Abstract
Interconnects in emerging high performance computing systems feature hardware support for one-sided, asynchronous communication and global address space programming models in order to improve parallel efficiency and productivity by allowing communication and computation overlap and outof- order delivery. In practice though, complex interactions between the software stack and the communication hardware make it challenging to obtain optimum performance for a full application expressed with a one-sided programming paradigm. Here, we present a proof-of-concept study for an autotuning framework that instantiates hybrid kernels based on refactored codes using available communication libraries or languages on a Cray XE6 and a SGI Altix UV 1000. We validate our approach by improving performance for bandwidth- and latency-bound kernels of interest in quantum physics and astrophysics by up to 35% and 80% respectively.
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