Exploiting HLS-Generated Multi-Version Kernels to Improve CPU-FPGA Cloud Systems

Abstract

Cloud Warehouses have been exploiting CPU-FPGA collaborative execution environments, where multiple clients share the same infrastructure to achieve to maximize resource utilization with the highest possible energy efficiency and scalability. However, the resource provisioning is challenging in these environments, since kernels may be dispatched to both CPU and FPGA concurrently in a highly variant scenario, in terms of available resources and workload characteristics. In this work, we propose MultiVers, a framework that leverages automatic HLS generation to enable further gains in such CPU-FPGA collaborative systems. MultiVers exploits the automatic generation from HLS to build libraries containing multiple versions of each incoming kernel request, greatly enlarging the available design space exploration passive of optimization by the allocation strategies in the cloud provider. Multivers makes both kernel multiversioning and allocation strategy to work symbiotically, allowing fine-tuning in terms of resource usage, performance, energy, or any combination of these parameters. We show the efficiency of MultiVers by using real-world cloud request scenarios with a diversity of benchmarks, achieving average improvements on makespan and energy of up to 4.62× and 19.04×, respectively, over traditional allocation strategies executing non-optimized kernels.