This is valuable to enable for their optimization, or to fit the resources needed by the program into the resources offered by the architecture. One concern in the development of parallel programs is the prediction of their performance from the source code. The PAPP workshops are aimed both at researchers involved in the development of high level approaches to parallel and grid computing and at computational science researchers who are potential users of these languages and tools. The PAPP workshops focus on practical aspects of high-level parallel programming: design, implementation and optimization of high-level programming languages and tools (performance predictors working on high-level parallel/grid source code, visualisation of abstract behaviour, automatic hotspot detectors, high-level GRID resource managers, compilers, automatic generators, etc.), applications in all fields of computational science, benchmarks and experiments. These articles are a selection of extended and revised versions of papers presented at the second international workshop on Practical Aspects of High-Level Parallel Programming (PAPP), affiliated to the International Conference on Computational Science (ICCS 2005). This special issue of Scalable Computing: Practice and Experience presents recent work of researchers in these fields. have all produced methods and tools to improve the price/performance ratio of parallel software, and broaden the range of target applications. Algorithmic skeletons, parallel extensions of functional languages such as Haskell and ML, as well as parallel logic and constraint programming or parallel execution of declarative programs such as SQL queries, etc. By basing them on formal semantics, it even becomes possible to certify the correctness of critical parts of the application. High level languages offer a high degree of abstraction which eases the development of complex systems. Parallel and grid computing provide solutions to this increasing need for computing power. Practical Aspects of High-Level Parallel ProgrammingĬomputational Science applications are getting more and more complex to develop and require more and more computing power.
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