Applications must scale well to make efficient use of even medium-scale parallel systems. Because scaling problems are often difficult to diagnose, there is a critical need for scalable tools that guide scientists to the root causes of performance bottlenecks.

Today's largest supercomputers have over two hundred thousand CPU cores and even larger systems are under development. Typically, these systems are programmed using message passing. Over the past decade, there has been considerable interest in developing simpler and more expressive programming models for them. Partitioned global address space (PGAS) languages are viewed as perhaps the most promising alternative. In this paper, we report on our experience developing a set of PGAS extensions to Fortran that we call Co array Fortran 2.0 (CAF 2.0). Our design for CAF 2.0 goes well beyond the original 1998 design of Co array Fortran (CAF) by Numrich and Reid. CAF 2.0 includes language support for many features including teams, collective communication, asynchronous communication, function shipping, and synchronization. We describe the implementation of these features and our experiences using them to implement the High Performance Computing Challenge (HPCC) benchmarks, including High Performance Linpack (HPL), Random Access, Fast Fourier Transform (FFT), and STREAM triad. On 4096 CPU cores of a Cray XT with 2.3 GHz single socket quad-core Opteron processors, we achieved 18.3 TFLOP/s with HPL, 2.01 GUP/s with Random Access, 125 GFLOP/s with FFT, and a bandwidth of 8.73 TByte/s with STREAM triad. we call Co array Fortran 2.0 (CAF 2.0). Our design for CAF 2.0 goes well beyond the original 1998 design of Coarray Fortran (CAF) by Numrich and Reid. CAF 2.0 includes language support for many features including teams, collective communication, asynchronous communication, function shipping, and synchronization. We describe the implementation of these features and our experiences using them to implement the High Performance Computing Challenge (HPCC) benchmarks, including High Performance Linpack (HPL), Random Access, Fast Fourier Transform (FFT), and STREAM triad. On 4096 CPU cores of a Cray XT with 2.3 GHz single socket quad-core Opteron processors, we achieved 18.3 TFLOP/s with HPL, 2.01 GUP/s with Random Access, 125 GFLOP/s with FFT, and a bandwidth of 8.73 TByte/s with STREAM triad.

Applications must scale well to make efficient use of today's class of petascale computers, which contain hundreds of thousands of processor cores. Inefficiencies that do not even appear in modest-scale executions can become major bottlenecks in large-scale executions. Because scaling problems are often difficult to diagnose, there is a critical need for scalable tools that guide scientists to the root causes of scaling problems. Load imbalance is one of the most common scaling problems. To provide actionable insight into load imbalance, we present post-mortem parallel analysis techniques for pinpointing and quantifying load imbalance in the context of call path profiles of parallel programs. We show how to identify load imbalance in its static and dynamic context by using only low-overhead asynchronous call path profiling to locate regions of code responsible for communication wait time in SPMD executions. We describe the implementation of these techniques within HPCTOOLKIT.

Abstract HPCT OOLKIT is an integrated suite of tools that supports measurement, analysis, attribution, and presentation of application performance for both sequential and parallel programs. HPCT OOLKIT can pinpoint and quantify scalability bottlenecks in fully optimized parallel programs with a measurement overhead of only a few percent. Recently, new capabilities were added to HPCT OOLKIT for collecting call path profiles for fully optimized codes without any compiler support, pinpointing and quantifying bottlenecks in multithreaded programs, exploring performance information and source code using a new user interface, and displaying hierarchical space–time diagrams based on traces of asynchronous call path samples. This paper provides an overview of HPCT OOLKIT and illustrates its utility for performance analysis of parallel applications. Copyright © 2009 John Wiley & Sons, Ltd.

Cutting-edge science and engineering applications require petascale computing. It is, however, a significant challenge to use petascale computing platforms effectively. Consequently, there is a critical need for performance tools that enable scientists to understand impediments to performance on emerging petascale systems. In this paper, we describe HPCToolkit---a suite of multi-platform tools that supports sampling-based analysis of application performance on emerging petascale platforms. HPCToolkit uses sampling to pinpoint and quantify both scaling and node performance bottlenecks. We study several emerging petascale applications on the Cray XT and IBM BlueGene/P platforms and use HPCToolkit to identify specific source lines --- in their full calling context --- associated with performance bottlenecks in these codes. Such information is exactly what application developers need to know to improve their applications to take full advantage of the power of petascale systems.

OpenMP was recently proposed by a group of vendors as a programming model for shared memory parallel architectures. The growing popularity of such systems, and the rapid availability of product-strength compilers for OpenMP, seem to guarantee a broad take-up of this paradigm if appropriate tools for application development can be provided. POST is an EU-funded project that is developing a product, based on FORESYS from Simulog, which aims to reduce the human effort involved in the creation of OpenMP code. Additional research within the project focuses on alternative techniques to support OpenMP application development that target a broad variety of users. Functionality ranges from fully automatic strategies for novice users, the provision of parallelization hints, and step-by-step strategies for porting code, to a range of transformations and source code analyses that may be used by experts, including the ability to create application-specific transformations. The work is accompanied by the development of OpenMP versions of several industrial applications. Copyright © 2000 John Wiley & Sons, Ltd.