Uintah parallelism infrastructure: a performance evaluation on the SGI origin 2000

ManuscriptUintah is a component-based visual problem solving environment (PSE) designed to specifically address the unique problems inherent in running massively parallel scientific computations on terascale computing platforms. In particular, development of the Uintah system is part of the C-SAFE [2] effort to study the interactions between hydrocarbon fires, structures and high-energy materials (explosives and propellants). In this paper we describe methods for generating meaningful performance measurements for the Uintah PSE runing on the SGI Origin 2000 multiprocessor architecture (these methods are applicable to many other applications.) These techniques include utilizing the non-intrusive performance counters built into the R10k and R12k processors, controlling process placement, controlling memory layout, and utilization of a task graph approach to specifying and solving the problem

Uintah: a massively parallel problem solving environment

Journal ArticleThis paper describes Uintah, a component-based visual problem solving environment (PSE) that is designed to specifically address the unique problems of massively parallel computation on terascale computing platforms. Uintah supports the entire life cycle of scientific applications by allowing scientific programmers to quickly and easily develop new techniques, debug new implementations, and apply known algorithms to solve novel problems. Uintah is built on three principles: 1) As much as possible, the complexities of parallel execution should be handled for the scientist, 2) software should be reusable at the component level, and 3) scientists should be able to dynamically steer and visualize their simulation results as the simulation executes. To provide this functionality, Uintah builds upon the best features of the SCIRun PSE and the DOE Common Component Architecture (CCA)

Performance analysis integration in the Uintah software development cycle

ManuscriptThe increasing complexity of high-performance computing environments and programming methodologies presents challenges for empirical performance evaluation. Evolving parallel and distributed systems require performance technology that can be flexibly configured to observe different events and associated performance data of interest. It must also be possible to integrate performance evaluation techniques with the programming paradigms and software engineering methods. This is particularly important for tracking performance on parallel software projects involving many code teams over many stages of development. This paper describes the integration of the TAU and XPARE tools in the Uintah Computational Framework (UCF). Discussed is the use of performance mapping techniques to associate low-level performance data to higher levels of abstraction in UCF and the use of performance regression testing to provides a historical portfolio of the evolution of application performance. A scalability study shows the benefits of integrating performance technology in building large-scale parallel applications