SQRL: Hardware accelerator for collecting software data structures

Abstract

Software data structures are a critical aspect of emerging data-centric applications which makes it imperative to improve the energy efficiency of data delivery. We propose SQRL, a hardware accelerator that integrates with the last-level-cache (LLC) and enables energy-efficient iterative computation on data structures. SQRL integrates a data structure-specific LLC refill engine (Collector) with a compute array of lightweight processing elements (PEs). The collector exploits knowledge of the compute kernel to i) run ahead of the PEs in a decoupled fashion to gather data objects and ii) throttle fetch rate and adaptively tile the dataset based on the locality characteristics. The collector exploits data structure knowledge to find the memory level parallelism and eliminate data structure instructions.

DOI: 10.1145/2628071.2628118

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