Gregory Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, Sanjeev Satheesh
Tatsumi Aoyama, Ken-Ichi Ishikawa, Yasuyuki Kimura, Hideo Matsufuru, Atsushi Sato, Tomohiro Suzuki, Sunao Torii
Steven Eliuk, Cameron Upright, Anthony Skjellum
Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Linear Algebra, Matrix multiplication, Neural and Evolutionary Computing, Neural networks, nVidia, OpenMPI, Tesla K80
Marat Dukhan, Richard Vuduc, Jason Riedy
Jorge F. Fabeiro, Diego Andrade, Basilio B. Fraguela
Tags: AMD FirePro S9150, ATI, Code generation, Computer science, Heterogeneous systems, Intel Xeon Phi, Matrix multiplication, nVidia, OpenCL, Package, Performance, performance portability, Tesla K20
February 11, 2016 by
hgpuToomas Remmelg, Thibaut Lutz, Michel Steuwer, Christophe Dubach
February 10, 2016 by
hgpuA. Abdelfattah, M. Baboulin, V. Dobrev, J. Dongarra, C. Earl, J. Falcou, A. Haidar, I. Karlin, Tz. Kolev, I. Masliah, S. Tomov
Tags: Algorithms, Code generation, Computer science, CUDA, FEM, Finite element method, Linear Algebra, Matrix multiplication, nVidia, OpenMP, Tesla K40
Linnan Wang, Wei Wu, Jianxiong Xiao, Yang Yi
Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan X, Tesla K40
November 20, 2015 by
hgpu
