Wyatt Spear

An experimental approach to performance measurement of heterogeneous parallel applications using CUDA

Heterogeneous parallel systems using GPU devices for application acceleration have garnered significant attention in the supercomputing community. However, to realize the full potential of GPU computing, application developers will require tools to measure and analyze accelerator performance with respect to the parallel execution as a whole. A performance measurement technology for the NVIDIA CUDA platform has been developed and integrated with the TAU parallel performance system. The design of the TAUcuda package is based on an experimental NVIDIA CUDA driver and associated runtime and device libraries. In any environment where the CUDA experimental driver is installed, TAUcuda can provide detailed performance information regarding the execution of GPU kernels and the interactions with the parallel program without any modification to the program source or executable code. The paper describes the TAUcuda technology and how it is integrated with the TAU measurement framework to provide integrated performance views. Various examples of TAUcuda use are presented, including CUDA SDK examples, a GPU version of the Linpack benchmark, and a scalable molecular dynamics application, NAMD.

An approach to creating performance visualizations in a parallel profile analysis tool

With increases in the scale of parallelism the dimensionality and complexity of parallel performance measurements has placed greater challenges on analysis tools. Performance visualization can assist in understanding performance properties and relationships. However, the creation of new visualizations in practice is not supported by existing parallel profiling tools. Users must work with presentation types provided by a tool and have limited means to change its design. Here we present an approach for creating new performance visualizations within an existing parallel profile analysis tool. The approach separates visual layout design from the underlying performance data model, making custom visualizations such as performance over system topologies straightforward to implement and adjust for various use cases.

A Holistic Approach for Performance Measurement and Analysis for Petascale Applications

Contemporary high-end Terascale and Petascale systems are composed of hundreds of thousands of commodity multi-core processors interconnected with high-speed custom networks. Performance characteristics of applications executing on these systems are a function of system hardware and software as well as workload parameters. Therefore, it has become increasingly challenging to measure, analyze and project performance using a single tool on these systems. In order to address these issues, we propose a methodology for performance measurement and analysis that is aware of applications and the underlying system hierarchies. On the application level, we measure cost distribution and runtime dependent values for different components of the underlying programming model. On the system front, we measure and analyze information gathered for unique system features, particularly shared components in the multi-core processors. We demonstrate our approach using a Petascale combustion application called S3D on two high-end Teraflops systems, Cray XT4 and IBM Blue Gene/P, using a combination of hardware performance monitoring, profiling and tracing tools.

Integrating TAU with eclipse: a performance analysis system in an integrated development environment

The Eclipse platform offers Integrated Development Environment support for a diverse and growing array of programming applications and languages. There is an increasing call for programming tools to support various development tasks from within Eclipse. This includes tools for testing and analyzing program performance. We describe the high-level synthesis of the Eclipse platform with the TAU parallel performance analysis system. By leveraging Eclipses modularity and extensibility with TAUs robust automated performance analysis mechanisms we produce an integrated, GUI controlled performance analysis system for Java, C/C++ and High Performance Computing development within Eclipse.

Observing Performance Dynamics Using Parallel Profile Snapshots

Performance analysis tools are only as useful as the data they collect. Not just accuracy of performance data, but accessibility, is necessary for performance analysis tools to be used to their full effect. The diversity of performance analysis and tuning problems calls for more flexible means of storing and representing performance data. The development and maintenance cycles of high performance programs, in particular, stand to benefit from exploration of and expansion of the means used to record and describe program execution behavior. We describe a means of representing program performance data via a time or event delineated series of performance profiles, or profile snapshots, implemented in the TAU performance analysis system. This includes an explanation of the profile snapshot format and means of snapshot analysis.

Making Performance Analysis and Tuning Part of the Software Development Cycle

Although there are a number of performance tools available to Department of Defense (DoD) users, the process of performance analysis and tuning has yet to become an integral part of the DoD software development cycle. Instead, performance analysis and tuning is the domain of a small number of experts who cannot possibly address all the codes that need attention. We believe the main reasons for this are a lack of knowledge about these tools, the real or perceived steep learning curve required to use them, and the absence of a centralized method that incorporates their use in the software development cycle. This paper presents ongoing efforts to enable a larger number of DoD High Performance Computing Modernization Program (HPCMP) users to benefit from available performance analysis tools by integrating them into the Eclipse Parallel Tools Platform (Eclipse/PTP), an integrated development environment for parallel programs.

Evolution of a Parallel Performance System

The TAU Performance System® is an integrated suite of tools for instrumentation, measurement, and analysis of parallel programs targeting large-scale, high-performance computing (HPC) platforms. Representing over fifteen calendar years and fifty person years of research and development effort, TAU’s driving concerns have been portability, flexibility, interoperability, and scalability. The result is a performance system which has evolved into a leading framework for parallel performance evaluation and problem solving. This paper presents the current state of TAU, overviews the design and function of TAU’s main features, discusses best practices of TAU use, and outlines future development.

The Eclipse parallel tools platform: toward an integrated development environment for XSEDE resources

Eclipse [1] is a widely used, open source integrated development environment that includes support for C, C++, Fortran, and Python. The Parallel Tools Platform (PTP) [2] extends Eclipse to support development on high performance computers. PTP allows the user to run Eclipse on her laptop, while the code is compiled, run, debugged, and profiled on a remote HPC system. PTP provides development assistance for MPI, OpenMP, and UPC; it allows users to submit jobs to the remote batch system and monitor the job queue. It also provides a visual parallel debugger. The XSEDE community comprises a large part of PTPs user base, and we are actively working to make PTP a productive, easy-to-use development environment for the full breadth of XSEDE resources. In this paper, we will describe capabilities we have recently added to PTP to better support XSEDE resources. These capabilities include submission and monitoring of jobs on systems running Sun/Oracle Grid Engine, support for GSI authentication and MyProxy logon, support for environment modules, and integration with compilers from Cray and PGI. We will describe ongoing work and directions for future collaboration, including OpenACC support and parallel debugger integration.

Parametric Studies in Eclipse with TAU and PerfExplorer

With support for C/C++, Fortran, MPI, OpenMP, and performance tools, the Eclipse integrated development environment (IDE) is a serious contender as a programming environment for parallel applications. There is interest in adding capabilities in Eclipse for conducting workflows where an application is executed under different scenarios and its outputs are processed. For instance, parametric studies are a requirement in many benchmarking and performance tuning efforts, yet there was no experiment management support available for the Eclipse IDE. In this paper, we describe an extension of the Parallel Tools Platform (PTP) plugin for the Eclipse IDE. The extension provides a graphical user interface for selecting experiment parameters, launches build and run jobs, manages the performance data, and launches an analysis application to process the data. We describe our implementation, and discuss three experiment examples which demonstrate the experiment management support.

Performance Tool Workflows

Using the Eclipse platform we have provided a centralized resource and unified user interface for the encapsulation of existing command-line based performance analysis tools. In this paper we describe the user-definable tool workflow system provided by this performance framework. We discuss the frameworks implementation and the rationale for its design. A use case featuring the TAU performance analysis system demonstrates the utility of the workflow system with respect to conventional performance analysis procedures.