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Scalable Molecular Dynamics with NAMD

物件導向軟體之架構(Object-Oriented Software Construction)探討

A large number of MPI implementations are currently available, each of which emphasize different aspects of high-performance computing or are intended to solve a specific research problem. The result is a myriad of incompatible MPI implementations, all of which require separate installation, and the combination of which present significant logistical challenges for end users. Building upon prior research, and influenced by experience gained from the code bases of the LAM/MPI, LA-MPI, and FT-MPI projects, Open MPI is an all-new, production-quality MPI-2 implementation that is fundamentally centered around component concepts. Open MPI provides a unique combination of novel features previously unavailable in an open-source, production-quality implementation of MPI. Its component architecture provides both a stable platform for third-party research as well as enabling the run-time composition of independent software add-ons. This paper presents a high-level overview the goals, design, and implementation of Open MPI.

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Open MPI: Goals, Concept, and Design of a Next Generation MPI Implementation

[서평]「Component Software」 - Beyond Object-Oriented Programming -

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 instrmentation and measurement, and how effectively they are integrated and composed This paper presents the TAU (Tuning and Analysis Utilities) parallel performance sytem and describe how it addresses diverse requirements for performance observation and analysis

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The Tau Parallel Performance System

Parallel programs are complex and often require a multilevel debugging strategy that combines both eventand state-based debugging. We report here on preliminary work that combines these approaches within the TAU program analysis environment for pC++. This work extends the use of event-based modeling to object-parallel languages, provides an alternative mechanism for establishing meaningful global breakpoints in object-oriented languages, introduces the TAU program interaction and control infrastructure, and provides an environment for the assessment of mixed eventand state-based strategies.

Event and state-based debugging in TAU: a prototype

Modern parallel performance measurement systems collect performance information either through probes inserted in the application code or via statistical sampling. Probe-based techniques measure performance metrics directly using calls to a measurement library that execute as part of the application. In contrast, sampling-based systems interrupt program execution to sample metrics for statistical analysis of performance. Although both measurement approaches are represented by robust tool frameworks in the performance community, each has its strengths and weaknesses. In this paper, we investigate the creation of a hybrid measurement system, the goal being to exploit the strengths of both systems and mitigate their weaknesses. We show how such a system can be used to provide the application programmer with a more complete analysis of their application. Simple example and application codes are used to demonstrate its capabilities. We also show how the hybrid techniques can be combined to provide real cross-language performance evaluation of an uninstrumented run for mixed compiled/interpreted execution environments (e.g., Python and C/C++/Fortran).

Design and Implementation of a Hybrid Parallel Performance Measurement System

In this paper we give an overview of SCALEA, which is a new performance analysis tool for OpenMP, MPI, HPF, and mixed parallel/distributed programs. SCALEA instruments, executes and measures programs and computes a variety of performance overheads based on a novel overhead classification. Source code and HW-profiling is combined in a single system which significantly extends the scope of possible overheads that can be measured and examined, ranging from HW-counters, such as the number of cache misses or floating point operations, to more complex performance metrics, such as control or loss of parallelism. Moreover, SCALEA uses a new representation of code regions, called the dynamic code region call graph, which enables detailed overhead analysis for arbitrary code regions. An instrumentation description file is used to relate performance information to code regions of the input program and to reduce instrumentation overhead. Several experiments with realistic codes that cover MPI, OpenMP, HPF, and mixed OpenMP/MPI codes demonstrate the usefulness of SCALEA.

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On Using SCALEA for Performance Analysis of Distributed and Parallel Programs

As computer systems grow in size and complexity, tool support is needed to facilitate the efficient mapping of large-scale applications onto these systems. To help achieve this mapping, performance analysis tools must provide robust performance observation capabilities at all levels of the system, as well as map low-level behavior to high-level program constructs. Instrumentation and measurement strategies, developed over the last several years, must evolve together with performance analysis infrastructure to address the challenges of new scalable parallel systems.

Performance instrumentation and measurement for terascale systems

Measurement-based profiling introduces intrusion in program execution. Intrusion effects can be mitigated by compensating for measurement overhead. Techniques for compensation analysis in performance profiling are presented and their implementation in the TAU performance system described. Experimental results on the NAS parallel benchmarks demonstrate that overhead compensation can be effective in improving the accuracy of performance profiling. However, parallel time profiling requires the execution delay introduced by measurement on individual processes to be communicated between processes when they interact. Parallel execution scenarios are given to model the effects and to determine the analysis procedures to be applied online.

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Sameer Shende

Papers

Event and state-based debugging in TAU: a prototype

Design and Implementation of a Hybrid Parallel Performance Measurement System

On Using SCALEA for Performance Analysis of Distributed and Parallel Programs

Performance instrumentation and measurement for terascale systems

Overhead compensation in performance profiling