high performance computing on graphics processing units: hgpu.org

hgpu.org » Applications

AutoMat – Automatic Differentiation for Generalized Standard Materials on GPUs

Johannes Blühdorn, Nicolas R. Gauger, Matthias Kabel

View

Download (PDF)

Tags: Computer science, CUDA, FFT, Mathematical Software, nVidia, nVidia Quadro GV100

June 14, 2020 by hgpu

Neural Architecture Search without Training

Joseph Mellor, Jack Turner, Amos Storkey, Elliot J. Crowley

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package

June 14, 2020 by hgpu

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

Jiajian Xiao, Philipp Andelfinger, Wentong Cai, Paul Richmond, Alois Knoll, David Eckhoff

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 1060, nVidia GeForce GTX 2080 Ti, OpenCL, Package

June 7, 2020 by hgpu

SOFF: An OpenCL High-Level Synthesis Framework for FPGAs

Gangwon Jo, Heehoon Kim, Jeesoo Lee, Jaejin Lee

View

Download (PDF)

Source codes

Tags: Compilers, Computer science, FPGA, OpenCL, Package, Performance

June 7, 2020 by hgpu

Investigating Single Precision Floating General Matrix Multiply in Heterogeneous

Steven Harris

View

Download (PDF)

Tags: ASIC, Computer science, FPGA, Heterogeneous systems, Matrix multiplication, OpenCL, Performance, performance portability, Thesis

June 7, 2020 by hgpu

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Shaohuai Shi, Qiang Wang, Xiaowen Chu

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Package, Sparse matrix, Tesla P100

June 7, 2020 by hgpu

Multi-GPU Performance Optimization of a CFD Code using OpenACC on Different Platforms

Weicheng Xue, Christoper J. Roy

View

Download (PDF)

Tags: cfd, Fluid dynamics, MPI, nVidia, OpenACC, Performance, Tesla C2050, Tesla M2015, Tesla P100, Tesla V100

June 7, 2020 by hgpu

Evaluating the performance of HPC-style SYCL applications

Tom Deakin, Simon McIntosh-Smith

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, Heterogeneous systems, OpenCL, OpenMP, Package, Performance, SYCL

May 31, 2020 by hgpu

Lessons learned in a decade of research software engineering GPU applications

Ben van Werkhoven, Willem Jan Palenstijn, Alessio Sclocco

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, OpenCL, Software Engineering

May 31, 2020 by hgpu

Character-level Transformer-based Neural Machine Translation

Nikolay Banar, Walter Daelemans, Mike Kestemont

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, NLP, nVidia, nVidia GeForce GTX 1080 Ti

May 31, 2020 by hgpu

Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs

Behnam Pourghassemi, Chenghao Zhang, Joo Hwan Lee, Aparna Chandramowlishwaran

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, TensorFlow, Tesla K40

May 31, 2020 by hgpu

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, Young-ri Choi

View

Download (PDF)

Tags: Computer science, CUDA, Data parallelism, Deep learning, GPU cluster, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX Titan V, nVidia GeForce RTX 2060, nVidia Quadro P 4000, nVidia Titan RTX

May 31, 2020 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Applications

AutoMat – Automatic Differentiation for Generalized Standard Materials on GPUs

Neural Architecture Search without Training

OpenABLext: An automatic code generation framework for agent-based simulations on CPU-GPU-FPGA heterogeneous platforms

SOFF: An OpenCL High-Level Synthesis Framework for FPGAs

Investigating Single Precision Floating General Matrix Multiply in Heterogeneous

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Multi-GPU Performance Optimization of a CFD Code using OpenACC on Different Platforms

Evaluating the performance of HPC-style SYCL applications

Lessons learned in a decade of research software engineering GPU applications

Character-level Transformer-based Neural Machine Translation

Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

Recent source codes

XaaS containers

microSYCL: SYCL micro-benchmarks repository

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

Most viewed papers (last 30 days)