high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia

Comparison of HPC Architectures for Computing All-Pairs Shortest Paths. Intel Xeon Phi KNL vs NVIDIA Pascal

Manuel Costanzo, Enzo Rucci, Ulises Costi, Franco Chichizola, Marcelo Naiouf

View

Download (PDF)

Source codes

Tags: Algorithms, All-Pairs Distance, Computer science, CUDA, HPC, Intel, Intel Xeon Phi, nVidia, nVidia GeForce GTX Titan X, Package

May 23, 2021 by hgpu

NPBench: A Benchmarking Suite for High-Performance NumPy

Alexandros Nikolaos Ziogas, Tal Ben-Nun, Timo Schneider, Torsten Hoefler

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, HPC, Linear Algebra, nVidia, Package, Performance, Python, Tesla V100

May 16, 2021 by hgpu

Performance Assessment of using OpenCL on FPGA Systems for ODE Solvers

Patrick Tobias Haft

View

Download (PDF)

Tags: Code generation, Computational biology, Computer science, Differential equations, FPGA, nVidia, nVidia GeForce GTX 980 Ti, ODEs, OpenCL, Optimization, Ordinary differential equations, Tesla K20, Thesis

May 16, 2021 by hgpu

Raster Time Series: Learning and Processing

Johannes Drönner

View

Download (PDF)

Tags: AMD Radeon RX 580, ATI, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Sensing, Thesis, Visualization

May 16, 2021 by hgpu

PeriPy – A High Performance OpenCL Peridynamics Package

B. Boys, T. J. Dodwell, M. Hobbs, M. Girolami

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, Heterogeneous systems, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Python

May 16, 2021 by hgpu

A fluid simulation system based on the MPS method

André Luiz Buarque Vieira e Silva, Caio José dos Santos Brito, Francisco Paulo Magalhães Simões, Veronica Teichrieb

View

Download (PDF)

Source codes

Tags: cfd, CUDA, Fluid dynamics, Fluid simulation, nVidia, nVidia GeForce GTX 1080 Ti, OpenMP, Package

May 9, 2021 by hgpu

Irregularity Mitigation and Portability Abstractions for Accelerated Sparse Matrix Factorization

Daniel Thürck

View

Download (PDF)

Tags: Algorithms, Computer science, Factorization, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan V, OpenCL, Sparse matrix, Thesis

May 9, 2021 by hgpu

DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning

Sheldon Fung, Xuequan Lu, Chao Zhang, Chang-Tsun Li

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce RTX 2080 Ti, PyTorch

May 2, 2021 by hgpu

Enabling Energy-Efficient DNN Training on Hybrid GPU-FPGA Accelerators

Xin He, Jiawen Liu, Zhen Xie, Hao Chen, Guoyang Chen, Weifeng Zhang, Dong Li

View

Download (PDF)

Tags: Computer science, Deep learning, Design space exploration, FPGA, Hybrid computing, nVidia, OpenCL, Tesla V100

May 2, 2021 by hgpu

Performance analysis and optimization of highly diverging algorithms on GPUs

Hendrik Schwanekamp

View

Download (PDF)

Tags: CUDA, nVidia, nVidia A100, nVidia GeForce GTX 1080, Performance, Physics

May 2, 2021 by hgpu

Easy and Efficient Transformer: Scalable Inference Solution For large NLP mode

Gongzheng li, Yadong Xi, Jingzhen Ding, Duan Wang, Bai Liu, Changjie Fan, Xiaoxi Mao, Zeng Zhao

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, NLP, nVidia, nVidia GeForce RTX 2080 Ti, Package, Tesla A100

May 2, 2021 by hgpu

tcFFT: Accelerating Half-Precision FFT through Tensor Cores

Binrui Li, Shenggan Cheng, James Lin

View

Download (PDF)

Tags: Computer science, FFT, Mixed precision, nVidia, nVidia DGX-2, nVidia DGX-A100, Tesla V100

May 2, 2021 by hgpu

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Comparison of HPC Architectures for Computing All-Pairs Shortest Paths. Intel Xeon Phi KNL vs NVIDIA Pascal

NPBench: A Benchmarking Suite for High-Performance NumPy

Performance Assessment of using OpenCL on FPGA Systems for ODE Solvers

Raster Time Series: Learning and Processing

PeriPy – A High Performance OpenCL Peridynamics Package

A fluid simulation system based on the MPS method

Irregularity Mitigation and Portability Abstractions for Accelerated Sparse Matrix Factorization

DeepfakeUCL: Deepfake Detection via Unsupervised Contrastive Learning

Enabling Energy-Efficient DNN Training on Hybrid GPU-FPGA Accelerators

Performance analysis and optimization of highly diverging algorithms on GPUs

Easy and Efficient Transformer: Scalable Inference Solution For large NLP mode

tcFFT: Accelerating Half-Precision FFT through Tensor Cores

Recent source codes

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

AutoDock for GPUs and other accelerators

spotPython: Sequential Parameter Optimization in Python

Test benchmarks for CELERITY

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Redwood heterogeneous framework

Most viewed papers (last 30 days)