Integrating model-based optimization and program transformation to generate efficient parallel programs |
| |
| Affiliation: | 1. Université Paris-Est, Lab’Urba (EA 3482), UPEC, UPEMLV, EIVP, F-7720 Champs-sur-Marne, France;2. Department of Science, Technology, Engineering and Public Policy, University College London, 36-38 Fitzroy Square, London W1T 6EY, United Kingdom;1. Department of Agronomy, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur 741 252, West Bengal, India;2. Department of Agricultural Chemistry and Soil Science, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur 741 252, West Bengal, India;1. State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China;2. School of Management Science and Engineering, Central University of Finance and Economics, Beijing, 100081, China;3. School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, 48109-1041, United States;4. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing, 100084, China |
| |
| Abstract: | The paper gives an overview on the DSPL programming environment, an integrated approach to automate system design and implementation of applications run on dedicated parallel systems. The programming environment consists of a data-flow language and an integrated set of tools. The tools automatically derive a software model from the given application program. Based on the model, the design decisions as the network topology, the task mapping and schedule as well as the optimal use of buffers are computed. Finally, the design decisions are automatically implemented by transforming the application program in executable code for the chosen processor network. The DSPL programming environment integrates model-based optimization techniques and program transformation techniques. The integration allows to include new aspects in the optimization process. Especially optimizations crucial to the semantics of the program can be included. The most important examples of such optimizations are the enforcement of the schedule in case of data-dependent execution of tasks and the transformation of buffered communication to unbuffered communication. Both aspects are crucial to the generation of efficient parallel implementations. The integration of the two aspects is supported by a formal framework. This allows to formally prove the correctness of the program optimizations performed by the programming environment. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
