@inproceedings{c35fa2ec7e0547a2899a18396b70e2ef,
title = "Computation and Communication Aware task graph Scheduling on multi-GPU systems",
abstract = "GPUs have emerged as popular throughput computing platforms due to the massively parallel computing capability and low cost. To attain further performance enhancement beyond single GPU, there is a growing interest in exploiting systems with multiple GPUs. Attaining superior performance in a multi-GPU system involves three main design challenges, namely load balance, memory utilization, and data transfer. Imbalanced loading across a system could cause idling of GPUs while poor data reuse would trigger excessive memory accesses. The inefficient data transfer between a host and a device becomes a considerable performance overhead during high throughput computing. This paper aims at addressing the above design issues by proposing a Computation and Communication Aware task graph Scheduling (CCAS) for multi-GPU systems. The proposed scheduling approach (CCAS) adopts an effective heuristic algorithm that considers both data reuse and load balance in a multi-GPU system. The data transfer overhead is hidden by extensively overlapping computation and data communication. The experimental results of the proposed CCAS have demonstrated an average of 22.15% performance enhancement when compared with a previous work.",
keywords = "GPUs, Scheduling, Task Graph",
author = "Wang, {Yun Ting} and Lee, {Jia Ying} and Bo-Cheng Lai",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE International Conference on Digital Signal Processing, DSP 2015 ; Conference date: 21-07-2015 Through 24-07-2015",
year = "2015",
month = sep,
day = "9",
doi = "10.1109/ICDSP.2015.7251841",
language = "English",
series = "International Conference on Digital Signal Processing, DSP",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "115--119",
booktitle = "2015 IEEE International Conference on Digital Signal Processing, DSP 2015",
address = "美國",
}