Bibliographic Details
| Title: |
Unleashing In-network Computing on Scientific Workloads |
| Authors: |
Kim, Daehyeok, Jain, Ankush, Liu, Zaoxing, Amvrosiadis, George, Hazen, Damian, Settlemyer, Bradley, Sekar, Vyas |
| Publication Year: |
2020 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Networking and Internet Architecture, Computer Science - Distributed, Parallel, and Cluster Computing |
| More Details: |
Many recent efforts have shown that in-network computing can benefit various datacenter applications. In this paper, we explore a relatively less-explored domain which we argue can benefit from in-network computing: scientific workloads in high-performance computing. By analyzing canonical examples of HPC applications, we observe unique opportunities and challenges for exploiting in-network computing to accelerate scientific workloads. In particular, we find that the dynamic and demanding nature of scientific workloads is the major obstacle to the adoption of in-network approaches which are mostly open-loop and lack runtime feedback. In this paper, we present NSinC (Network-accelerated ScIeNtific Computing), an architecture for fully unleashing the potential benefits of in-network computing for scientific workloads by providing closed-loop runtime feedback to in-network acceleration services. We outline key challenges in realizing this vision and a preliminary design to enable acceleration for scientific applications.
Comment: 8 pages, 3 figures |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2009.02457 |
| Accession Number: |
edsarx.2009.02457 |
| Database: |
arXiv |
