- 著者
- Sayaka Akioka Yuki Ohno Midori Sugaya Tatsuo Nakajima
- 出版者
- Information and Media Technologies 編集運営会議
- 雑誌
- Information and Media Technologies (ISSN:18810896)
- 巻号頁・発行日
- vol.6, no.4, pp.1149-1157, 2011 (Released:2011-12-15)
- 参考文献数
- 24
This paper proposes SPLiT (Scalable Performance Library Tool) as the methodology to improve performance of applications on multicore processors through CPU and cache optimizations on the fly. SPLiT is designed to relieve the difficulty of the performance optimization of parallel applications on multicore processors. Therefore, all programmers have to do to benefit from SPLiT is to add a few library calls to let SPLiT know which part of the application should be analyzed. This simple but compelling optimization library contributes to enrich pervasive servers on a multicore processor, which is a strong candidate for an architecture of information appliances in the near future. SPLiT analyzes and predicts application behaviors based on CPU cycle counts and cache misses. According to the analysis and predictions, SPLiT tries to allocate processes and threads sharing data onto the same physical cores in order to enhance cache efficiency. SPLiT also tries to separate cache effective codes from the codes with more cache misses for the purpose of the avoidance of cache pollutions, which result in performance degradation. Empirical experiments assuming web applications validated the efficiency of SPLiT and the performance of the web application is improved by 26%.
- 著者
- Sayaka Akioka Yuki Ohno Midori Sugaya Tatsuo Nakajima
- 出版者
- 一般社団法人 情報処理学会
- 雑誌
- Journal of Information Processing (ISSN:18826652)
- 巻号頁・発行日
- vol.19, pp.421-429, 2011 (Released:2011-08-10)
- 参考文献数
- 24
This paper proposes SPLiT (Scalable Performance Library Tool) as the methodology to improve performance of applications on multicore processors through CPU and cache optimizations on the fly. SPLiT is designed to relieve the difficulty of the performance optimization of parallel applications on multicore processors. Therefore, all programmers have to do to benefit from SPLiT is to add a few library calls to let SPLiT know which part of the application should be analyzed. This simple but compelling optimization library contributes to enrich pervasive servers on a multicore processor, which is a strong candidate for an architecture of information appliances in the near future. SPLiT analyzes and predicts application behaviors based on CPU cycle counts and cache misses. According to the analysis and predictions, SPLiT tries to allocate processes and threads sharing data onto the same physical cores in order to enhance cache efficiency. SPLiT also tries to separate cache effective codes from the codes with more cache misses for the purpose of the avoidance of cache pollutions, which result in performance degradation. Empirical experiments assuming web applications validated the efficiency of SPLiT and the performance of the web application is improved by 26%.