high performance computing on graphics processing units: hgpu.org

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Static GPU threads and an improved scan algorithm

Jens Breitbart

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 280, OpenCL, Performance, Presentation, Programming techniques

October 6, 2011 by hgpu

Profiling Heterogeneous Multi-GPU Systems to Accelerate Cortically Inspired Learning Algorithms

Andrew Nere, Atif Hashmi, Mikko Lipasti

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Image recognition, Neural networks, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, Optimization, Tesla C2050

October 5, 2011 by hgpu

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

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Static GPU threads and an improved scan algorithm

Profiling Heterogeneous Multi-GPU Systems to Accelerate Cortically Inspired Learning Algorithms

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

Recent source codes

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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