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Capability Models for Manycore Memory Systems: A Case-Study with Xeon Phi KNL

Sabela Ramos, Torsten Hoefler
Scalable Parallel Computing Lab, Department of Computer Science, ETH Zurich
31st IEEE International Parallel & Distributed Processing Symposium (IPDPS’17), 2017

@article{ramo2017scapability,

   title={Capability Models for Manycore Memory Systems: A Case-Study with Xeon Phi KNL},

   author={Ramos, Sabela and Hoefler, Torsten},

   year={2017}

}

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Increasingly complex memory systems and onchip interconnects are developed to mitigate the data movement bottlenecks in manycore processors. One example of such a complex system is the Xeon Phi KNL CPU with three different types of memory, fifteen memory configuration options, and a complex on-chip mesh network connecting up to 72 cores. Users require a detailed understanding of the performance characteristics of the different options to utilize the system efficiently. Unfortunately, peak performance is rarely achievable and achievable performance is hardly documented. We address this with capability models of the memory subsystem, derived by systematic measurements, to guide users to navigate the complex optimization space. As a case study, we provide an extensive model of all memory configuration options for Xeon Phi KNL. We demonstrate how our capability model can be used to automatically derive new close-to-optimal algorithms for various communication functions yielding improvements 5x and 24x over Intel’s tuned OpenMP and MPI implementations, respectively. Furthermore, we demonstrate how to use the models to assess how efficiently a bitonic sort application utilizes the memory resources. Interestingly, our capability models predict and explain that the high bandwidth MCDRAM does not improve the bitonic sort performance over DRAM.
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