Object-oriented application frameworks
Communications of the ACM
ACM Computing Surveys (CSUR)
Integration and applications of the TAU performance system in parallel Java environments
Proceedings of the 2001 joint ACM-ISCOPE conference on Java Grande
Problem Solving Environment Infrastructure for High Performance Computer Systems
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
Integrating Performance Analysis in the Uintah Software Development Cycle
ISHPC '02 Proceedings of the 4th International Symposium on High Performance Computing
Editorial: enterprise frameworks
Software—Practice & Experience - Special issue: Enterprise frameworks
JHDL - An HDL for Reconfigurable Systems
FCCM '98 Proceedings of the IEEE Symposium on FPGAs for Custom Computing Machines
Toward a Common Component Architecture for High-Performance Scientific Computing
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
The Cactus Computational Toolkit and Using Distributed Computing to Collide Neutron Stars
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Uintah: A Massively Parallel Problem Solving Environment
HPDC '00 Proceedings of the 9th IEEE International Symposium on High Performance Distributed Computing
Coven " A Framework for High Performance Problem Solving Environments
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Coven: a computational model and problem solving environment framework for supporting optimization of parallel applications
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The computational problems that scientists face are rapidly escalating in size and scope. Moreover, the computer systems used to solve these problems are becoming significantly more complex than the familiar, well-understood sequential model on their desktops. While it is possible to re-train scientists to use emerging high-performance computing (HPC) models, it is much more effective to provide them with a higher-level programming environment that has been specialized to their particular domain. By fostering interaction between HPC specialists and the domain scientists, problem-solving environments (PSEs) provide a collaborative environment. A PSE environment allows scientists to focus on expressing their computational problem while the PSE and associated tools support mapping that domain-specific problem to a high-performance computing system. This article describes Arches, an object-oriented framework for building domain-specific PSEs. The framework was designed to support a wide range of problem domains and to be extensible to support very different high-performance computing targets. To demonstrate this flexibility, two PSEs have been developed from the Arches framework to solve problem in two different domains and target very different computing platforms. The Coven PSE supports parallel applications that require large-scale parallelism found in cost-effective Beowulf clusters. In contrast, RCADE targets FPGA-based reconfigurable computing and was originally designed to aid NASA Earth scientists studying satellite instrument data.