Parallel Heterogeneous Algorithms for Computational Science

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High performance computing (HPC) resources are becoming more readily available at a reasonable cost, particularly with the availability of programmable graphics processing units (GPUs). The parallelism is challenging to exploit in practice, and the rapidly evolving hardware can lead to increased maintenance cost of software. This proposal addresses the challenges associated with developing dynamic architecture-aware parallel scientific computing applications, to deal with the rapidly changing nature of HPC resources. GPUs presently contain hundreds of cores, which when combined with the growth of the number of cores per CPU is resulting in a dramatic increase in the overall degree of parallelism required of applications. Three levels of parallelism must be considered to fully use such systems: coarse grained thread-level parallelism for CPUs, massive fine grained data parallelism for GPUs, and message passing for clusters of CPUs/GPUs. The balance between these three types of parallelism also needs to be matched to the computing system. Therefore static approaches to algorithm design and implementation may hinder portability of performance across different types of parallel computers. This proposal focuses on new algorithmic approaches to finding sufficient multi-level parallelism and on identification and scheduling of tasks, taking into account the performance of each computing platform component and the communication time between components. This will lead to applications that are able to adapt to different platforms by scheduling tasks to the heterogeneous components at runtime through efficient scheduling algorithms and selection of optimal values of performance-critical parameters. Work will focus initially on two areas: dynamic programming and solution of time dependent partial differential equations. In both areas new algorithmic approaches combined with scheduling on heterogeneous platforms will lead to a range of flexible high performance scientific computing applications in areas such as simulation of physical and biological processes and in bioinformatics.