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73
The Tau Parallel Performance System
- The International Journal of High Performance Computing Applications
, 2006
"... The ability of performance technology to keep pace with the growing complexity of parallel and distributed systems depends on robust performance frameworks that can at once provide system-specific performance capabilities and support high-level performance problem solving. Flexibility and portabilit ..."
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Cited by 242 (21 self)
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The ability of performance technology to keep pace with the growing complexity of parallel and distributed systems depends on robust performance frameworks that can at once provide system-specific performance capabilities and support high-level performance problem solving. Flexibility and portability in empirical methods and processes are influenced primarily by the strategies available for instrumentation and measurement, and how effectively they are integrated and composed. This paper presents the TAU (Tuning and Analysis Utilities) parallel performance system and describe how it addresses diverse requirements for performance observation and analysis.
Vertical Profiling: Understanding the Behavior of Object-Oriented Applications
"... Object-oriented programming languages provide a rich set of features that provide significant software engineering benefits. The increased productivity provided by these features comes at a justifiable cost in a more sophisticated runtime system whose responsibility is to implement these features e# ..."
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Cited by 71 (14 self)
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Object-oriented programming languages provide a rich set of features that provide significant software engineering benefits. The increased productivity provided by these features comes at a justifiable cost in a more sophisticated runtime system whose responsibility is to implement these features e#ciently. However, the virtualization introduced by this sophistication provides a significant challenge to understanding complete system performance, not found in traditionally compiled languages, such as C or C++. Thus, understanding system performance of such a system requires profiling that spans all levels of the execution stack, such as the hardware, operating system, virtual machine, and application.
Using Hardware Performance Monitors to Understand the Behavior of Java Applications
- IN PROC. OF THE THIRD USENIX VIRTUAL MACHINE RESEARCH AND TECHNOLOGY SYMP
, 2004
"... Modern Java programs, such as middleware and application servers, include many complex software components. Improving the performance of these Java applications requires a better understanding of the interactions between the application, virtual machine, operating system, and architecture. Hardware ..."
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Cited by 51 (8 self)
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Modern Java programs, such as middleware and application servers, include many complex software components. Improving the performance of these Java applications requires a better understanding of the interactions between the application, virtual machine, operating system, and architecture. Hardware performance monitors, which are available on most modern processors, provide facilities to obtain detailed performance measurements of long-running applications in real time. However, interpreting the data collected using hardware performance monitors is difficult because of the low-level nature of the data. We have
The SCALASCA performance toolset architecture
- In International Workshop on Scalable Tools for High-End Computing (STHEC
, 2008
"... www.scalasca.org SCALASCA is a performance toolset that has been specifically designed to analyze parallel application execution behavior on large-scale systems. It offers an incremental performanceanalysis procedure that integrates runtime summaries with in-depth studies of concurrent behavior via ..."
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Cited by 48 (8 self)
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www.scalasca.org SCALASCA is a performance toolset that has been specifically designed to analyze parallel application execution behavior on large-scale systems. It offers an incremental performanceanalysis procedure that integrates runtime summaries with in-depth studies of concurrent behavior via event tracing, adopting a strategy of successively refined measurement configurations. Distinctive features are its ability to identify wait states in applications with very large numbers of processes and combine these with efficiently summarized local measurements. In this article, we review the current toolset architecture, emphasizing its scalable design and the role of the different components in transforming raw measurement data into knowledge of application execution behavior. The scalability and effectiveness of SCALASCA are then surveyed from experience measuring and analyzing real-world applications on a range of computer systems. 1
Understanding and detecting real-world performance bugs
- in PLDI
, 2012
"... Developers frequently use inefficient code sequences that could be fixed by simple patches. These inefficient code sequences can cause significant performance degradation and resource waste, referred to as performance bugs. Meager increases in single threaded performance in the multi-core era and in ..."
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Cited by 45 (7 self)
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Developers frequently use inefficient code sequences that could be fixed by simple patches. These inefficient code sequences can cause significant performance degradation and resource waste, referred to as performance bugs. Meager increases in single threaded performance in the multi-core era and increasing emphasis on energy efficiency call for more effort in tackling performance bugs. This paper conducts a comprehensive study of 109 real-world performance bugs that are randomly sampled from five representative software suites (Apache, Chrome, GCC, Mozilla, and MySQL). The findings of this study provide guidance for future work to avoid, expose, detect, and fix performance bugs. Guided by our characteristics study, efficiency rules are extracted from 25 patches and are used to detect performance bugs. 332 previously unknown performance problems are found in the latest versions of MySQL, Apache, and Mozilla applications, including 219 performance problems found by applying rules across applications.
