首页|Reproducing Performance of Data-Centric Python by SCC Team From National Tsing Hua University

Reproducing Performance of Data-Centric Python by SCC Team From National Tsing Hua University

扫码查看
As part of the Student Cluster Competition at the SC22 conference, this work aims to reproduce the performance evaluations of the Data Centric (DaCe) Python framework by leveraging Intel MKL and NVIDIA CUDA interface. The evaluations are conducted on a single CPU-based node, NVIDIA A100 GPUs, and an eight-node cloud supercomputer. Our experimental results successfully reproduce the performance evaluations on our cluster. Additionally, we provide insightful analysis and propose effective methods for achieving higher performance when utilizing DaCe as an acceleration library.

Graphics processing unitsBenchmark testingLibrariesPythonParallel processingRuntimeKernel

Fu-Chiang Chang、En-Ming Huang、Pin-Yi Kuo、Chan-Yu Mou、Hsu-Tzu Ting、Pang-Ning Wu、Jerry Chou

展开 >

Department of Computer Science, National Tsing Hua Univeristy, Hsinchu City, Taiwan

2025

IEEE transactions on parallel and distributed systems

IEEE transactions on parallel and distributed systems

SCI
ISSN:
年,卷(期):2025.36(5)
  • 14