Filip Petrovič, David Střelák, Jana Hozzová, Jaroslav Oľha, Richard Trembecký, Siegfried Benkner, Jiří Filipovič
Tags: AMD Radeon RX Vega 56, Auto-Tuning, Benchmarking, Computer science, CUDA, Electron microscopy, Intel Xeon Phi, Microscopy, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Performance, performance portability, Tesla K20
Martin Bauer, Sebastian Eibl, Christian Godenschwager, Nils Kohl, Michael Kuron, Christoph Rettinger, Florian Schornbaum, Christoph Schwarzmeier, Dominik Thönnes, Harald Köstler, Ulrich Rüde
Tags: Code generation, CUDA, Heterogeneous systems, Lattice Boltzmann model, MPI, nVidia, Package, Particle simulation, performance portability, Physics, Tesla P100
D. Lebrun-Grandie, A. Prokopenko, B. Turcksin, S. R. Slattery
September 8, 2019 by
hgpuPaul Sathre, Mark Gardner, Wu-chun Feng
Tags: AMD FirePro S9150, ATI, Compilers, Computer science, CUDA, FPGA, nVidia, OpenCL, Package, performance portability, Tesla K80
Simon Garcia De Gonzalo, Sitao Huang, Juan Gomez-Luna, Simon Hammond, Onur Mutlu, Wen-mei Hwu
John Lawson, Mehdi Goli, Duncan McBain, Daniel Soutar, Louis Sugy
Tags: AMD R9 Nano, ATI, BLAS, Computer science, Deep learning, Linear Algebra, Machine learning, Mathematical Software, OpenCL, Package, Performance, performance portability, SYCL
Paul Sathre, Mark Gardner, Wu-chun Feng
Tags: AMD FirePro S9150, ATI, Computer science, CUDA, FPGA, Intel Xeon Phi, nVidia, OpenCL, Package, performance portability, Tesla K80
Ada Sedova, Andreas Tillack, Arnold Tharrington
David Pfander, Gregor Daiss, Dirk Pfluger
Tags: Clustering, Computer science, Data mining, Distributed computing, Heterogeneous systems, Machine learning, MPI, nVidia, OpenCL, Package, performance portability, Tesla P100
February 10, 2019 by
hgpu
