high performance computing on graphics processing units: hgpu.org

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Comparing CUDA and OpenGL implementations for a Jacobi iteration

Ronan Amorim, Gundolf Haase, Manfred Liebmann, Rodrigo Weber dos Santos

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Tags: Computer science, CUDA, GLSL, nVidia, nVidia GeForce 8600 GT, nVidia GeForce 8800 GT, nVidia GeForce GTX 280, OpenGL, Performance

February 11, 2011 by hgpu

Next-generation acceleration and code optimization for light transport in turbid media using GPUs

Erik Alerstam, William C. Yip Lo, Tianyi D. Han, Jonathan Rose, Stefan Andersson-Engels, Lothar Lilge

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Tags: Biology, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295, nVidia GeForce GTX 480, Optics, Package

February 9, 2011 by hgpu

Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition

Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Juergen Schmidhuber

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Tags: Artificial intelligence, CUDA, Image processing, Image recognition, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX 280

February 9, 2011 by hgpu

Evaluating multi-core platforms for HPC data-intensive kernels

Alexander S. van Amesfoort, Ana Lucia Varbanescu, Henk J. Sips, Rob V. van Nieuwpoort

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Tags: Algorithms, Cell processor, Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Programming techniques, Tesla C1060

February 6, 2011 by hgpu

NeMo: A Platform for Neural Modelling of Spiking Neurons Using GPUs

Andreas Fidjeland, Etienne B. Roesch, Murray P. Shanahan, Wayne Luk

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 280, Package, Tesla C1060

February 5, 2011 by hgpu

QR decomposition on GPUs

Andrew Kerr, Dan Campbell, Mark Richards

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280

February 5, 2011 by hgpu

QUDA programming for staggered quarks

Steven Gottlieb, Guochun Shi, Aaron Torok, Volodymyr Kindratenko

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Tags: CUDA, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Package, Physics, QCD, Tesla C1060, Tesla C2050, Tesla S1070

February 4, 2011 by hgpu

MILC on GPUs

Gushing Shi, Steven Gottlieb, Volodymyr Kindratenko

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Tags: Conjugate gradient solver, CUDA, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce GTX 280, Physics, QCD

February 3, 2011 by hgpu

3D Information Extraction Based on GPU

Ying Zhang

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce 8400 M GS, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, nVidia Quadro FX 3700, Tesla C1060

February 3, 2011 by hgpu

Algebraic Splats Representation for Point Based Models

Naveen Kumar Bolla, P. J. Narayanan

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Tags: Computer science, nVidia, nVidia GeForce 7900 GTX, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Raytracing, Rendering

February 1, 2011 by hgpu

SPH on GPU with CUDA

Alexis Herault, Giuseppe Bilotta, Robert A. Dalrymple

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Tags: CUDA, Fluid dynamics, Fluid simulation, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 280, Package, Particle simulation, Tesla C1060

February 1, 2011 by hgpu

Constraint Fluids on GPU

Martin Nilsson

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Tags: Algorithms, Collision detection, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 280, Thesis

February 1, 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

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high performance computing on graphics processing units: hgpu.org

Comparing CUDA and OpenGL implementations for a Jacobi iteration

Next-generation acceleration and code optimization for light transport in turbid media using GPUs

Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition

Evaluating multi-core platforms for HPC data-intensive kernels

NeMo: A Platform for Neural Modelling of Spiking Neurons Using GPUs

QR decomposition on GPUs

QUDA programming for staggered quarks

MILC on GPUs

3D Information Extraction Based on GPU

Algebraic Splats Representation for Point Based Models

SPH on GPU with CUDA

Constraint Fluids 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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