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Objectives: The objectives of this course are to understand the fundamental concepts supporting message-passing and shared memory programming models. The course covers the two widely used programming models: MPI for the distributed-memory environments, and OpenMP for the shared-memory architectures. It also presents the main tools developed at BSC to get information and analyze the execution of parallel applications, Paraver and Extrae. Moreover it sets the basic foundations related with task decomposition and parallelization inhibitors, using a tool to analyze potential parallelism and dependences, Tareador.
Additionally, it presents the Parallware compiler, which is able to automatically parallelize a large number of program structures, and provide hints to the programmer with respect to how to change the code to improve parallelization. It deals with debugging alternatives, including the use of GDB and Totalview. The use of OpenMP in conjunction with MPI to better exploit the shared-memory capabilities of current compute nodes in clustered architectures is also considered. Paraver will be used along the course as the tool to understand the behavior and performance of parallelized codes.
The course is taught using formal lectures and practical/programming sessions to reinforce the key concepts and set up the compilation/execution environment.
Level: For trainees with some theoretical and practical knowledge, some programming experience.
Learning Outcomes: On completion of this course students should be able to:
- Develop benchmarks and applications with the MPI, OpenMP/OmpSs and mixed MPI/OpenMP/OmpSs programming models.
- Analyze the execition of MPI/OpenMP/OmpSs applications, tune their behaviour, and debug them in parallel architectures.
- Gain experience with the Tareador and Parallware tools, to obtain hints for a better parallelization of applications.
Prerequisites: Fortran, C or C++ programming. All examples in the course will be done in C.
Please, bring your own laptop. Attendants can bring their own applications and work with them during the course for parallelization and analysis.