Efficient CPU-GPU Work Sharing for Data-Parallel Java']JavaScript Workloads

被引:0
|
作者
Piao, Xianglan [1 ]
Kim, Channoh [1 ]
Oh, Younghwan [1 ]
Kim, Hanjun [2 ]
Lee, Jae W. [1 ]
机构
[1] Sungkyunkwan Univ, Suwon, South Korea
[2] POSTECH, Pohang, South Korea
关键词
Web browser; !text type='Java']Java[!/text]Script; data parallelism; GPU; work sharing; scheduler; multi-core; heterogeneity;
D O I
10.1145/2567948.2577338
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Modern web browsers are required to execute many complex, compute-intensive applications, mostly written in JavaScript. With widespread adoption of heterogeneous processors, recent JavaScript-based data-parallel programming models, such as River Trail and WebCL, support multiple types of processing elements including CPUs and GPUs. However, significant performance gains are still left on the table since the program kernel runs on only one compute device, typically selected at kernel invocation. This paper proposes a new framework for efficient work sharing between CPU and GPU for data-parallel JavaScript workloads. The work sharing scheduler partitions the input data into smaller chunks and dynamically dispatches them to both CPU and GPU for concurrent execution. For four data-parallel programs, our framework improves performance by up to 65% with a geometric mean speedup of 33% over GPU-only execution.
引用
收藏
页码:357 / 358
页数:2
相关论文
共 50 条
  • [1] JAWS: A Java']JavaScript Framework for Adaptive CPU-GPU Work Sharing
    Piao, Xianglan
    Kim, Channoh
    Oh, Younghwan
    Li, Huiying
    Kim, Jincheon
    Kim, Hanjun
    Lee, Jae W.
    [J]. ACM SIGPLAN NOTICES, 2015, 50 (08) : 251 - 252
  • [2] Transparent CPU-GPU Collaboration for Data-Parallel Kernels on Heterogeneous Systems
    Lee, Janghaeng
    Samadi, Mehrzad
    Park, Yongjun
    Mahlke, Scott
    [J]. 2013 22ND INTERNATIONAL CONFERENCE ON PARALLEL ARCHITECTURES AND COMPILATION TECHNIQUES (PACT), 2013, : 245 - 255
  • [3] EFFICIENT PARALLEL PROCESSING BY IMPROVED CPU-GPU INTERACTION
    Khatter, Harsh
    Aggarwal, Vaishali
    [J]. PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON ISSUES AND CHALLENGES IN INTELLIGENT COMPUTING TECHNIQUES (ICICT), 2014, : 159 - 161
  • [4] Simultaneous CPU-GPU Execution of Data Parallel Algorithmic Skeletons
    Wrede, Fabian
    Ernsting, Steffen
    [J]. INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING, 2018, 46 (01) : 42 - 61
  • [5] A user mode CPU-GPU scheduling framework for hybrid workloads
    Wang, Bin
    Ma, Ruhui
    Qi, Zhengwei
    Yao, Jianguo
    Guan, Haibing
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2016, 63 : 25 - 36
  • [6] Parallel Graph Partitioning on a CPU-GPU Architecture
    Goodarzi, Bahareh
    Burtscher, Martin
    Goswami, Dhrubajyoti
    [J]. 2016 IEEE 30TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW), 2016, : 58 - 66
  • [7] Analyzing OpenCL 2.0 Workloads Using a Heterogeneous CPU-GPU Simulator
    Wang, Li
    Tsai, Ren-Wei
    Wang, Shao-Chung
    Chen, Kun-Chih
    Wang, Po-Han
    Cheng, Hsiang-Yun
    Lee, Yi-Chung
    Shu, Sheng-Jie
    Yang, Chun-Chieh
    Hsu, Min-Yih
    Kan, Li-Chen
    Lee, Chao-Lin
    Yu, Tzu-Chieh
    Peng, Rih-Ding
    Yang, Chia-Lin
    Hwang, Yuan-Shin
    Lee, Jenq-Kuen
    Tsao, Shiao-Li
    Ouhyoung, Ming
    [J]. 2017 IEEE INTERNATIONAL SYMPOSIUM ON PERFORMANCE ANALYSIS OF SYSTEMS AND SOFTWARE (ISPASS), 2017, : 127 - 128
  • [8] Power-Aware Characterization and Mapping of Workloads on CPU-GPU Processors
    Dev, Kapil
    Zhan, Xin
    Reda, Sherief
    [J]. PROCEEDINGS OF THE 2016 IEEE INTERNATIONAL SYMPOSIUM ON WORKLOAD CHARACTERIZATION, 2016, : 225 - 226
  • [9] Efficient Parallel TLD on CPU-GPU Platform for Real-Time Tracking
    Chen, Zhaoyun
    Huang, Dafei
    Luo, Lei
    Wen, Mei
    Zhang, Chunyuan
    [J]. KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, 2020, 14 (01): : 201 - 220
  • [10] Offloading Accelerator-intensive Workloads in CPU-GPU Heterogeneous Processors
    Tsog, Nandinbaatar
    Mubeen, Saad
    Bruhn, Fredrik
    Behnam, Moris
    Sjodin, Mikael
    [J]. 2021 26TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 2021,