Fast classification of MPI applications using Lamport’s logical clocks.

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Bibliographic Details
Title: Fast classification of MPI applications using Lamport’s logical clocks.
Authors: Tong, Zhou1 tong@cs.fsu.edu, Pakin, Scott2 pakin@lanl.gov, Lang, Michael2 mlang@lanl.gov, Yuan, Xin1 xyuan@cs.fsu.edu
Source: Journal of Parallel & Distributed Computing. Oct2018, Vol. 120, p77-88. 12p.
Subjects: Logical clocks, Counters (Computer science), Distributed computing, Computer networks, Cloud computing
Abstract: We present a novel trace-based analysis tool that rapidly classifies an MPI application as bandwidth-bound, latency-bound, load-imbalance-bound, or computation-bound for different interconnection networks. The tool uses an extension of Lamport’s logical clock to track application progress in the trace replay. It has two unique features. First, it can predict application performance for many latency and bandwidth parameters from a single replay of the trace. Second, it infers the performance characteristics of an application and classifies the application using the predicted performance trend for a range of network configurations instead of using the predicted performance for a particular network configuration. We describe the techniques used in the tool and its design and implementation, and report our performance study of the tool and our experience with classifying twelve applications and mini-apps from the DOE DesignForward project as well as the NAS Parallel Benchmarks. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:We present a novel trace-based analysis tool that rapidly classifies an MPI application as bandwidth-bound, latency-bound, load-imbalance-bound, or computation-bound for different interconnection networks. The tool uses an extension of Lamport’s logical clock to track application progress in the trace replay. It has two unique features. First, it can predict application performance for many latency and bandwidth parameters from a single replay of the trace. Second, it infers the performance characteristics of an application and classifies the application using the predicted performance trend for a range of network configurations instead of using the predicted performance for a particular network configuration. We describe the techniques used in the tool and its design and implementation, and report our performance study of the tool and our experience with classifying twelve applications and mini-apps from the DOE DesignForward project as well as the NAS Parallel Benchmarks. [ABSTRACT FROM AUTHOR]
ISSN:07437315
DOI:10.1016/j.jpdc.2018.05.005