high performance computing on graphics processing units: hgpu.org

hgpu.org » Optimization

GPU-Optimized Molecular Dynamics Simulations

Tyson J. Lipscomb

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Tags: Biology, Biomolecules, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Optimization, Pseudo-random number generators, Review, RNA, Tesla C2070, Thesis

July 8, 2012 by hgpu

Automatic Optimization of In-Flight Memory Transactions for GPU Accelerators based on a Domain-Specific Language for Medical Imaging

Richard Membarth, Frank Hannig, Jurgen Teich, Mario Korner, Wieland Eckert

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Tags: Code generation, CUDA, Image processing, nVidia, OpenCL, Optimization, Package, Tesla C2050

July 3, 2012 by hgpu

Acceleration of bilateral filtering algorithm for manycore and multicore architectures

Dinesh Agarwal, Sami Wilf, Abinashi Dhungel, Sushil K. Prasad

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Tags: Algorithms, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX 280, Optimization, Package

July 2, 2012 by hgpu

An Optimized Large-Scale Hybrid DGEMM Design for CPUs and ATI GPUs

Jiajia Li, Xingjian Li, Guangming Tan, Mingyu Chen, Ninghui Sun

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Tags: Algorithms, ATI, ATI CAL, ATI Radeon HD 5970, ATI Stream, Computer science, Heterogeneous systems, Matrix multiplication, Optimization, Package

June 29, 2012 by hgpu

Software Performance Analysis with Parallel Programming Approaches

Suneeta H. Angadi, G. T. Raju, Abhishek B

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Tags: Computer science, Matrix multiplication, OpenCL, OpenMP, Optimization, Performance

June 27, 2012 by hgpu

GPU Implementation of the Branch and Bound method for knapsack problems

Mohamed Esseghir Lalami, Didier El-Baz

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Tags: Computer science, CUDA, Hybrid computing, nVidia, Optimization, Tesla C2050

June 25, 2012 by hgpu

An Adaptative Multi-GPU based Branch-and-Bound. A Case Study: the Flow-Shop Scheduling Problem

Imen Chakroun, Nouredine Melab

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Tags: Computer science, CUDA, GPU cluster, nVidia, Optimization, Tesla S1070

June 25, 2012 by hgpu

Parallel Neural Network Training with OpenCL

Nenad Krpan, Domagoj Jakobovic

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Tags: Algorithms, Computer science, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTS 250, OpenCL, Optimization

June 23, 2012 by hgpu

GPU Accelerated Greedy Algorithms for Compressed Sensing

Jeffrey D. Blanchard, Jared Tanner

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Tags: Algorithms, Computer science, CUDA, nVidia, Optimization, Sensing, Tesla C2050

June 20, 2012 by hgpu

Parallel Rendering on Hybrid Multi-GPU Clusters

S. Eilemann, A. Bilgili, M. Abdellah, J. Hernando, M. Makhinya, R. Pajarola, F. Schurmann

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Tags: 3D Graphics and Realism, Computer science, GPU cluster, nVidia, nVidia GeForce GTX 580, OpenGL, Optimization, Rendering, Visualization

June 19, 2012 by hgpu

E-MOGA: A General Purpose Platform for Multi Objective Genetic Algorithm running on CUDA

Sami M. Salamin, Mohammed Aldasht, Hashem Tamimi

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 9500 GTX, Optimization

June 16, 2012 by hgpu

Large-scale Nanostructure Simulations from X-ray Scattering Data On Graphics Processor Clusters

Abhinav Sarje, Jack Pien, Xiaoye S. Li, Elaine Chan, Slim Chourou, Alexander Hexemer, Arthur Scholz and Edward Kramer

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Tags: Algorithms, CUDA, GPU cluster, Monte Carlo simulation, MPI, nVidia, Optimization, Physics, Tesla C2050, Tesla M2090

May 30, 2012 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

GPU-Optimized Molecular Dynamics Simulations

Automatic Optimization of In-Flight Memory Transactions for GPU Accelerators based on a Domain-Specific Language for Medical Imaging

Acceleration of bilateral filtering algorithm for manycore and multicore architectures

An Optimized Large-Scale Hybrid DGEMM Design for CPUs and ATI GPUs

Software Performance Analysis with Parallel Programming Approaches

GPU Implementation of the Branch and Bound method for knapsack problems

An Adaptative Multi-GPU based Branch-and-Bound. A Case Study: the Flow-Shop Scheduling Problem

Parallel Neural Network Training with OpenCL

GPU Accelerated Greedy Algorithms for Compressed Sensing

Parallel Rendering on Hybrid Multi-GPU Clusters

E-MOGA: A General Purpose Platform for Multi Objective Genetic Algorithm running on CUDA

Large-scale Nanostructure Simulations from X-ray Scattering Data On Graphics Processor Clusters

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)