Packet coalescing exploiting data redundancy in GPGPU architectures
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2017 Association for Computing Machinery. General Purpose Graphics Processing Units (GPGPUs) are becoming a cost-effective hardware approach for parallel computing. Many executions on the GPGPUs place heavy stress on the memory system, creating network bottlenecks near memory controllers. We observe that data redundancy in communication traffic is commonplace across a wide range of GPGPU applications. To exploit the data redundancy, we propose a packet coalescing mechanism to alleviate the network bottlenecks by directly reducing the traffic volume. The key idea is to coalesce multiple packets into one without increasing the packet size when they carry redundant cache blocks. To ensure that the coalesced packets are delivered to their respective destinations, we adopt multicast routing for the interconnection network of GPGPUs. Our coalescing approach yields 15% IPC improvement (up to 112%) in a large-scale GPGPU with 2D mesh across various GPGPU applications, by reducing average memory access time (AMAT) by 15.5% (up to 65.2%) and obtaining network bandwidth savings by 13% (up to 37%). Also, our coalescing approach achieves 7% IPC improvement in the NVIDIA Fermi architecture with the crossbar.
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Proceedings of the International Conference on Supercomputing