An Algebra for Cross-Experiment Performance Analysis
- In Proc. of the International Conference on Parallel Processing (ICPP
, 2004
"... Performance tuning of parallel applications usually involves multiple experiments to compare the effects of different optimization strategies. This article describes an algebra that can be used to compare, integrate, and summarize performance data from multiple sources. The algebra consists of a dat ..."
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Cited by 42 (22 self)
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Performance tuning of parallel applications usually involves multiple experiments to compare the effects of different optimization strategies. This article describes an algebra that can be used to compare, integrate, and summarize performance data from multiple sources. The algebra consists of a data model to represent the data in a platformindependent fashion plus arithmetic operations to merge, subtract, and average the data from different experiments. A distinctive feature of this approach is its closure property, which allows processing and viewing all instances of the data model in the same way- regardless of whether they represent original or derived data- in addition to an arbitrary and easy composition of operations.
PerfExplorer: A Performance Data Mining Framework for Large-Scale Parallel Computing
- In Proceedings of SC 2005 conference, ACM
, 2005
"... Parallel applications running on high-end computer systems manifest a complexity of performance phenomena. Tools to observe parallel performance attempt to capture these phenomena in measurement datasets rich with information relating multiple performance metrics to execution dynamics and parameters ..."
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Cited by 37 (10 self)
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Parallel applications running on high-end computer systems manifest a complexity of performance phenomena. Tools to observe parallel performance attempt to capture these phenomena in measurement datasets rich with information relating multiple performance metrics to execution dynamics and parameters specific to the application-system experiment. However, the potential size of datasets and the need to assimilate results from multiple experiments makes it a daunting challenge to not only process the information, but discover and understand performance insights. In this paper, we present PerfExplorer, a framework for parallel performance data mining and knowledge discovery. The framework architecture enables the development and integration of data mining operations that will be applied to large-scale parallel performance profiles. PerfExplorer operates as a client-server system and is built on a robust parallel performance database (PerfDMF) to access the parallel profiles and save its analysis results. Examples are given demonstrating these techniques for performance analysis of ASCI applications. 1.
Design and Implementation of a Parallel Performance Data Management Framework
- IN: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON PARALLEL COMPUTING
, 2005
"... Empirical performance evaluation of parallel systems and applications can generate significant amounts of performance data and analysis results from multiple experiments as performance is investigated and problems diagnosed. Hence, the management of performance information is a core component of per ..."
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Cited by 36 (16 self)
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Empirical performance evaluation of parallel systems and applications can generate significant amounts of performance data and analysis results from multiple experiments as performance is investigated and problems diagnosed. Hence, the management of performance information is a core component of performance analysis tools. To better support tool integration, portability, and reuse, there is a strong motivation to develop performance data management technology that can provide a common foundation for performance data storage, access, merging, and analysis. This paper presents the design and implementation of the Performance Data Management Framework (PerfDMF). PerfDMF addresses objectives of performance tool integration, interoperation, and reuse by providing common data storage, access, and analysis infrastructure for parallel performance profiles. PerfDMF includes an extensible parallel profile data schema and relational database schema, a profile query and analysis programming interface, and an extendible toolkit for profile import/export and standard analysis. We describe the PerfDMF objectives and architecture, give detailed explanation of the major components, and show examples of PerfDMF application.
An evaluation of global address space languages: Co-array Fortran and Unified Parallel C
- In Principles and Practice of Parallel Programming
, 2005
"... Co-array Fortran (CAF) and Unified Parallel C (UPC) are two emerging languages for single-program, multiple-data global address space programming. These languages boost programmer productivity by providing shared variables for communication instead of message passing. However, the performance of the ..."
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Cited by 34 (1 self)
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Co-array Fortran (CAF) and Unified Parallel C (UPC) are two emerging languages for single-program, multiple-data global address space programming. These languages boost programmer productivity by providing shared variables for communication instead of message passing. However, the performance of these emerging languages still has room for improvement. In this paper, we study the performance of
