A Component Architecture for High-Performance Scientific Computing

The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry

Annual Report of the University, 2000-2001, Volumes 1-4

Message from the President Thank you for joining me in this look back over the past year at the University of New Mexico. It was a year filled with activity, accomplishment and challenge, and this is our opportunity to reflect back on that year. In 2000-2001 we engaged in a University-wide strategic planning process that called on the energies and talents of hundreds of individuals- faculty, staff, students and members of our broader community. The plan, which will be completed in Fall 2001, will serve as our roadmap for the future and will guide our efforts to capitalize on the opportunities and to meet the challenges of the next several years. This process has encouraged us to examine closely our mission and our values, who we are and what we aspire to become. It has given us reason to be proud of our past and cause to think seriously about how we must change in the future. While this was a year for looking ahead, it was also a year of significant accomplishment. For example, we launched a comprehensive set of programs designed to enrich the academic and social experiences of our undergraduate students. We began the implementation of Freshman Learning Communities where small cohorts of students study and learn together in a common set of courses under the guidance of a senior faculty scholar. We reorganized our advisement systems, we undertook the construction or renovation of student-centered facilities on campus, and we created new support systems to enhance student academic success. It was a year in which our support of faculty, staff and students was our highest priority. Through the support of the New Mexico Legislature, faculty and staff received significant salary increases. A new health benefits plan for graduate assistants was implemented. Our Staff as Students program enabled more than 40 staff members to obtain UNM degrees. And, a Center for Scholarship in Teaching and Learning was established to assist faculty in their efforts to develop more effective teaching skills. Finally, this was a year in which UNM dramatically expanded its role in the local community and throughout the state. Never before has the University been as active or as visible in meeting its public responsibility as it was in 2000-2001. From its active participation in economic development initiatives, to its involvement in K-12 educational improvement efforts, to its significant leadership role in health care delivery, UNM demonstrated its ability to help the state meet its most pressing social challenges. And, as UNM took on a more visible role in supporting the state\\u27s citizens, the support for UNM was returned in kind. This year, annual giving to the University rose to a record 35.3 million dollars, a 40% increase over just two years ago. All told, it has been a gratifying and successful year. However, we cannot allow our past accomplishments to mask the continued challenges facing this University. Neither will we allow these challenges to dominate our thinking and diminish out pride in what the University has achieved. So we will savor our successes and continue to move forward. As always, we thank you for sharing our dreams and for supporting the University of New Mexico. Sincerely, William C. Gordon, Presiden

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Generating and auto-tuning parallel stencil codes

In this thesis, we present a software framework, Patus, which generates high performance stencil codes for different types of hardware platforms, including current multicore CPU and graphics processing unit architectures. The ultimate goals of the framework are productivity, portability (of both the code and performance), and achieving a high performance on the target platform. A stencil computation updates every grid point in a structured grid based on the values of its neighboring points. This class of computations occurs frequently in scientific and general purpose computing (e.g., in partial differential equation solvers or in image processing), justifying the focus on this kind of computation. The proposed key ingredients to achieve the goals of productivity, portability, and performance are domain specific languages (DSLs) and the auto-tuning methodology. The Patus stencil specification DSL allows the programmer to express a stencil computation in a concise way independently of hardware architecture-specific details. Thus, it increases the programmer productivity by disburdening her or him of low level programming model issues and of manually applying hardware platform-specific code optimization techniques. The use of domain specific languages also implies code reusability: once implemented, the same stencil specification can be reused on different hardware platforms, i.e., the specification code is portable across hardware architectures. Constructing the language to be geared towards a special purpose makes it amenable to more aggressive optimizations and therefore to potentially higher performance. Auto-tuning provides performance and performance portability by automated adaptation of implementation-specific parameters to the characteristics of the hardware on which the code will run. By automating the process of parameter tuning — which essentially amounts to solving an integer programming problem in which the objective function is the number representing the code's performance as a function of the parameter configuration, — the system can also be used more productively than if the programmer had to fine-tune the code manually. We show performance results for a variety of stencils, for which Patus was used to generate the corresponding implementations. The selection includes stencils taken from two real-world applications: a simulation of the temperature within the human body during hyperthermia cancer treatment and a seismic application. These examples demonstrate the framework's flexibility and ability to produce high performance code