Objective-driven workload allocation in heterogeneous computing systems

In this work, we explore heterogeneous computing hardware, including CPUs, GPUs and FPGAs, for scientific computing. We study system metrics such as throughput, energy efficiency and temperature, and formulate the problem of workload allocation among computing hardware in mathematical models with regards to the three metrics. The workload allocation approach is evaluated using Linpack on a hardware platform containing one CPU, one GPU and one FPGA. Results show that the heterogeneous computing system with appropriate workload allocation provides high energy efficiency with peak value at 1.1 GFLOPs/W and reduces power consumption by 56.54%; and that workload allocation schemes are significantly different with regards to different system metrics.