Distributed and Parallel Programming Paradigms on the K computer and a Cluster

In this paper, we focus on a distributed and parallel programming paradigm for massively multicore supercomputers. We introduce YML, a development and execution environment for parallel and distributed applications based on a graph of task components scheduled at runtime and optimized for several middlewares. Then we show why YML may be well adapted to applications running on a lot of cores. The tasks are developed with the PGAS language XMP based on directives. We use YML/XMP to implement the block-wise Gaussian elimination to solve linear systems. We also implemented it with XMP and MPI without blocks. ScaLAPACK was also used to created an non-block implementation of the resolution of a dense linear system through LU factorization. Furthermore, we run it with different amount of blocks and number of processes per task. We find out that a good compromise between the number of blocks and the number of processes per task gives interesting results. YML/XMP obtains results faster than XMP on the K computer and close to XMP, MPI and ScaLAPACK on clusters of CPUs. We conclude that parallel and distributed multilevel programming paradigms like YML/XMP may be interesting solutions for extreme scale computing.