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Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

Beau Johnston

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Tags: Algorithms, AMD Radeon RX 480, ATI Radeon HD 7970, Benchmarking, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, Tesla K20, Tesla K40, Thesis

September 15, 2019 by hgpu

Principles, Techniques, and Tools for Explicit and Automatic Parallelization

Nico Reissmann

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Tags: Computer science, CUDA, nVidia, OpenMP, Package, Performance, performance portability, Tesla K20, Thesis

May 5, 2019 by hgpu

Transparent Acceleration for Heterogeneous Platforms With Compilation to OpenCL

Heinrich Riebler, Gavin Vaz, Tobias Kenter, Christian Plessl

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Tags: Compilers, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Tesla K20

May 1, 2019 by hgpu

Hybrid CPU-GPU execution support in the skeleton programming framework SkePU

Tomas Oehberg, August Ernstsson, Christoph Kessler

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Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, MapReduce, nVidia, OpenCL, Package, Tesla K20

March 31, 2019 by hgpu

Using Compiler Directives for Performance Portability in Scientific Computing: Kernels from Molecular Simulation

Ada Sedova, Andreas Tillack, Arnold Tharrington

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Tags: Chemistry, Molecular dynamics, Molecular simulation, nVidia, OpenACC, performance portability, Physics, Tesla K20

March 3, 2019 by hgpu

Auto-tuned OpenCL kernel co-execution in OmpSs for heterogeneous systems

B. P'erez, E. Stafford, J. L. Bosque, R. Beivide, S. Mateo, X. Teruel, X. Martorell, E. Ayguad'e

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Tags: Computer science, Heterogeneous systems, nVidia, OmpSs, OpenCL, Tesla K20

January 13, 2019 by hgpu

On Runtime Systems for Task-based Programming on Heterogeneous Platforms

Samuel Thibault

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Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia Quadro FX 5800, OpenCL, Operating systems, StarPU, Task scheduling, Tesla C2050, Tesla K20, Tesla M2075, Thesis

December 23, 2018 by hgpu

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

High Performance Portable Tsunami Simulations on Many-core CPU, GPU, and FPGA

Fumiya Kono

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Tags: AMD FirePro W8100, AMD FirePro W9100, AMD Radeon R9 280X, ATI, CUDA, Earth and Space Sciences, FPGA, Intel Xeon Phi, Numerical simulation, nVidia, OpenCL, Tesla K20, Thesis

December 9, 2018 by hgpu

Dense and sparse parallel linear algebra algorithms on graphics processing units

Alejandro Lamas Davina

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, MPI, nVidia, Sparse, Tesla K20, Tesla M2090, Thesis

November 25, 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

Evaluating Performance Portability of Accelerator Programming Models using SPEC ACCEL 1.2 Benchmarks

Swen Boehm, Swaroop Pophale, Veronica G. Vergara Larrea, Oscar Hernandez

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenACC, OpenCL, OpenMP, performance portability, Tesla K20, Tesla V100

September 23, 2018 by hgpu

Watershed analysis algorithm implementation in CUDA and OpenACC

Parallelizing Multiple Flow Accumulation Algorithm using CUDA and OpenACC

OpenCL Performance Prediction using Architecture-Independent Features

Characterizing and Predicting Scientific Workloads for Heterogeneous Computing Systems

PySPH: a Python-based SPH framework

PySPH: a Python-based framework for smoothed particle hydrodynamics

Efficient Interleaved Batch Matrix Solvers for CUDA

Efficient Interleaved Batch Matrix Solvers for CUDA

ArborX: Performance-portable geometric search library

ArborX: A Performance Portable Search Library

FlowSeq: Generative Flow based Sequence-to-Sequence Toolkit

FlowSeq: Non-Autoregressive Conditional Sequence Generation with Generative Flow

Compositional Deep Learning in Futhark

Compositional Deep Learning in Futhark

MLPerf Benchmark Suite

Demystifying the MLPerf Benchmark Suite

stdgpu: Efficient STL-like Data Structures on the GPU

stdgpu: Efficient STL-like Data Structures on the GPU

ikra-cpp: C++ Library for Object-oriented Programming with Structure of Arrays Layout

Memory-Efficient Object-Oriented Programming 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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