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Shuffle is an indispensable process in distributed online analytical processing systems to enable task-level parallelism exploitation via multiple nodes. As a data-intensive data reorganization process, shuffle implemented on general-purpose CPUs not only incurs data traffic back and forth between the computing and storage resources, but also pollutes the cache hierarchy with almost zero data reuse. As a result, shuffle can easily become the bottleneck of distributed analysis pipelines.

Our PSACS approach attacks these bottlenecks with the rising computational storage paradigm. Shuffle is offloaded to the storage-side PSACS accelerator to avoid polluting computing node memory hierarchy and enjoy the latency, bandwidth and energy benefits of near-data computing. Further, the microarchitecture of PSACS exploits data-, subtask-, and task-level parallelism for high performance and a customized scratchpad for fast on-chip random access. PSACS achieves 4.6 x− 5.7 x shuffle throughput at kernel-level and up to 1.3 x overall shuffle throughput with only a twentieth of CPU utilization comparing to software baselines. These mount up to 23% end-to-end OLAP query speedup on average.