Auto-tuned parallel algorithms for hybrid multi-core/many-core processor clusters

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The main goal of parallel computing research is to create enabling technology for solving data intensive and/or computationally hard problems in the Natural Sciences, Engineering, Medical Sciences, and Social Sciences. My research objective is to contribute towards that goal by developing general parallel algorithm design methodologies and parallel algorithms for specific problem areas.For the next funding period, I propose to study auto-tuned parallel algorithms for hybrid multi-core/many-core processor clusters. Such hybrid processor clusters consist of nodes that contain both, multi-core and many-core (GPU) processors. The focus of our proposed fundamental research will be on non-numerical problems such as computational geometry and graph algorithms. These problems often involve irregular data movements and complex data structures that are particularly challenging for parallel computing. I propose to extend this parallel algorithms research for hybrid clusters by integrating the study of auto-tuned parallel methods. The goal of auto-tuning is to design efficient parallel algorithms and software that adapt automatically to different hardware configurations. Rather than developing new parallel algorithms for every new parallel architecture, auto-tuning aims at designing portable parallel algorithms and software for a large class of current and (hopefully) future parallel machines. The auto-tuning approach is currently most advanced for linear algebra and other matrix based problems but there is very little work published so far on auto-tuning for non-numerical problems such as computational geometry and graph algorithms proposed for this project. Due to the irregular data movements and more advanced data structures, auto-tuning parallel algorithms for these problems will be considerably more challenging. To demonstrate the significance of our research and facilitate knowledge transfer, this project also has a research component on parallel scientific computing, focusing on the design and implementation of an auto-tuned parallel protein interaction prediction algorithm for hybrid processor clusters.