著者

Satoshi Matsuoka Hideharu Amano Kengo Nakajima Koji Inoue Tomohiro Kudoh Naoya Maruyama Kenjiro Taura Takeshi Iwashita Takahiro Katagiri Toshihiro Hanawa Toshio Endo

雑誌

研究報告ハイパフォーマンスコンピューティング(HPC) (ISSN:21888841)

巻号頁・発行日

vol.2016-HPC-155, no.32, pp.1-14, 2016-08-01

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Slowdown and inevitable end in exponential scaling of processor performance, the end of the so-called “Moore's Law”is predicted to occur around 2025-2030 timeframe. Because CMOS semiconductor voltage is also approaching its limits, this means that logic transistor power will become constant, and as a result, the system FLOPS will cease to improve, resulting in serious consequences for IT in general, especially supercomputing. Existing attempts to overcome the end of Moore 's law are rather limited in their future outlook or applicability. We claim that data-oriented parameters, such as bandwidth and capacity, or BYTES, are the new parameters that will allow continued performance gains for periods even after computing performance or FLOPS ceases to improve, due to continued advances in storage device technologies and optics, and manufacturing technologies including 3-D packaging. Such transition from FLOPS to BYTES will lead to disruptive changes in the overall systems from applications, algorithms, software to architecture, as to what parameter to optimize for, in order to achieve continued performance growth over time. We are launching a new set of research efforts to investigate and devise new technologies to enable such disruptive changes from FLOPS to BYTES in the Post-Moore era, focusing on HPC, where there is extreme sensitivity to performance, and expect the results to disseminate to the rest of IT.

著者

Chaojie Zhang Koichi Shirahata Shuji Suzuki Yutaka Akiyama Satoshi Matsuoka

雑誌

研究報告計算機アーキテクチャ(ARC)

巻号頁・発行日

vol.2014-ARC-213, no.29, pp.1-7, 2014-12-02

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Homology search to be used in emerging bioinformatics problems such as metagenomics is of increasing importance and challenge as its application area grows more broadly while the computational complexity is increasing, thus requiring massive parallel data processing. Earlier work by some of the authors have devised novel algorithms such as GHOSTX, but the master-worker parallelization to enumerate and schedule for data processing was done with a privately developed, MPI-based master-worker framework called GHOST-MP. An alternative is to utilize the now-popular big data software substrates, such as MapReduce with abundant associated software tool-chains, but it is not clear whether the massive resource required by metagenomic homology search would not overwhelm its known limitations. By converting the GHOST-MP master-worker data processing pipeline to accommodate MapReduce, and benchmarking them on a variety of high-performance MapReduce incarnations including Hadoop and Spark, we attempt to characterize the appropriateness of MapReduce as a generic framework for metagenomics that embody extremely resource consuming requirements for both compute and data.

著者

Mateusz Bysiek Mohamed Wahib Aleksandr Drozd Satoshi Matsuoka

雑誌

研究報告ハイパフォーマンスコンピューティング(HPC) (ISSN:21888841)

巻号頁・発行日

vol.2018-HPC-165, no.38, pp.1-7, 2018-07-23

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We present a method for accelerating the execution of Python programs. We rely on just-in-time automatic code translation and compilation with Python itself being used as a high-level intermediate representation. We also employ performance-oriented code transformations and compiler directives to achieve high performance portability while enabling end users to keep their codebase in pure Python. To evaluate our method, we implement an open-source transpilation framework with an easy-to-use interface that achieves performance better than state-of-the-art methods for accelerating Python.