Faculty Profiles - YASUGI Masahiro

Papers - YASUGI Masahiro

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HOPE: A Parallel Execution Model Based on Hierarchical Omission Reviewed International journal

Masahiro Yasugi, Daisuke Muraoka, Tasuku Hiraishi, Seiji Umatani, Kento Emoto

Proceedings of the 48th International Conference on Parallel Processing (ICPP 2019) 77:1 - 77:11 2019.08

A Coherent Random Number Utilization Scheme for a Fault-tolerant Parallel Execution Model Based on Hierarchical Omission Reviewed

Shogo Hiromatsu, Masahiro Yasugi, Tasuku Hiraishi, Kento Emoto

Journal of Information Processing ( Information Processing Society of Japan ) 33 ( 0 ) 295 - 311 2025.04

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal)

High productivity, scalability, load balancing, and fault tolerance are all important issues of massively parallel computing. A parallel execution model called HOPE, which we are proposing, addresses these issues by “hierarchical omission of redundant computations”. Every HOPE worker performs the entire divide-and-conquer computation with its own planned order, whereas it can omit subcomputations whose results are obtained from other workers at runtime, achieving fault tolerance and parallel efficiency as a team of workers. In most random number generation algorithms, a random number is generated on the basis of the internal state after the previous random number generation. Therefore, simple reordering does not preserve coherent results/conditions among workers based on the properties of utilized random numbers. In order to always use the same sequence for each hierarchical subcomputation, this study explores coherent random number utilization schemes, using Sobol' sequences or the PCG random number generator, which allow generation starting from the latter part by skipping the first part. In addition, the Monte Carlo method is an important application of random numbers. In this study, we examine a HOPE application that computes highly dimensional numerical integrals for pricing securities known as MBS (mortgage-backed securities) using the Monte Carlo method, where the convergence speed of integration errors is independent of dimensionality.

DOI： 10.2197/ipsjjip.33.295

Designing Restartable Exception Handling Mechanisms for Implementing Efficient and Safe High-level Languages Reviewed

Masahiro Yasugi, Kento Emoto, Tasuku Hiraishi

Journal of Information Processing ( Information Processing Society of Japan ) 32 ( 0 ) 436 - 450 2024.05

Authorship：Lead author,　Corresponding author Language：English Publishing type：Research paper (scientific journal)

Mechanisms for legitimate execution stack access (LESA for short) provide legitimate access to values of callers' variables sleeping deeply in a C-like execution stack. LESA mechanisms enable efficient implementations of high-level services that require dynamic rearrangement of running software, such as garbage collection, first-class continuations, and dynamic load balancing, for implementing efficient and safe high-level languages. As a nested-function-style LESA mechanism, we can create a closure from a nested function definition and indirectly call the nested function via a pointer to the closure, achieving access to values of the variables captured in the closure-creation-time environment. In this study, we propose restartable exception handling mechanisms as new LESA mechanisms, which provide callable exception handlers without non-local exiting; handlers are found on the basis of dynamic scope without requiring pointers. In addition, the LESA mechanisms can be used at multiple levels; e.g., garbage collection can be started during the creation of a first-class continuation. In this paper, we design core languages that feature leveled restartable exception handling mechanisms and discuss their properties and implementations.

DOI： 10.2197/ipsjjip.32.436

Scopus

CiNii Research

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195605010&origin=inward

Portable Implementations of Work Stealing Reviewed International journal

Masahiro Yasugi, Tasuku Hiraishi, Chihiro Takeuchi

Proceedings of International Conference on High Performance Computing in Asia-Pacific Region (HPCAsia 2024) 12 - 22 2024.01

Authorship：Lead author,　Corresponding author Language：English Publishing type：Research paper (international conference proceedings)

Japan Nagoya 2024.01.25 - 2024.01.27

DOI： 10.1145/3635035.3635041

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85184360040&origin=inward

Evaluating and Analyzing Irregular Tree Search in the Tascell and HOPE Parallel Programming Languages Reviewed International journal

Yusuke Shiki, Masahiro Yasugi, Tasuku Hiraishi

Post-proceedings of the 23rd International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT 2022) 13798 LNCS 262 - 272 2023.04

Construction of Hierarchical Matrix on Distributed Memory Systems using a Task Parallel Language Reviewed International journal

