The design of high-performance computing architectures requires performance analysis of large-scale parallel applications to derive various parameters concerning hardware design and software development. The process of performance analysis and benchmarking an application can be done in several ways with varying degrees of fidelity. One of the most cost-effective ways is to do a coarse-grained study of large-scale parallel applications through the use of program skeletons. The concept of a "program skeleton" that we discuss in this paper is an abstracted program that is derived from a larger program where source code that is determined to be irrelevant is removed for the purposes of the skeleton. In this work, we develop a semi-automatic approach for extracting program skeletons based on compiler program analysis. We demonstrate correctness of our skeleton extraction process by comparing details from communication traces, as well as show the performance speedup of using skeletons by running simulations in the SST/macro simulator.

Twig is a language for writing typemaps, programs which transform the type of a value while preserving its underlying meaning. Typemaps are typically used by tools that generate code, such as multi-language wrapper generators, to automatically convert types as needed. Twig builds on existing typemap tools in a few key ways. Twig's typemaps are composable so that complex transformations may be built from simpler ones. In addition, Twig incorporates an abstract, formal model of code generation, allowing it to output code for different target languages. We describe Twig's formal semantics and show how the language allows us to concisely express typemaps. Then, we demonstrate Twig's utility by building an example typemap.

Workflows offer scientists a simple but flexible programming model at a level of abstraction closer to the domain-specific activities that they seek to perform. However, languages for describing workflows tend to be highly complex, or specialized towards a particular domain, or both. WOOL is an abstract workflow language with human-readable syntax, intuitive semantics, and a powerful abstract type system. WOOL workflows can be targeted to almost any kind of runtime system supporting data-flow computation. This paper describes the design of the WOOL language and the implementation of its compiler, along with a simple example runtime. We demonstrate its use in an image-processing workflow.

The performance of a parallel application on a scalable HPC system is determined by user-level execution of the application code and system-level (OS kernel) operations. To understand the influences of system-level factors on application performance, the measurement of OS kernel activities is key. We describe a technology to observe kernel actions and make this information available to application-level performance measurement tools. The benefits of merged application and OS performance information and its use in parallel performance analysis are demonstrated, both for profiling and tracing methodologies. In particular, we focus on the problem of kernel noise assessment as a stress test of the approach. We show new results for characterizing noise and introduce new techniques for evaluating noise interference and its effects on application execution. Our kernel measurement and noise analysis technologies are being developed as part of Linux OS environments for scalable parallel systems.

The ability to understand the factors contributing to parallel program performance are vital for understanding the impact of machine parameters on the performance of specific applications. We propose a methodology for analyzing the performance characteristics of parallel programs based, on message-passing traces of their execution on a set of processors. Using this methodology, we explore how perturbations in both single processor performance and the messaging layer impact the performance of the traced run. This analysis provides a quantitative description of the sensitivity of applications to a variety of performance parameters to better understand the range of systems upon which an application can be expected to perform well. These performance parameters include operating system, interference and variability in message latencies within the interconnection network layer.

Abstract The Common Component Architecture (CCA) is a component model for high‐performance computing, developed by a grass‐roots effort of computational scientists. Although the CCA is usable with CORBA‐like distributed‐object components, its main purpose is to set forth a component model for high‐performance, parallel computing. Traditional component models are not well suited for performance and massive parallelism. We outline the design pattern for the CCA component model, discuss our strategy for language interoperability, describe the development tools we provide, and walk through an illustrative example using these tools. Performance and scalability, which are distinguishing features of CCA components, affect choices throughout design and implementation. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract Chasm is a toolkit providing seamless language interoperability between Fortran 95 and C++. Language interoperability is important to scientific programmers because scientific applications are predominantly written in Fortran, while software tools are mostly written in C++. Two design features differentiate Chasm from other related tools. First, we avoid the common‐denominator type systems and programming models found in most Interface Definition Language (IDL)‐based interoperability systems. Chasm uses the intermediate representation generated by a compiler front‐end for each supported language as its source of interface information instead of an IDL. Second, bridging code is generated for each pairwise language binding, removing the need for a common intermediate data representation and multiple levels of indirection between the caller and callee. These features make Chasm a simple system that performs well, requires minimal user intervention and, in most instances, bridging code generation can be performed automatically. Chasm is also easily extensible and highly portable. Copyright © 2005 John Wiley & Sons, Ltd.

Abstract This work targets the emerging use of software component technology for high‐performance scientific parallel and distributed computing. While component software engineering will benefit the construction of complex science applications, its use presents several challenges to performance measurement, analysis, and optimization. The performance of a component application depends on the interaction (possibly nonlinear) of the composed component set. Furthermore, a component is a ‘binary unit of composition’ and the only information users have is the interface the component provides to the outside world. A performance engineering methodology and development approach is presented to address evaluation and optimization issues in high‐performance component environments. We describe a prototype implementation of a performance measurement infrastructure for the Common Component Architecture (CCA) system. A case study demonstrating the use of this technology for integrated measurement, monitoring, and optimization in CCA component‐based applications is given. Copyright © 2005 John Wiley & Sons, Ltd.

Microbenchmarks, i.e. very small computational kernels, have become commonly used for quantitative measures of node performance in clusters. For example, a commonly used benchmark measures the amount of time required to perform a fixed quantum of work. Unfortunately, this benchmark is one of many that violate well known rules from sampling theory, leading to erroneous, contradictory or misleading results. At a minimum, these types of benchmarks can not be used to identify time-based activities that may interfere with and hence limit application performance. Our original and primary goal remains to identify noise in the system due to periodic activities that are not part of user application code. We discuss why the 'fixed quantum of work' benchmark provides data that is of limited use for analysis; and we show code for, discuss, and analyze results from a microbenchmark which follows good rules of sampling hygiene, and hence provides useful data for analysis.

Supermon is a flexible set of tools for high speed, scalable cluster monitoring. Node behavior can be monitored much faster than with other commonly used methods (e.g., rstatd). In addition, Supermon uses a data protocol based on symbolic expressions (S-expressions) at all levels of Supermon, from individual nodes to entire clusters. This contributes to Supermon's scalability and allows it to function in a heterogeneous environment. This paper presents the Supermon architecture and discuss initial performance measurements on a cluster of heterogeneous Alpha-processor based nodes.

The Virtual Notebook Environment (ViNE) is a platform-independent, web-based interface designed to support a range of scientific activities across distributed, heterogeneous computing platforms. ViNE provides scientists with a web-based version of the common paper-based lab notebook, but in addition, it provides support for collaboration and management of computational experiments. Collaboration is supported with the web-based approach, which makes notebook material generally accessible and with a hierarchy of security mechanisms that screen that access. ViNE provides uniform, system-transparent access to data, tools, and programs throughout the scientist's computing infrastructure. Computational experiments can be launched from ViNE using a visual specification language. The scientist is freed from concerns about inter-tool connectivity, data distribution, or data management details. ViNE also provides support for dynamically linking analysis results back into the notebook content. In this paper we present the ViNE system architecture and a case study of its use in neuropsychology research at the University of Oregon. Our case study with the Brain Electrophysiology Laboratory (BEL) addresses their need for data security and management, collaborative support, and distributed analysis processes. The current version of ViNE is a prototype system being tested with this and other scientific applications.