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Extra resources for High Performance Computing: 31st International Conference, ISC High Performance 2016, Frankfurt, Germany, June 19-23, 2016, Proceedings

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High Perform. Comput. Appl. 24(4), 511–515 (2010). 1177/1094342010385729 19. : Fast implementation of DGEMM on Fermi GPU. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2011, pp. 35:1–35:11. ACM, New York (2011). 2063431 20. : Towards dense linear algebra for hybrid GPU accelerated manycore systems. Parellel Comput. Syst. Appl. 36(5–6), 232– 240 (2010) 21. : Benchmarking GPUs to tune dense linear algebra. In: SC 2008: Proceedings of the 2008 ACM/IEEE conference on Supercomputing, pp.

Metab. Eng. 25C, 50–62 (2014) 12. : Autotuning GEMM kernels for the Fermi GPU. IEEE Trans. Parallel Distrib. Syst. 23(11), 2045–2057 (2012) 13. : Performance upper bound analysis and optimization of SGEMM on Fermi and Kepler GPUs. In: Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), CGO 2013, pp. 1– 10. IEEE Computer Society, Washington, DC, USA (2013). org/10. 6494986 14. : A note on auto-tuning GEMM for GPUs. A. ) ICCS 2009, Part I. LNCS, vol.

The number of classes can be extended based on other sensors like cache TCU: A Multi-Objective Hardware Thread Mapping Unit for HPC Clusters 45 Table 2. Application class configuration matrix with weights for different sensors Class ID Sensors Fill level CPU Util. CPU Temp. 3 miss counters etc. Applications express their hardware needs by specifying their class id as part of the thread descriptor. We assume a thread descriptor having a 3-tuple data structure (void * thread, void * argv, char class id) with the memory footprint of just 9 bytes.

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