Identification and Elimination of the Overhead of Accelerate with a Super-resolution Application

• Asakura Izumi

  School of Computing, Tokyo Institute of Technology

• Masuhara Hidehiko

  School of Computing, Tokyo Institute of Technology

• Matsumoto Takuya

  Graduate School of Engineering, Kochi University of Technology

• Matsuzaki Kiminori

  School of Information, Kochi University of Technology

抄録

This paper supplements the evaluation of libraries for parallel computing in Haskell by Matsumoto and Matsuzaki. The previous work implemented an image processing program by using two different libraries and compared expressiveness and parallel execution performance. In particular, it found that a parallel execution of the Accelerate program on a GPU is slower than the execution of the handwritten CUDA-C program by a factor of more than 70. We analyzed the execution of the Accelerate program and identified that the major sources of the overhead are in the process of the syntax tree before execution of a GPU kernel. Since the program realizes repeated execution by constructing a large syntax tree whose size is proportional to the number of repetitions, we rewrote this part by using a while-construct, which significantly reduces the overhead as it runs half as fast as the CUDA-C program. We also discuss language design alternatives for avoiding this type of overhead.

収録刊行物

• Journal of Information Processing

  Journal of Information Processing 26 (0), 48-53, 2018

  一般社団法人 情報処理学会

キーワード

• GPGPU
• Accelerate

詳細情報

• CRID: 1390282680270416128
• NII論文ID: 130006309267
• DOI: 10.2197/ipsjjip.26.48
• ISSN: 18826652
• Web Site: https://www.jstage.jst.go.jp/article/ipsjjip/26/0/26_48/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可