Effective SIMD Vectorization for Intel Xeon Phi Coprocessors

Article English OPEN
Xinmin Tian; Hideki Saito; Serguei V. Preis; Eric N. Garcia; Sergey S. Kozhukhov; Matt Masten; Aleksei G. Cherkasov; Nikolay Panchenko;
(2015)
  • Publisher: Hindawi Limited
  • Journal: Scientific Programming (issn: 1058-9244, eissn: 1875-919X)
  • Related identifiers: doi: 10.1155/2015/269764
  • Subject: Computer software | Article Subject | QA76.75-76.765
    acm: ComputerSystemsOrganization_PROCESSORARCHITECTURES

Efficiently exploiting SIMD vector units is one of the most important aspects in achieving high performance of the application code running on Intel Xeon Phi coprocessors. In this paper, we present several effective SIMD vectorization techniques such as less-than-full-v... View more
  • References (23)
    23 references, page 1 of 3

    Intel Corporation, null. Intel Xeon Phi Coprocessor System Software Developers Guide.

    Satish, N., Kim, C., Chhugani, J., Saito, H., Krishnaiyer, R., Smelyanskiy, M., Girkar, M., Dubey, P.. Can traditional programming bridge the Ninja performance gap for parallel computing applications?. : 440-451

    Reinders, J.. An Overview of Programming for Intel Xeon processor and Intel Xeon Phi Coprocessor.

    Intel Corporation, null. Intel Advanced Vector Extensions Programming Reference. 2011

    Bik, A. J. C., Girkar, M., Grey, P. M., Tian, X.. Automatic intra-register vectorization for the intel architecture. International Journal of Parallel Programming. 2002; 30 (2): 65-98

    Bik, A. J. C., Kreitzer, D. L., Tian, X.. A case study on compiler optimizations for the Intel CoreTM 2 duo processor. International Journal of Parallel Programming. 2008; 36 (6): 571-591

    Tian, X., Saito, H., Girkar, M., Preis, S. V., Kozhukhov, S. S., Cherkasov, A. G., Nelson, C., Panchenko, N., Geva, R.. Compiling C/C++ SIMD extensions for function and loop vectorizaion on multicore-SIMD processors. : 2349-2358

    Lu, H. J., Garkar, M., Matz, M., Hubicka, J., Jaeger, A., Mitchell, M.. System V Application Binary Interface K1OM Architecture Processor Supplement.

    Aarseth, S. J.. Gravitational N-Body Simulations: Tools and Algorithm. 2003

    Gray, A. G., Moore, A. W.. ‘N-body’ problems in statistical learning. Advances in Neural Information Processing Systems (NIPS). 2000: 521-527

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