| アイテムタイプ |
デフォルトアイテムタイプ（フル）その２(1) |
| 公開日 |
2022-12-07 |
| タイトル |
|
|
タイトル |
A Case for Term Weighting using a Dictionary on GPUs |
|
言語 |
en |
| 作成者 |
Wakatsuki, Toshiaki
Keyaki, Atsushi
Miyazaki, Jun
Miyazaki, Jun
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
GPGPU |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
term weighting |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
dictionary |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
parallel primitive |
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
This paper demonstrates a fast Okapi's BM25 term weighting method on GPUs for information retrieval by combining a GPU-based dictionary using a succinct data structure and data parallel primitives. The problem of handling documents on GPUs is to processing variable length strings such as a document itself and a word. Processing variable size of data causes many idle cores, i.e., load imbalances among threads, due to the SIMD nature of GPU architecture. Our term weighting method is carefully composed of efficient data parallel primitives to avoid load imbalance. Additionally, we implemented a haigh performance compressed dictionary on GPUs. By using this dictionary, words are converted into IDs so that costly string comparisons can be avoided. Our experimental results revealed that the proposed term weighting method on GPUs performs up to 5x faster than the MapReduce-based one on multi-core CPUs. |
|
言語 |
en |
| 日付 |
|
|
日付 |
2017-08-02 |
|
日付タイプ |
Issued |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_3248 |
|
資源タイプ |
book part |
| 出版タイプ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 関連情報 |
|
|
関連タイプ |
isPartOf |
|
|
関連名称 |
Lecture Notes in Computer Science book series (LNISA,volume 10439) |
| ページ数 |
|
|
ページ数 |
17 |
0602200101.pdf (317.0 KB)
