学位論文

@phdthesis{ytaguchi_dthesis_2018,
title = {データ保護システムの性能評価およびサイジング方式の研究},
author = {田口 雄一},
url = {http://doi.org/10.18952/00008800},
doi = {10.18952/00008800},
year = {2018},
date = {2018-09-28},
school = {情報システム学研究科 情報ネットワークシステム学専攻},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

@phdthesis{tnakajima_dthesis_2017,
title = {動画配信サービスのための軽量分散協調キャッシュ基盤の研究},
author = {中島拓真},
url = {http://id.nii.ac.jp/1438/00008559/},
year = {2017},
date = {2017-12-22},
school = {電気通信大学情報システム学研究科情報ネットワークシステム学専攻},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

@phdthesis{b_61,
title = {Design and Evaluation of an FPGA-based Query Accelerator for Data Streams},
author = {Yasin Oge},
url = {http://id.nii.ac.jp/1438/00000837/},
year = {2015},
date = {2015-12-31},
school = {電気通信大学情報システム学研究科情報ネットワークシステム学専攻},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

@phdthesis{b,
title = {A Fully Optical Ring Network-on-Chip with Static and Dynamic Wavelength Allocation},
author = {CISSE AHMADOU DIT ADI},
url = {http://id.nii.ac.jp/1438/00000930/},
year = {2013},
date = {2013-01-01},
school = {電気通信大学情報システム学研究科情報ネットワークシステム学専攻},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}

@phdthesis{dthesis09Yoshimi,
title = {A Study on Monte-Carlo Biochemical Simulation for Field-Programmable Gate Arrays},
author = {Masato Yoshimi},
url = {https://comp.lab.uec.ac.jp/wp-content/uploads/2013/08/yoshimi_phd_pub.pdf},
year = {2009},
date = {2009-03-23},
school = {Keio University},
abstract = {Since 1970s, many biologists and computer scientists have been studying various approach method- ologies to simulate chain chemical reactions inside living cells using computers. These studies were supported by rapid progress of computer performance and operational cost. Stochastic simulation method has become increasingly important for various study fields in the systems biology, which is a strategic research area that aims to analyze life processes as a system through simulations of biochemical models. Life processes often exhibit stochastic phenomena. Stochastic Biochemical Simulation Algorithm, which is abbreviated as SSA, is a method to calculate time changes of molec- ular numbers in a stochastic biochemical model. SSA requires vast number of repetitive computation, because it originates in a Monte-Carlo simulation methodology. Therefore, various algorithms have been developed to reduce the amount of calculation, and there are all many ongoing studies on the development of new computer system to achieve high-throughput.
Since 2000s, the baseline performance improvement procedure for microprocessor evolved from acceleration with high operational frequency to parallel operation using multi-core processor. Under such circumstance, a new type of computational systems is being focused for allocating a part of scientific operations to dedicated hardware in order to achieve both low-cost and high-performance. For example, Graphical Processing Units (GPUs) and other many-core processors in equipped in large-scale PC-clusters for the aim of reducing operational cost. Especially, a Field Programmable Gate Array is anticipated as an alternative for small- to middle-class PC-clusters with less than dozens of PCs, and various researches are now in progress. Dedicated systems with FPGAs allow high- speed or high-throughput computation by well-scheduled dataflow that fills up arithmetic pipelines solving the algorithm. However, just like other dedicated hardware, high-performance is achieved with FPGAs when algorithms have high loop-level parallelism, and conditional branches in the loop may degrade performance drastically. Performance improvement obtained by use of an FPGA greatly varies by the characteristics of target algorithm, because FPGAs have strict limitation of data size, dataflow and parallelism to achieve high throughput.
This work studies implementation and evaluation of two different SSAs on an FPGA, which aims to achieve high-performance compared to execution on microprocessors. The goal of this work is to clarify the relationship between algorithm, hardware architecture, and performance according to data size based on the evaluation results.
First SSA is called First Reaction Method (FRM). Processes in the algorithm is simple, and has high degree of loop- and data-level parallelism. Thus, the hardware design was fixed, and data flow was statically scheduled to enhance performance by consecutively injecting data into deep pipelines of floating point units. Arithmetic operations were configured faithfully to the original algorithm by single-precision floating-point data. We validated that the implemented hardware can treat small- scale models well. The design achieved more than 80-fold throughput compared to software execu- tion on Xeon 2.80 GHz, with large-scale biochemical systems for up to 1023 reactions.
Second SSA is called Next Reaction Method (NRM). The algorithm adopts two distinctive data structures: a binary tree in an Indexed Priority Que (IPQ) and a Dependency Graph (DG). They are used for improving computational efficiency over FRM for large-scale models, but processing time dynamically varies because of the nature of their data structures. Main concept of the design for executing NRM was to allow multi-thread execution. However, since it is highly difficult to achieve high throughput by naively exploiting data-level parallelism, the design adopted a data-driven methodology. The modules in implemented hardware are linked with an interconnection network. By modifying the network among modules, hardware design can be flexibly tuned to perform well on the target FPGA device. We found that the bus-based interconnection network achieves reasonable performance. It was evaluated with different number of threads, and through the result analysis we studied a methodology to reduce waiting time. The design achieved approximately 4.2 to 5.4 times higher throughput compared to execution on Core 2 Quad Q6600 2.40GHz.
And finally, the performance of two implementations for various size of biochemical model is discussed to lead to future development of scientific application using FPGAs.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}