From the experimental
result, the proposed approach showed 1.7x performance improvement on average
over the baseline systems. In addition, the energy consumption by the transmission of the
network is reduced by 47.28% over the baseline system and the compressor/decompressor
unit takes 25% of the total area.
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Keywords
HMC, Graph Processing