HaoCL: Harnessing Large-scale Heterogeneous Processors Made Easy
| Autor: | Hongshi Tan, Jiong He, Xin Long, Zhenxiang Zhang, Deming Chen, Yuhang Chen, Marianne Winslett, Yao Chen |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Distributed Computing Environment Source code Semantics (computer science) Computer science media_common.quotation_subject Distributed computing computer.software_genre Network topology Software framework Computer Science - Distributed Parallel and Cluster Computing Systems design Overhead (computing) Graph (abstract data type) Distributed Parallel and Cluster Computing (cs.DC) computer media_common |
| Zdroj: | ICDCS |
| Popis: | The pervasive adoption of Deep Learning (DL) and Graph Processing (GP) makes it a de facto requirement to build large-scale clusters of heterogeneous accelerators including GPUs and FPGAs. The OpenCL programming framework can be used on the individual nodes of such clusters but is not intended for deployment in a distributed manner. Fortunately, the original OpenCL semantics naturally fit into the programming environment of heterogeneous clusters. In this paper, we propose a heterogeneity-aware OpenCL-like (HaoCL) programming framework to facilitate the programming of a wide range of scientific applications including DL and GP workloads on large-scale heterogeneous clusters. With HaoCL, existing applications can be directly deployed on heterogeneous clusters without any modifications to the original OpenCL source code and without awareness of the underlying hardware topologies and configurations. Our experiments show that HaoCL imposes a negligible overhead in a distributed environment, and provides near-linear speedups on standard benchmarks when computation or data size exceeds the capacity of a single node. The system design and the evaluations are presented in this demo paper. Accepted by ICDCS 2020 |
| Databáze: | OpenAIRE |
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