The Impact of Adopting Computational Storage in Heterogeneous Computing Systems Heterogeneous computing systems have been the focus of major efforts in pursuit of Exascale computing over the past few years. These heterogeneous computing systems, often contain various accelerators, can significantly improve the computation performance thanks to their parallel compute cores. Much research has exploited accelerators to reduce the computation time; however, IO access time or endtoend total execution time is often overlooked. Typically, when IO access time with the storage is considered, the performance gain from accelerators can be substantially degraded. In this work, we designed a computational storage in a heterogeneous computing system. Our evaluation employs a realworld computebound machine learning application on three computing systems: A conventional CPU system, a modern FPGA system, and the computational storage system. Compared to CPU system, we found that the modern FPGA system can achieve a 100x speedup for computation and 62x better energy efficiency, but only 26 xc3x97 speedup and 16 xc3x97 energy efficiency improvement when data movement time is taken into account; whereas the computational storage system can achieve 63 xc3x97 computation speedup and 51 xc3x97 better energy efficiency including data movement time. The experiment demonstrates that a computebound problem on conventional CPU system may become an IObound problem on heterogeneous system. By adopting computational storage in heterogeneous system, we find that both the computation performance and the IO performance is improved.

Design and Implementation of the Tianhe2 Data Storage and Management System With the convergence of highperformance computing (HPC), big data and artificial intelligence (AI), the HPC community is pushing for xe2x80x9ctriple usexe2x80x9d systems to expedite scientific discoveries. However, supporting these converged applications on HPC systems presents formidable challenges in terms of storage and data management due to the explosive growth of scientific data and the fundamental differences in IO characteristics among HPC, big data and AI workloads. In this paper, we discuss the driving force behind the converging trend, highlight three data management challenges, and summarize our efforts in addressing these data management challenges on a typical HPC system at the parallel file system, data management middleware, and user application levels. As HPC systems are approaching the border of exascale computing, this paper sheds light on how to enable applicationdriven data management as a preliminary step toward the deep convergence of exascale computing ecosystems, big data, and AI.