PICS - a Performance-analysis-based Introspective Control System to steer parallel applications

Sun, Yanhua

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https://hdl.handle.net/2142/78431 Copy

Description

Title

PICS - a Performance-analysis-based Introspective Control System to steer parallel applications

Author(s)

Sun, Yanhua

Issue Date

2015-04-23

Director of Research (if dissertation) or Advisor (if thesis)

Kale, Laxmikant V.

Doctoral Committee Chair(s)

Kale, Laxmikant V.

Committee Member(s)

• Gropp, William D.
• Kloeckner, Andreas
• Balaji, Pavan

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Date of Ingest

2015-07-22T22:17:12Z

Keyword(s)

• parallel computing
• control points
• runtime system
• application reconfiguration
• automatic performance analysis
• large scale analysis

Abstract

Parallel programming has always been difficult due to the complexity of hardware and the diversity of applications. Although significant progress has been achieved over the years, attaining high parallel efficiency on large supercomputers for various applications is still quite challenging. As we go beyond the current scale of computers to those with peak capacities of an ExaFLOP/s, it is clear that an introspective and adaptive runtime system (RTS) will be critical to reduce programmers' tuning efforts by automatically handling the complexities of applications and machines. This is the motivation for my research on a Performance-analysis-based Introspective Control System - PICS. PICS intelligently steers parallel applications and runtime system configurations to achieve desired goals by utilizing expert knowledge to analyze performance data and adaptively reconfiguring applications. This thesis designs a holistic introspective control system for automatic performance tuning that combines the real-time performance analysis and performance steering to effectively automate the optimization. A few techniques are explored to make the parallel runtime system and applications more adaptive and controllable. Control points are defined for applications to interact with PICS. Decision tree based automatic performance analysis is implemented to significantly reduce the search space of multiple configurations. Parallel evaluation and sampling techniques are exploited to reduce the overhead of the system and to improve its scalability. In addition, the result of automatic performance analysis can be visualized to help developers manually tune their applications. The utility of PICS is demonstrated with several benchmarks and real- world applications.

Graduation Semester

2015-5

Type of Resource

text

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http://hdl.handle.net/2142/78431

Copyright and License Information

Copyright 2015 Yanhua Sun

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