/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

/pdf/the-tau-parallel-performance-system-3fr3lax14z.pdf

The Tau Parallel Performance System

Workload characterization is an important technique that helps us understand the performance of parallel applications and the demands they place on the system It can be used to describe performance effects due to application parameters, compiler options, and platform configurations In this paper, workload characterization features in the TAU parallel performance system are demonstrated for elucidating the performance of the MPI library based on the sizes of messages Such characterization partitions the time spent in the MPI routines used by an application based on the type of MPI operation and the message size involved It requires a two-level mapping of performance data, a unique feature implemented in TAU Results from the NPB LU benchmark are presented We also discuss the use of mapping for memory consumption characterization

/pdf/workload-characterization-using-the-tau-performance-system-1zg0aflmwz.pdf

Workload characterization using the TAU performance system

Performance evaluation tools play an important role in helping understand application performance, diagnose performance problems and guide tuning decisions on modern HPC systems. Tools to observe parallel performance must evolve to keep pace with the ever-increasing complexity of these systems. In this paper, we describe our experience in building novel tools and techniques in the TAU Performance System® to observe application performance effectively and efficiently at scale. It describes the extensions to TAU to contend with large data volumes associated with increasing core counts. These changes include new instrumentation choices, efficient handling of disk I/O operations in the measurement layer, and strategies for visualization of performance data at scale in TAU's analysis layer, among others. We also describe some techniques that allow us to fully characterize the performance of applications running on hundreds of thousands of cores.

Improving the scalability of performance evaluation tools

Fault-tolerance has always been an important topic when it comes to running massively parallel programs at scale. Statistically, hardware and software failures are expected to occur more often on systems gathering millions of computing units. Moreover, the larger jobs are, the more computing hours would be wasted by a crash. In this paper, we describe the work done in our MPI runtime to enable both transparent and application-level checkpointing mechanisms. Unlike the MPI 4.0 User-Level Failure Mitigation (ULFM) interface, our work targets solely Checkpoint/Restart and ignores other features such as resiliency. We show how existing checkpointing methods can be practically applied to a thread-based MPI implementation given sufficient runtime collaboration. The two main contributions are the preservation of high-speed network performance during transparent C/R and the over-subscription of checkpoint data replication thanks to a dedicated user-level scheduler support. These techniques are measured on MPI benchmarks such as IMB, Lulesh and Heatdis, and associated overhead and trade-offs are discussed.

Checkpoint/restart approaches for a thread-based MPI runtime

Parallel Java environments present challenging problems for performance tools because of Java’s rich language system and its multi-level execution platform combined with the integration of native-code application libraries and parallel runtime software. In addition to the desire to provide robust performance measurement and analysis capabilities for the Java language itself, the coupling of different software execution contexts under a uniform performance model needs careful consideration of how events of interest are observed and how cross-context parallel execution information is linked. This paper relates our experience in extending the TAU performance system to a parallel Java environment based on mpiJava. We describe the instrumentation model used, how performance measurements are made, and the overhead incurred. A parallel Java application simulating the game of life is used to show the performance system’s capabilities.

Performance Tools for Parallel Java Environments

The influences of OS and system-specific effects on application performance are increasingly important in high performance computing. In this regard, OS kernel measurement is necessary to understand the interrelationship of system and application behavior. This can be viewed from two perspectives: kernel-wide and process-centric. An integrated methodology and framework to observe both views in HPC systems using OS kernel measurement has remained elusive. We demonstrate a new tool called KTAU (Kernel TAU) that aims to provide parallel kernel performance measurement from both perspectives. KTAU extends the TAU performance system with kernel-level monitoring, while leveraging TAU's measurement and analysis capabilities. As part of the ZeptoOS scalable operating systems project, we report early experiences using KTAU in ZeptoOS on the IBM BG/L system.

/pdf/early-experiences-with-ktau-on-the-ibm-bg-l-130ict856m.pdf

Sameer Shende

Papers

Workload characterization using the TAU performance system

Improving the scalability of performance evaluation tools

Checkpoint/restart approaches for a thread-based MPI runtime

Performance Tools for Parallel Java Environments

Early experiences with KTAU on the IBM BG/L