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Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

Mark Silberstein, Assaf Schuster, John D. Owens

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Optimization, Package, Performance, Probability, Task scheduling, Tesla C2050

October 5, 2011 by hgpu

Parallel Direct Simulation Monte Carlo Computation Using CUDA on GPUs

C.-C. Su, C.-W. Hsieh, M. R. Smith, M. C. Jermy, and J.-S. Wua

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Tags: Benchmarking, Computer science, CUDA, Monte Carlo simulation, nVidia, Tesla C1060

October 5, 2011 by hgpu

A Framework for Automated Performance Tuning and Code Verification on GPU Computing Platforms

Allison S. Gehrke, Ilkyeun Ra, Daniel A. Connors

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Tags: Code generation, Computer science, Numerical simulation, nVidia, Optimization, Performance, PTX

October 5, 2011 by hgpu

Flexible, high performance convolutional neural networks for image classification

Dan C. Ciresan, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber

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Tags: Benchmarking, Computer science, CUDA, Image processing, Neural networks, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, nVidia GeForce GTX 580

October 5, 2011 by hgpu

Architecture-Aware Optimization on a 1600-core Graphics Processor

Mayank Daga, Thomas R.W. Scogland, Wu-chun Feng

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Tags: ATI, ATI Radeon HD 5870, Cluster computing, Computer science, CUDA, Molecular modeling, nVidia, nVidia GeForce GTX 280, OpenCL, Optimization, Tesla C2050

October 4, 2011 by hgpu

Fine-grained Parallel ILU Preconditioners with Fill-ins for Multi-core CPUs and GPUs

Vincent Heuveline, Dimitar Lukarski and Jan-philipp Weiss

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Tags: Algorithms, Computer science, FEM, Finite element method, Heterogeneous systems, Numerical simulation, OpenCL, Package, Partial differential equations, PDEs

October 4, 2011 by hgpu

GPU Algorithms for Diamond-based Multiresolution Terrain Processing

M. Adil Yalcin, Kenneth Weiss, Leila De Floriani

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Tags: 3D Graphics and Realism, Algorithms, Computer science, nVidia, nVidia GeForce GTX 260, OpenCL, OpenGL, Rendering, Tessellation, Visualization

October 4, 2011 by hgpu

Berkeley Dwarfs on CUDA

Paul Springer

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Tesla C2050, Thesis

October 4, 2011 by hgpu

Comparing Parallel Simulation of Social Agents using Cilk and OpenCL

Dominik Moser, Andreas Riener, Kashif Zia, Alois Ferscha

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Tags: Computer science, OpenCL, Sensing

October 4, 2011 by hgpu

Optimization of the Gaussian Mixture Model Evaluation on GPU

Jan Vanek, Jan Trmal, Josef V. Psutka, Josef Psutka

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 240, nVidia GeForce GTX 260, OpenCL, Optimization, Speech recognition

October 4, 2011 by hgpu

Acceleration of Radiance for Lighting Simulation by Using Parallel Computing with OpenCL

W. Zuo, A. McNeil, M. Wetter, E.S. Lee

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Tags: Algorithms, Computer science, Matrix multiplication, nVidia, nVidia GeForce GTX 460, OpenCL, Raytracing

October 4, 2011 by hgpu

Tranformation of CPU-based Applications To Leverage on Graphics Processors using CUDA

Fawnizu Azmadi Hussin, Anas Mohd Nazlee, Noohul Basheer Zain Ali

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Tags: Algorithms, Computer science, CUDA, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 260, Performance, Programming techniques, Security

October 3, 2011 by hgpu

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

LLOR: Automatic Repair of OpenMP Programs

LLOR: Automated Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

Unified schemes for directive-based GPU offloading

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

See all packages

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high performance computing on graphics processing units: hgpu.org

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

Parallel Direct Simulation Monte Carlo Computation Using CUDA on GPUs

A Framework for Automated Performance Tuning and Code Verification on GPU Computing Platforms

Flexible, high performance convolutional neural networks for image classification

Architecture-Aware Optimization on a 1600-core Graphics Processor

Fine-grained Parallel ILU Preconditioners with Fill-ins for Multi-core CPUs and GPUs

GPU Algorithms for Diamond-based Multiresolution Terrain Processing

Berkeley Dwarfs on CUDA

Comparing Parallel Simulation of Social Agents using Cilk and OpenCL

Optimization of the Gaussian Mixture Model Evaluation on GPU

Acceleration of Radiance for Lighting Simulation by Using Parallel Computing with OpenCL

Tranformation of CPU-based Applications To Leverage on Graphics Processors using CUDA

Recent source codes

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

tdg-benchs: benchmarks used to test the performance of taskgraph

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

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