Reproducing the Behavior of a Parallel Program by Using Dataflow Execution Models

Naohisa TAKAHASHI; Takeshi MIEI

Reproducing the Behavior of a Parallel Program by Using Dataflow Execution Models

Naohisa TAKAHASHI, Takeshi MIEI

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We present a general framework with which we can evaluate the flexibility and efficiency of various replay systems for parallel programs. In our approach, program monitoring is modeled by making a virtual dataflow program graph, referred to as a VDG, that includes all the instructions executed by the program. The behavior of the program replay is modeled on the parallel interpretation of a VDG based on two basic parallel execution models for dataflow program graphs: a data-driven model and a demand-driven model. Previous attempts to replay parallel programs, known as Instant Replay and P-Sequence, are also modeled as variations of the data-driven replay, i.e. the datadriven interpretation of a VDG. We show that the demand-driven replay, i.e. the demand-driven interpretation of a VDG, is more flexible in program replay than the data-driven replay since it allows better control of parallelism and a more selective replay. We also show that we can implement a demand-driven replay that requires almost the same amount of data to be saved during program monitoring as does the data-driven replay, and which eliminates any centralized bottleneck during program monitoring by optimizing the demand propagation and using an effective data structure.

Publication: IEICE TRANSACTIONS on Information Vol.E80-D No.4 pp.495-503

Publication Date: 1997/04/25

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Type of Manuscript: Special Section PAPER (Special Issue on Parallel and Distributed Supercomputing)

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Naohisa TAKAHASHI, Takeshi MIEI, "Reproducing the Behavior of a Parallel Program by Using Dataflow Execution Models" in IEICE TRANSACTIONS on Information, vol. E80-D, no. 4, pp. 495-503, April 1997, doi: .
Abstract: We present a general framework with which we can evaluate the flexibility and efficiency of various replay systems for parallel programs. In our approach, program monitoring is modeled by making a virtual dataflow program graph, referred to as a VDG, that includes all the instructions executed by the program. The behavior of the program replay is modeled on the parallel interpretation of a VDG based on two basic parallel execution models for dataflow program graphs: a data-driven model and a demand-driven model. Previous attempts to replay parallel programs, known as Instant Replay and P-Sequence, are also modeled as variations of the data-driven replay, i.e. the datadriven interpretation of a VDG. We show that the demand-driven replay, i.e. the demand-driven interpretation of a VDG, is more flexible in program replay than the data-driven replay since it allows better control of parallelism and a more selective replay. We also show that we can implement a demand-driven replay that requires almost the same amount of data to be saved during program monitoring as does the data-driven replay, and which eliminates any centralized bottleneck during program monitoring by optimizing the demand propagation and using an effective data structure.
URL: https://global.ieice.org/en_transactions/information/10.1587/e80-d_4_495/_p

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@ARTICLE{e80-d_4_495,
author={Naohisa TAKAHASHI, Takeshi MIEI, },
journal={IEICE TRANSACTIONS on Information},
title={Reproducing the Behavior of a Parallel Program by Using Dataflow Execution Models},
year={1997},
volume={E80-D},
number={4},
pages={495-503},
abstract={We present a general framework with which we can evaluate the flexibility and efficiency of various replay systems for parallel programs. In our approach, program monitoring is modeled by making a virtual dataflow program graph, referred to as a VDG, that includes all the instructions executed by the program. The behavior of the program replay is modeled on the parallel interpretation of a VDG based on two basic parallel execution models for dataflow program graphs: a data-driven model and a demand-driven model. Previous attempts to replay parallel programs, known as Instant Replay and P-Sequence, are also modeled as variations of the data-driven replay, i.e. the datadriven interpretation of a VDG. We show that the demand-driven replay, i.e. the demand-driven interpretation of a VDG, is more flexible in program replay than the data-driven replay since it allows better control of parallelism and a more selective replay. We also show that we can implement a demand-driven replay that requires almost the same amount of data to be saved during program monitoring as does the data-driven replay, and which eliminates any centralized bottleneck during program monitoring by optimizing the demand propagation and using an effective data structure.},
keywords={},
doi={},
ISSN={},
month={April},}

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TY - JOUR
TI - Reproducing the Behavior of a Parallel Program by Using Dataflow Execution Models
T2 - IEICE TRANSACTIONS on Information
SP - 495
EP - 503
AU - Naohisa TAKAHASHI
AU - Takeshi MIEI
PY - 1997
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E80-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 1997
AB - We present a general framework with which we can evaluate the flexibility and efficiency of various replay systems for parallel programs. In our approach, program monitoring is modeled by making a virtual dataflow program graph, referred to as a VDG, that includes all the instructions executed by the program. The behavior of the program replay is modeled on the parallel interpretation of a VDG based on two basic parallel execution models for dataflow program graphs: a data-driven model and a demand-driven model. Previous attempts to replay parallel programs, known as Instant Replay and P-Sequence, are also modeled as variations of the data-driven replay, i.e. the datadriven interpretation of a VDG. We show that the demand-driven replay, i.e. the demand-driven interpretation of a VDG, is more flexible in program replay than the data-driven replay since it allows better control of parallelism and a more selective replay. We also show that we can implement a demand-driven replay that requires almost the same amount of data to be saved during program monitoring as does the data-driven replay, and which eliminates any centralized bottleneck during program monitoring by optimizing the demand propagation and using an effective data structure.
ER -