/pdf/scalable-molecular-dynamics-with-namd-3j7xyc70g4.pdf

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

The ability of performance technology to keep pace with the growing	complexity of parallel and distributed systems will depend on robust performance frameworks that can at once provide system-specific performance	capabilities and support high-level performance problem solving.	The TAU system is offered as an example framework that meets these requirements. With a flexible, modular instrumentation and measurement system, and an open performance data and analysis environment, TAU can	target a range of complex performance scenarios. Examples are given showing the diversity of TAU application.

Performance Technology for Complex Parallel and Distributed Systems

The integration of scalable performance analysis in parallel development tools is difficult. The potential size of data sets and the need to compare results from multiple experiments presents a challenge to manage and process the information. Simply to characterize the performance of parallel applications running on potentially hundreds of thousands of processor cores requires new scalable analysis techniques. Furthermore, many exploratory analysis processes are repeatable and could be automated, but are now implemented as manual procedures. In this paper, we will discuss the current version of PerfExplorer, a performance analysis framework which provides dimension reduction, clustering and correlation analysis of individual trails of large dimensions, and can perform relative performance analysis between multiple application executions. PerfExplorer analysis processes can be captured in the form of Python scripts, automating what would otherwise be time-consuming tasks. We will give examples of large-scale analysis results, and discuss the future development of the framework, including the encoding and processing of expert performance rules, and the increasing use of performance metadata.

/pdf/knowledge-support-and-automation-for-performance-analysis-uv16x942uw.pdf

Knowledge support and automation for performance analysis with PerfExplorer 2.0

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.

/pdf/performance-instrumentation-and-measurement-for-terascale-4cgp162oij.pdf

Performance instrumentation and measurement for terascale systems

Extreme-scale computing requires a new perspective on the role of performance observation in the Exascale system software stack. Because of the anticipated high concurrency and dynamic operation in these systems, it is no longer reasonable to expect that a post-mortem performance measurement and analysis methodology will suffice. Rather, there is a strong need for performance observation that merges first-and third-person observation, in situ analysis, and introspection across stack layers that serves online dynamic feedback and adaptation. In this paper we describe the DOE-funded XPRESS project and the role of autonomic performance support in Exascale systems. XPRESS will build an integrated Exascale software stack (called OpenX) that supports the ParalleX execution model and is targeted towards future Exascale platforms. An initial version of an autonomic performance environment called APEX has been developed for OpenX using the current TAU performance technology and results are presented that highlight the challenges of highly integrative observation and runtime analysis.

/pdf/an-early-prototype-of-an-autonomic-performance-environment-3hbsr3moao.pdf

An early prototype of an autonomic performance environment for exascale

Performance profiling generates measurement overhead during parallel program execution. Measurement overhead, in turn, introduces intrusion in a program's runtime performance behavior. Intrusion can be mitigated by controlling instrumentation degree, allowing a tradeoff of accuracy for detail. Alternatively, the accuracy in profile results can be improved by reducing the intrusion error due to measurement overhead. Models for compensation of measurement overhead in parallel performance profiling are described. An approach based on rational reconstruction is used to understand properties of compensation solutions for different parallel scenarios. From this analysis, a general algorithm for on-the-fly overhead assessment and compensation is derived.

/pdf/compensation-of-measurement-overhead-in-parallel-performance-r2m26xh323.pdf

Sameer Shende

Papers

Performance Technology for Complex Parallel and Distributed Systems

Knowledge support and automation for performance analysis with PerfExplorer 2.0

Performance instrumentation and measurement for terascale systems

An early prototype of an autonomic performance environment for exascale

Compensation of Measurement Overhead in Parallel Performance Profiling