Accelerator-Centered Programming on Heterogeneous Systems

Abstract

Parallel many cores contribute to heterogeneous architectures and achieve high computation throughput. Working as coprocessors and connected to general-purpose CPUs via PCIe, those special-purpose cores usually work as float computing accelerators (ACC). The popular programming models typically offload the computing intensive parts to accelerator then aggregate results, which would result in a great amount of data transfer via PCIe. In this paper, we introduce an ACC-centered model to leverage the limited bandwidth of PCIe, increase performance, reduce idle time of ACC. In order to realize dada-near-computing, our ACC-centered model arms to program centered on ACC and the control intensive parts are offloaded to CPU. Both CPU and ACC are devoted to higher performance with their architect feature. Validation on the Tianhe-2 supercomputer shows that the implementation of ACC-centered LU competes with the highly optimized Intel MKL hybrid implementation and achieves about 5× speedup versus the CPU version.