Younghwan Go, Muhammad Jamshed, YoungGyoun Moon, Changho Hwang, KyoungSoo Park
E. Calore, A. Gabbana, S. F. Schifano, R. Tripiccione
Tags: AMD FirePro W9100, ATI, Fluid dynamics, GPU cluster, Heterogeneous systems, Intel Xeon Phi, Lattice Boltzmann model, MPI, nVidia, OpenACC, OpenMP, Performance, performance portability, Tesla K80
Zeke Wang, Johns Paul, Bingsheng He, Wei Zhang
E. Calore, A. Gabbana, J. Kraus, S. F. Schifano, R. Tripiccione
Tags: AMD FirePro S9150, ATI, Benchmarking, CUDA, Fluid dynamics, Lattice Boltzmann model, MPI, nVidia, OpenACC, OpenCL, Performance, Tesla K80
Yixing Li, Zichuan Liu, Kai Xu, Hao Yu, Fengbo Ren
Tags: CNN, Computer science, CUDA, Deep learning, Energy-efficient computing, FPGA, Hardware Architecture, Neural networks, nVidia, nVidia GeForce GTX Titan X, Performance
February 22, 2017 by
hgpuMathias Bourgoin, Emmanuel Chailloux, Anastasios Doumoulakis
February 18, 2017 by
hgpuWilliam McDoniel, Markus Hohnerbach, Rodrigo Canales, Ahmed E. Ismail, Paolo Bientinesi
February 18, 2017 by
hgpuShaohuai Shi, Pengfei Xu, Xiaowen Chu
Tags: Algorithms, BLAS, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Linear Algebra, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Performance
February 14, 2017 by
hgpuEmanouil Atanassov, Michaela Barth, Mikko Byckling, Vali Codreanu, Nevena Ilieva, Tomas Karasek, Jorge Rodriguez, Sami Saarinen, Ole Widar Saastad, Michael Schliephake, Martin Stachon, Janko Strassburg, Volker Weinberg (Editor)
February 14, 2017 by
hgpuEriko Nurvitadhi, David Sheffield, Jaewoong Sim, Asit Mishra, Ganesh Venkatesh, Debbie Marr
Tags: Algorithms, ASIC, Computer science, CUDA, Deep learning, FPGA, Neural networks, nVidia, nVidia GeForce GTX Titan X, nVidia Tegra TX1, Performance
February 14, 2017 by
hgpuRobert V. Lim, Boyana Norris, Allen D. Malony
