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Achieving high-performance with a sparse direct solver on Intel KNL

Emmanuel Agullo, Alfredo Buttari, Mikko Byckling, Abdou Guermouche, Ian Masliah
Inria – LaBRI, Bordeaux
[Research Report] RR-9035, Inria Bordeaux Sud-Ouest, 2017

@phdthesis{agullo2017achieving,

   title={Achieving high-performance with a sparse direct solver on Intel KNL},

   author={Agullo, Emmanuel and Buttari, Alfredo and Byckling, Mikko and Guermouche, Abdou and Masliah, Ian},

   year={2017},

   school={Inria Bordeaux Sud-Ouest; CNRS-IRIT; Intel corporation; Universit{‘e} Bordeaux}

}

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The need for energy-efficient high-end systems has led hardware vendors to design new types of chips for general purpose computing. However, designing or porting a code tailored for these new types of processing units is often considered as a major hurdle for their broad adoption. In this paper, we consider a modern Intel Xeon Phi processor, namely the Intel Knights Landing (KNL) and a numerical code initially designed for a classical multi-core system. More precisely, we consider the qr_mumps scientific library implementing a sparse direct method on top of the StarPU runtime system. We show that with a portable programming model (task-based programming), a good software support (a robust runtime system coupled with an efficient scheduler) and some well defined hardware and software settings, we are able to transparently run the exact same numerical code. This code not only achieves very high performance (up to 1 TFlop/s) on the KNL but also significantly outperforms a modern Intel Xeon multi-core processor both in terms of time to solution and energy efficiency up to a factor of 2.0.
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