UC Santa Barbara

Philosophically, our approaches to acceleration focus on the extreme. We must optimise accelerators to the maximum, leaving software to fix any hardware-software mismatches. Today's software abstractions for programming accelerators leak hardware details, requiring changes to data formats and manual memory and coherence management, among other issues. This harms generality and requires deep hardware knowledge to efficiently program accelerators, a state which we consider hardware-oriented. This paper proposes Software-Oriented Acceleration (SOA), where software uses existing abstractions, like software shared-memory queues, to interact with accelerators. We introduce the Cohort engine which exploits these queues' standard semantics to efficiently connect producers and consumers in software with accelerators with minimal application changes. Accelerators are even usable in chains which can be runtime reconfigured by software. Cohort significantly reduces the burden to add new accelerators while maintaining system-level guarantees. We implement a Cohort FPGA prototype which supports SOA applications running on multicore Linux. Our evaluation shows speedups for Cohort over traditional approaches ranging from 1.83× to 8.38× over MMIO, and from 1.69× to 11.24× for DMA baselines. Our software-oriented batching optimisations within Cohort also improve performance from 2.32× to 8.10×, demonstrating the power of SOA.