In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains. From parallel programming to exascale to quantum computing, the details are here.
Modern scientific computing requires a myriad of approaches with various advantages – triggers, accelerators, mobility – suited to specific uses. These authors (a team from the University of Chicago and Argonne National Laboratory) propose funcX: “a high-performance function-as-a-service (FaaS) platform that enables intuitive, flexible, efficient, scalable, and performant remote function execution on existing infrastructure[.]” The authors call this approach, which allows researchers to execute commands without specifying a physical resource, “serverless supercomputing.” They demonstrate results across experiments and deployments.
Authors: Ryan Chard, Tyler J. Skluzacek, Zhuozhao Li, Yadu Babuji, Anna Woodard, Ben Blaiszik, Steven Tuecke, Ian Foster and Kyle Chard.
Sparse matrix-vector multiplication (SpMV) is the core algorithm for solving sparse linear equations – a key tool in research and engineering fields. In this paper, the authors – a team from the Naval University of Engineering and the National University of Defense Technology in China – describe their design and implementation for an adaptive SpMV implementation on a CPU-GPU heterogeneous architecture, reporting significant performance gains.
Authors: Jing Nie, Chunlei Zhang, Dan Zou, Fei Xia, Lina Lu, Xiang Wang and Fei Zhao.