Zhengyang Bai, Tasuku Hiraishi, Akihiro Ida, Masahiro Yasugi, Keiichiro Fukazawa

Proceedings of the 10th International Symposium on Computing and Networking Workshops (CANDARW 2022) (10th International Workshop on Computer Systems and Architectures CSA 2022, held in conjunction with CANDAR 2022) 48 - 54 2023.03

Parallelization of Matrix Partitioning in Hierarchical Matrix Construction on Distributed Memory Systems Reviewed

Zhengyang Bai, Tasuku Hiraishi, Akihiro Ida, Masahiro Yasugi

Journal of Information Processing ( Information Processing Society of Japan ) 30 ( 0 ) 742 - 754 2022.10

Language：English Publishing type：Research paper (scientific journal)

A hierarchical matrix (H-matrix) is an approximated form that represents N × N correlations of N objects. H-matrix construction is achieved by dividing a matrix into submatrices (partitioning), followed by calculating these submatrices' element values (filling). Matrix partitioning consists of two steps: cluster tree (CT) construction, where objects are divided into clusters hierarchically; and block cluster tree (BCT) construction, which involves observing all cluster pairs at the same CT level that satisfies the admissibility condition. This study proposes two parallel implementation methods of partitioning operations on distributed memory systems (DMSs): distributed cluster tree construction (DCTC) and redundant cluster tree construction (RCTC). In DCTC, both CT and BCT constructions are parallelized using workers in all computing nodes. In RCTC, CT is constructed in every computing node redundantly by employing only intra-node work stealing. The BCT is then constructed in parallel using workers in all computing nodes. RCTC cannot achieve speedup using multiple computing nodes, but can eliminate the data exchange cost incurred by DCTC. We used the task-parallel language Tascell, which employs both intra- and inter-node work stealing, to handle arbitrary unbalanced tree construction and traversal on DMSs. Our RCTC implementations achieved a 1.11-1.20-fold speedup using up to 8 nodes × 36 workers in numerical experiments with 3D electric field analyses and N ≃ 108.

DOI： 10.2197/ipsjjip.30.742

Scopus

CiNii Research

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85139909867&origin=inward

Work-stealing Strategies That Consider Work Amount and Hierarchy Reviewed

Ryusuke Nakashima, Masahiro Yasugi, Hiroshi Yoritaka, Tasuku Hiraishi, Seiji Umatani

Journal of Information Processing ( Information Processing Society of Japan ) 29 478 - 489 2021.07

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal)

This paper proposes work-stealing strategies for an idle worker (thief) to select a victim worker. These strategies avoid small tasks being stolen to reduce the total task-division cost. We implemented these strategies on a work-stealing framework called Tascell. First, we propose new types of priority- and weight-based steal strategies. Programmers can let each worker estimate and declare, as a real number, the amount of remaining work required to complete its current task so that declared values are used as "priorities" or "weights". With a priority-based strategy, a thief selects the victim that has the highest known priority at that time. With a weight-based non-uniformly random strategy, a thief uses the relative weights of victim candidates as their selection probabilities. Second, we propose work-stealing strategies to alleviate excessive intra-node work stealing and excessive "steal backs" (or leapfroggings); for example, we allow workers to steal tasks from external nodes with some frequency even if work remains inside the current node. Our evaluation uses a parallel implementation of the "highly serial" version of the Barnes-Hut force-calculation algorithm in a shared memory environment and five benchmark programs in a distributed memory environment.

DOI： 10.2197/ipsjjip.29.478

CiNii Article

Other Link： https://ci.nii.ac.jp/naid/130008065250

Parallelization of Matrix Partitioning in Construction of Hierarchical Matrices using Task Parallel Languages Reviewed

Zhengyang Bai, Tasuku Hiraishi, Hiroshi Nakashima, Akihiro Ida, Masahiro Yasugi

Journal of Information Processing ( Information Processing Society of Japan ) 27 840 - 851 2019.12

Language：English Publishing type：Research paper (scientific journal)

