| Project/Area Number |
26540031
|
|---|
| Research Category |
Grant-in-Aid for Challenging Exploratory Research
|
| Allocation Type | Multi-year Fund |
|---|
| Research Field |
Software
|
|---|
| Research Institution | Chiba Institute of Technology (2017)
Institute of Physical and Chemical Research (2014-2016) |
|---|
Principal Investigator |
HASHIMOTO Masatomo 千葉工業大学, 人工知能・ソフトウェア技術研究センター, 上席研究員 (60357770)
|
|---|
| Project Period (FY) |
2014-04-01 – 2018-03-31
|
| Project Status |
Completed (Fiscal Year 2017)
|
| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2016: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2015: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2014: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
|
|---|
| Keywords | 高性能計算 / 性能チューニング / 最適化パターン推定 / 計算カーネル同定 / 目標実行効率推定 / プログラム構造理解支援 / プログラム解析 / 機械学習 / 計算カーネル分類 / 実行効率予測 / 大規模調査 / コード理解支援 / アプリケーション性能チューニング / 大規模科学技術計算 / 計算カーネル特定 / 最適化パターン予測 / ソースコード変更パターン同定 |
|---|
| Outline of Final Research Achievements |
Performance tuning is still a demanding manual task. To improve an application's efficiency, we have to identify its computational kernels, each of which is typically composed of one or more loops. Then various empirical attempts such as loop transformations are made. Thus, it is crucial to learn from the experience of performance tuning experts. As a proof-of-concept, we extracted various facts from performance tuning histories of a few real-world scientific applications, and then constructed a database of that facts. Based on the database, we constructed a few experimental predictive models for promising loop transformation patterns. We also explored a thousand computation-intensive applications to reveal the distribution of kernel classes, each of which is related to expected efficiency and specific tuning patterns. In addition, we constructed a binary classifier for identifying loop kernels and a multi-class classifier for predicting kernel classes.
|
