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A pipeline implementation for dynamic programming on GPU

発表形態:
原著論文
主要業績:
主要業績
単著・共著:
共著
発表年月:
2018年11月
DOI:
10.1109/CANDARW.2018.00063
会議属性:
国際会議(国内開催を含む)
査読:
有り
リンク情報:

日本語フィールド

著者:
Miyazaki, Makoto; Matsumae, Susumu
題名:
A pipeline implementation for dynamic programming on GPU
発表情報:
Proceedings - 2018 6th International Symposium on Computing and Networking Workshops, CANDARW 2018 ページ: 305 - 309
キーワード:
概要:
© 2018 IEEE. In this paper, we show the effectiveness of a pipeline implementation of Dynamic Programming (DP) on GPU. As an example, we parallelize a typical DP program where each element of its solution table is calculated in order by semigroup computations among some already computed elements in the table. We implement the DP program on GPU in a pipeline fashion, i.e., we use GPU cores for supporting pipeline-stages so that many elements of the solution table are partially computed in parallel at one time. Our implementation can determine one output value per one computational step, which is faster than the standard parallel implementation whose strategy is to speed up each semi-group computations. We evaluate the performance of our implementation and verify its speedup.
抄録:

英語フィールド

Author:
Miyazaki, Makoto; Matsumae, Susumu
Title:
A pipeline implementation for dynamic programming on GPU
Announcement information:
Proceedings - 2018 6th International Symposium on Computing and Networking Workshops, CANDARW 2018 Page: 305 - 309
An abstract:
© 2018 IEEE. In this paper, we show the effectiveness of a pipeline implementation of Dynamic Programming (DP) on GPU. As an example, we parallelize a typical DP program where each element of its solution table is calculated in order by semigroup computations among some already computed elements in the table. We implement the DP program on GPU in a pipeline fashion, i.e., we use GPU cores for supporting pipeline-stages so that many elements of the solution table are partially computed in parallel at one time. Our implementation can determine one output value per one computational step, which is faster than the standard parallel implementation whose strategy is to speed up each semi-group computations. We evaluate the performance of our implementation and verify its speedup.


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