Improve HMC Based Graph Processing By Adding Compress/Decompress Unit

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Addis Ababa University


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.



HMC, Graph Processing