high performance computing on graphics processing units: hgpu.org

Programming

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Real-Time Rendering of Molecular Dynamics Simulation Data: A Tutorial

N.Alharbi, M. Chavent, RS. Laramee

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Tags: Computer science, Image generation, Intel HD 6100, Molecular dynamics, OpenCL, OpenGL, Package, Rendering, Tutorial

September 3, 2017 by hgpu

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

Yuriy Kochura, Sergii Stirenko, Oleg Alienin, Michail Novotarskiy, Yuri Gordienko

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Tags: Computer science, CUDA, Deep learning, Image recognition, Java, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Performance, TensorFlow, Tesla K40

September 3, 2017 by hgpu

Accelerate Local Tone Mapping for High Dynamic Range Images Using OpenCL with GPU

Kuo-Feng Liao, Yarsun Hsu

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Tags: Algorithms, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GT 750 M, OpenCL

August 26, 2017 by hgpu

Dynamic Parallelism in GPU Optimized Barnes Hut Trees for Molecular Dynamics Simulations

Melisa Carranza Zuniga

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Tags: Algorithms, Biology, Biomolecules, Biophysics, Chemistry, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 480, Physics, Thesis

August 26, 2017 by hgpu

Large Integer Arithmetic in GPU for Cryptography

Lee Wen Dick

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, Security, Thesis

August 26, 2017 by hgpu

eccCL: parallelized GPU implementation of Ensemble Classifier Chains

Mona Riemenschneider, Alexander Herbst, Ari Rasch, Sergei Gorlatch, Dominik Heider

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Tags: Biology, Computer science, Java, Next-Generation sequencing, nVidia, OpenCL, Package, R, Tesla K20

August 26, 2017 by hgpu

Simulating the Cardinal Movements of Childbirth Using Finite Element Analysis on the Graphics Processing Unit

Zelimkhan Gerikhanov

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Tags: AMD Radeon HD 8970 M, ATI, Biology, Biomechanics, FEM, Finite element method, Medicine, MRI, OpenCL, Thesis

August 17, 2017 by hgpu

Performance Characterization of Multi-threaded Graph Processing Applications on Intel Many-Integrated-Core Architecture

Xu Liu, Langshi Chen, Jesun S. Firoz, Judy Qiu, Lei Jiang

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Tags: Computer science, CUDA, Graph theory, Intel Xeon Phi, nVidia, nVidia GeForce GTX 1070

August 17, 2017 by hgpu

Gpufit: An open-source toolkit for GPU-accelerated curve fitting

Adrian Przybylski, Bjorn Thiel, Jan Keller-Findeisen, Bernd Stock, Mark Bates

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Tags: Biology, Computer science, CUDA, Microscopy, nVidia, nVidia GeForce GTX 1080, Package, Python

August 17, 2017 by hgpu

GARDENIA: A Domain-specific Benchmark Suite for Next-generation Accelerators

Zhen Xu, Xuhao Chen, Jie Shen, Yang Zhang, Cheng Chen, Canqun Yang

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Tags: Benchmarking, Computer science, CUDA, Graph theory, Machine learning, nVidia, Package, Tesla K40

August 17, 2017 by hgpu

Accelerating digital forensic searching through GPGPU parallel processing techniques

Ethan Bayne

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Tags: Computer science, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 970 M, nVidia GeForce GTX 980, nVidia GeForce GTX 980 Ti, OpenCL, Thesis

August 14, 2017 by xhgpu

Using Graph Properties to Speed-up GPU-based Graph Traversal: A Model-driven Approach

Merijn Verstraaten, Ana Lucia Varbanescu, Cees de Laat

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Graph theory, Machine learning, nVidia, nVidia GeForce GTX Titan X, Package

August 8, 2017 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Real-Time Rendering of Molecular Dynamics Simulation Data: A Tutorial

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

Accelerate Local Tone Mapping for High Dynamic Range Images Using OpenCL with GPU

Dynamic Parallelism in GPU Optimized Barnes Hut Trees for Molecular Dynamics Simulations

Large Integer Arithmetic in GPU for Cryptography

eccCL: parallelized GPU implementation of Ensemble Classifier Chains

Simulating the Cardinal Movements of Childbirth Using Finite Element Analysis on the Graphics Processing Unit

Performance Characterization of Multi-threaded Graph Processing Applications on Intel Many-Integrated-Core Architecture

Gpufit: An open-source toolkit for GPU-accelerated curve fitting

GARDENIA: A Domain-specific Benchmark Suite for Next-generation Accelerators

Accelerating digital forensic searching through GPGPU parallel processing techniques

Using Graph Properties to Speed-up GPU-based Graph Traversal: A Model-driven Approach

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)