Multi-accelerator cluster runtime adaptation for enabling discrete concurrent-task applications

Proliferation of GPGPU and other accelerators, is making the industry consider accelerator based systems as a viable option for high-performance: low-power HPC systems. This paper describes a multi-accelerator heterogeneous cluster in which each node has GPGPU and FPGA cards. Extracting the maximum computational power simultaneously of all the compute elements, i.e. multi-core CPU, GPGPU and FPGA is an important challenge. StarPU is a popular open source runtime that supports heterogeneous architectures. This paper describes the key features of heterogeneous runtime and how StarPU has been adapted to execute parallel programs which span across both GPGPU and FPGA accelerators.