high performance computing on graphics processing units: hgpu.org

hgpu.org » Benchmarking

AI Benchmark: Running Deep Neural Networks on Android Smartphones

Andrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, Luc Van Gool

View

Download (PDF)

Tags: Android, Artificial intelligence, Benchmarking, Computer science, Deep learning, Neural networks

October 21, 2018 by hgpu

HSTREAM: A directive-based language extension for heterogeneous stream computing

Suejb Memeti, Sabri Pllana

View

Download (PDF)

Tags: Benchmarking, Compilers, Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia Quadro P 4000, OpenMP

October 6, 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

View

Download (PDF)

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

September 23, 2018 by hgpu

Benchmarking and Optimization of Gradient Boosted Decision Tree Algorithms

Andreea Anghel, Nikolaos Papandreou, Thomas Parnell, Alessandro De Palma, Haralampos Pozidis

View

Download (PDF)

Tags: Algorithms, Bayesian, Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 1080 Ti

September 16, 2018 by hgpu

Optimizing Web Virtual Reality

Rabimba Karanjai

View

Download (PDF)

Tags: Benchmarking, Computer science, Java, OpenGL, Rendering, Thesis, Virtual reality, WebGL

August 26, 2018 by hgpu

FluidFFT: common API (C++ and Python) for Fast Fourier Transform HPC libraries

Ashwin Vishnu Mohanan, Cyrille Bonamy, Pierre Augier

View

Download (PDF)

Source codes

Tags: Benchmarking, cfd, CUDA, FFT, Fluid dynamics, MPI, Package, Python

July 7, 2018 by hgpu

NengoDL: Combining deep learning and neuromorphic modelling methods

Daniel Rasmussen

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Python

June 2, 2018 by hgpu

Generic System Calls for GPUs

Jan Vesely, Arkaprava Basu, Abhishek Bhattacharjee, Gabriel H. Loh, Mark Oskin, Steven K. Reinhardt

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, HSA, Package

May 20, 2018 by hgpu

Dwarfs on Accelerators: Enhancing OpenCL Benchmarking for Heterogeneous Computing Architectures

Beau Johnston, Josh Milthorpe

View

Download (PDF)

Tags: AMD FirePro S9150, AMD Radeon HD 7970, AMD Radeon R9 290X, AMD Radeon R9 Fury X, AMD Radeon RX 480, ATI, Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1080, OpenCL, Tesla K20, Tesla K40

May 12, 2018 by hgpu

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Shaohuai Shi, Qiang Wang, Xiaowen Chu, Bo Li

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, Performance, Tesla K80

May 12, 2018 by hgpu

Fast Simulation of Large-Scale Floods Based on GPU Parallel Computing

Qiang Liu, Yi Qin, Guodong Li

View

Download (PDF)

Tags: Benchmarking, Earth and Space Sciences, Numerical simulation, nVidia, OpenACC, Tesla K20

May 5, 2018 by hgpu

Automatic generation of CUDA code performing tensor manipulations using C++ expression templates

Adam G.M. Lewis, Harald P. Pfeiffer

View

Download (PDF)

Tags: Benchmarking, Code generation, Computer science, CUDA, General Relativity and Quantum Cosmology, nVidia, Tesla K80, Tesla M2090, Tesla P100

April 28, 2018 by hgpu

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

Engineering Supercomputing Platforms for Biomolecular Applications

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

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

Can Large Language Models Predict Parallel Code Performance?

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

AI Benchmark: Running Deep Neural Networks on Android Smartphones

HSTREAM: A directive-based language extension for heterogeneous stream computing

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

Benchmarking and Optimization of Gradient Boosted Decision Tree Algorithms

Optimizing Web Virtual Reality

FluidFFT: common API (C++ and Python) for Fast Fourier Transform HPC libraries

NengoDL: Combining deep learning and neuromorphic modelling methods

Generic System Calls for GPUs

Dwarfs on Accelerators: Enhancing OpenCL Benchmarking for Heterogeneous Computing Architectures

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Fast Simulation of Large-Scale Floods Based on GPU Parallel Computing

Automatic generation of CUDA code performing tensor manipulations using C++ expression templates

Recent source codes

hpcbench: A set of benchmarking utilities for biomolecular simulation tools

HPCTransCompile: An AI Compiler Generated Dataset for High-Performance CUDA Transpilation and LLM Preliminary Exploration

chemtrain: Training Molecular Dynamics Potentials in JAX

microSYCL: SYCL micro-benchmarks repository

XaaS containers

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

SYCL Container

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

Can Large Language Models Predict Parallel Code Performance?

Most viewed papers (last 30 days)