© 2019 Information Proessing Soiety of Japan. A hierarchical matrix (H-matrix) is an approximated form that represents N × N correlations of N objects. H-matrix construction is achieved by partitioning a matrix into submatrices, followed by calculating the element values of these submatrices. This paper proposes implementations of matrix partitioning using task parallel languages, Cilk Plus and Tascell. Matrix partitioning is divided into two steps: cluster tree construction by dividing objects into clusters hierarchically, and block cluster tree construction by observing all cluster pairs at the same level of the cluster tree that satisfy an admissibility condition. As the two types of trees constructed and traversed in these steps are un-predictably unbalanced, it is expected that we can efficiently parallelize both these steps using task parallel languages. To obtain sufficient parallelism in the cluster tree construction, we not only execute recursive calls in parallel but also parallelize the inside of each recursive step. For the block cluster tree construction, we assigned each worker its own space so that the workers can store the cluster pairs without using locks. As a result, compared to a sequential implementation in C, we achieved up to an 11.5-fold speedup using Cilk Plus and a 12.6-fold speedup by Tascell for the cluster tree construction. For the block cluster tree construction, up to a 37.7-fold speedup by Cilk Plus and a 38.8-fold speedup using Tascell are achieved. In regard to the entire process of matrix partitioning, we achieved up to a 12.2-fold speedup by Cilk Plus and a 14.5-fold speedup using Tascell. All experiments were executed on two 18-core Xeon processors with real datasets to generate coefficient matrices used in the surface charge method.

DOI： 10.2197/ipsjjip.27.840

Scopus

CiNii Article

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85078518192&origin=inward

Extending a Work-Stealing Framework with Priorities and Weights Reviewed International journal

Ryusuke Nakashima, Hiroshi Yoritaka, Masahiro Yasugi, Tasuku Hiraishi, Seiji Umatani

Proceedings of the 9th Workshop on Irregular Applications: Architectures and Algorithms (IA3 2019) (held in conjunction with SC 2019) 9 - 16 2019.11

Evaluating Portable Mechanisms for Legitimate Execution Stack Access with a Scheme Interpreter in an Extended SC Language Reviewed

Masahiro Yasugi, Reichi Ikeuchi, Tasuku Hiraishi, Tsuneyasu Komiya

Journal of Information Processing 27 177 - 189 2019.02

Probabilistic guards: A mechanism for increasing the granularity of work-stealing programs Reviewed

Hiroshi Yoritaka, Ken Matsui, Masahiro Yasugi, Tasuku Hiraishi, Seiji Umatani

Parallel Computing 82 19 - 36 (Available online 22 June 2018) 2019.02

Parallelization of Extracting Connected Subgraphs with Common Itemsets in Distributed Memory Environments Reviewed

Shingo Okuno, Tasuku Hiraishi, Hiroshi Nakashima, Masahiro Yasugi, Jun Sese

Journal of Information Processing 25 ( 0 ) 256 - 267 2017.02

Evaluation of an MPI-Based Implementation of the Tascell Task-Parallel Language on Massively Parallel Systems Reviewed

Daisuke Muraoka, Masahiro Yasugi, Tasuku Hiraishi, Seiji Umatani

Proceedings of the 45th International Conference on Parallel Processing Workshops (ICPPW 2016) (Ninth International Workshop on Parallel Programming Models and Systems Software for High-End Computing P2S2 2016, held in conjunction with ICPP 2016) 161 - 170 2016.08

Extending a Work-Stealing Framework with Probabilistic Guards Reviewed

Hiroshi Yoritaka, Ken Matsui, Masahiro Yasugi, Tasuku Hiraishi, Seiji Umatani

Reducing Redundant Search in Parallel Graph Mining using Exceptions Reviewed

Shingo Okuno, Tasuku Hiraishi, Hiroshi Nakashima, Masahiro Yasugi, Jun Sese

Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW 2016) (21st International Workshop on High-Level Parallel Programming Models and Supportive Environments HIPS 2016, held in conjunction with IPDPS 2016) 328 - 337 2016.05

An Implementation of Exception Handling with Collateral Task Abortion Reviewed

Tasuku Hiraishi, Shingo Okuno, Masahiro Yasugi

Journal of Information Processing 24 ( 2 ) 2016.03

Design and Implementation of a Java Bytecode Manipulation Library for Clojure Reviewed

Seiji Umatani, Tomoharu Ugawa, Masahiro Yasugi

Journal of Information Processing 23 ( 5 ) 716 - 729 2015.09

Parallelization of Extracting Connected Subgraphs with Common Itemsets Reviewed

Shingo Okuno, Tasuku Hiraishi, Hiroshi Nakashima, Masahiro Yasugi, Jun Sese

IPSJ Transactions on Programming 7 ( 3 ) 22 - 39 2014.07

L-Closureの呼び出しコストの削減 Reviewed

田附正充, 八杉昌宏, 平石拓, 馬谷誠二

情報処理学会論文誌　プログラミング 6 ( 2 ) 13 - 32 2013.08

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Papers - YASUGI Masahiro