nVidia GeForce GTX 280 | Page 2

Trevor Bekolay, James Bergstra, Eric Hunsberger, Travis DeWolf, Terrence C. Stewart, Daniel Rasmussen, Xuan Choo, Aaron Russell Voelker, Chris Eliasmith

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Source codes

Tags: ATI, ATI Radeon HD 7970, Biology, Neuroscience, nVidia, nVidia GeForce GTX 280, OpenCL, Package, PyOpenCL, Python

January 9, 2014 by hgpu

Developing a High Performance Software Library with MPI and CUDA for Matrix Computations

Bogdan Oancea, Tudorel Andrei

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Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, Memory model, MPI, nVidia, nVidia GeForce GTX 280

December 29, 2013 by hgpu

Multilayered Abstractions for Partial Differential Equations

Graham Robert Markall

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Code generation, CUDA, Differential equations, FEM, Finite element method, Mathematics, MPI, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, OpenCL, Partial differential equations, PDEs, Software Engineering, Thesis

December 11, 2013 by hgpu

Runtime Support toward Transparent Memory Access in GPU-accelerated Heterogeneous Systems

Feng Ji

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Tags: Computer science, Hadoop, Heterogeneous systems, MapReduce, MPI, nVidia, nVidia GeForce GTX 280, Tesla M2070, Thesis

December 11, 2013 by hgpu

A GPU-Accelerated Framework for Image Processing and Computer Vision

Ashwini.A.Patil, Pankaja.A.Shahapure

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Tags: Computer vision, CUDA, GLSL, Image processing, nVidia, nVidia GeForce GTX 280, OpenCV

December 11, 2013 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

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high performance computing on graphics processing units: hgpu.org

Industrial Robot Collision Handling in Harsh Environments

Brain perfusion imaging: performance and accuracy

Fast Burrows Wheeler Compression Using CPU and GPU

GPU-based high-performance computing for radiation therapy

Auto-Generation and Auto-Tuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

Scheduling on Manycore and Heterogeneous Graphics Processors

Nengo: a Python tool for building large-scale functional brain models

Developing a High Performance Software Library with MPI and CUDA for Matrix Computations

Multilayered Abstractions for Partial Differential Equations

Runtime Support toward Transparent Memory Access in GPU-accelerated Heterogeneous Systems

A GPU-Accelerated Framework for Image Processing and Computer Vision

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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