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SIMD-X: Programming and Processing of Graph Algorithms on GPUs

Hang Liu, H. Howie Huang

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Tags: Computer science, CUDA, Graph theory, nVidia, Tesla K20, Tesla K40, Tesla P100

December 16, 2018 by hgpu

Parallel source code transformation techniques using design patterns

David del Rio Astorga

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Tags: Algorithms, Auto-Tuning, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 680, Tesla K40, Thesis

December 2, 2018 by hgpu

Integration of CUDA Processing within the C++ library for parallelism and concurrency (HPX)

Patrick Diehl, Madhavan Seshadri, Thomas Heller, Hartmut Kaiser

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Tags: Computer science, CUDA, nVidia, Package, Programming Languages, Tesla K40, Tesla K80

November 3, 2018 by hgpu

OpenCL Performance Prediction using Architecture-Independent Features

Beau Johnston, Greg Falzon, Josh Milthorpe

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Tags: AMD FirePro S9150, AMD Radeon R9 290X, AMD Radeon R9 295X2, AMD Radeon RX 480, ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 1080 Ti, OpenCL, Package, performance portability, Tesla K20, Tesla K40

November 3, 2018 by hgpu

On Reinforcement Learning for Full-length Game of StarCraft

Zhen-Jia Pang, Ruo-Ze Liu, Zhou-Yu Meng, Yi Zhang, Yang Yu, Tong Lu

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Tags: AI, Algorithms, Artificial intelligence, Computer science, Deep learning, Games, Machine learning, nVidia, Tesla K40

October 6, 2018 by hgpu

Using SIMD and SIMT vectorization to evaluate sparse chemical kinetic Jacobian matrices and thermochemical source terms

Nicholas J. Curtis, Kyle E. Niemeyer, Chih-Jen Sung

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Tags: Chemical Physics, Code generation, Computational Physics, Factorization, nVidia, OpenCL, Package, Physics, Tesla C2075, Tesla K40

September 9, 2018 by hgpu

Smoothed-Particle Hydrodynamics Models: Implementation Features on GPUs

Sergey Khrapov, Alexander Khoperskov

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Tags: Astrophysics, CUDA, hydrodynamics, nVidia, OpenMP, Tesla K20, Tesla K40, Tesla K80

July 28, 2018 by hgpu

Energy Consumption of Algorithms for Solving the Compressible Navier-Stokes Equations on CPU’s, GPU’s and KNL’s

Satya P. Jammy, Christian T. Jacobs, David J. Lusher, Neil D. Sandham

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Tags: CUDA, Finite difference, Fluid dynamics, Intel Xeon Phi, Navier-Stokes equations, NSEs, nVidia, Tesla K40

July 7, 2018 by hgpu

Directive-Based, High-Level Programming and Optimizations for High-Performance Computing with FPGAs

Jacob Lambert, Seyong Lee, Jungwon Kim, Jeffrey S. Vetter, Allen D. Malony

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Tags: Computer science, FPGA, nVidia, OpenACC, OpenCL, Tesla K40

July 1, 2018 by hgpu

Analysis-driven Engineering of Comparison-based Sorting Algorithms on GPUs

Ben Karsin, Volker Weichert, Henri Casanova, John Iacono, Nodari Sitchinava

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 770, nVidia Quadro M 4000, Performance, Sorting, Tesla K40

July 1, 2018 by hgpu

Strategies for the Heterogeneous Execution of Large-Scale Simulations on Hybrid Supercomputers

Xavier Alvarez, Andrey Gorobets, F. Xavier Trias

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Tags: cfd, Computational Physics, Fluid dynamics, Heterogeneous systems, HPC, Hybrid computing, MPI, nVidia, OpenCL, Sparse matrix, Tesla K40

June 24, 2018 by hgpu

DeepSmith: Compiler Fuzzing through Deep Learning

Chris Cummins, Pavlos Petoumenos, Alastair Murray, Hugh Leather

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Tags: Compilers, Computer science, Deep learning, nVidia, OpenCL, Package, Tesla K40

June 24, 2018 by hgpu

MapD Core database

Exact Selectivity Computation for Modern In-Memory Database Query Optimization

BitCracker: the first open source password cracking tool for memory units encrypted with BitLocker

BitCracker: BitLocker meets GPUs

caffe-android-opencl-fp16: Optimised Caffe with OpenCL supporting for less powerful devices such as mobile phones

HG-Caffe: Mobile and Embedded Neural Network GPU (OpenCL) Inference Engine with FP16 Supporting

ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers

ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers

AutoParallel-Fortran: A domain specific, automatically parallelising source-to-source compiler for Fortran-95 that takes scientific Fortran as input and produces parallel Fortran/OpenCL

Towards Automatic Transformation of Legacy Scientific Code into OpenCL for Optimal Performance on FPGAs

gpussjoin: Set Similarity joins in the GPGPU paradigm

Speeding-up the Verification Phase of Set Similarity Joins in the GPGPU paradigm

wav2letter: Facebook AI Research Automatic Speech Recognition Toolkit

wav2letter++: The Fastest Open-source Speech Recognition System

GenASiS_Basics: Object-oriented Utilitarian Functionality for Large-scale Physics Simulations

Targeting GPUs with OpenMP Directives on Summit: A Simple and Effective Fortran Experience

schnaps: a finite element solver

Optimization of a discontinuous finite element solver with OpenCL and StarPU

GPU-benchmarks

Mix-and-Match: A Model-driven Runtime Optimisation Strategy for BFS on GPUs

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2019

May

13-15

Boston, USA

7th International Workshop on OpenCL, 2019

2019

May

13

Boston, MA, USA

Distributed Heterogeneous Programming in C/C++ (DHPCC++), 2019

2019

May

14

Larnaca, Cyprus

3rd International Workshop on Theoretical Approaches to Performance Evaluation, Modeling and Simulation (TAPEMS), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Neural Networks (ICFNN), 2019

2019

July

26-28

Rome, Italy

International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), 2019

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