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gpustats: GPU Library for Statistical Computing in Python

Andrew Cron, Wes McKinney

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Tags: Algorithms, Bayesian, Code generation, CUDA, Mathematics, nVidia, nVidia GeForce GTS 250, OpenCL, Package, Probability, Python, Statistics

October 13, 2011 by hgpu

Python for Development of OpenMP and CUDA Kernels for Multidimensional Data

Zane W. Bell, Greg G. Davidson, Ed D'Azevedo, Thomas M. Evans, Wayne Joubert, John K. Munro, Jr., Dilip R. Patlolla, Bogdan Vacaliuc

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Tags: Boltzmann equation, CUDA, Heterogeneous systems, nVidia, Package, Physics, Presentation, Python, Tesla M2070

October 6, 2011 by hgpu

Democratic Population Decisions Result in Robust Policy-Gradient Learning: A Parametric Study with GPU Simulations

Paul Richmond, Lars Buesing, Michele Giugliano, Eleni Vasilaki

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Tags: Biology, CUDA, Neuroscience, nVidia, nVidia GeForce GTX 480, Package, Python

October 5, 2011 by hgpu

High-Order Discontinuous Galerkin Methods by GPU Metaprogramming

Andreas Klockner, Timothy Warburton, Jan S. Hesthaven

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Tags: CUDA, Differential equations, Mathematics, nVidia, nVidia GeForce GTX 280, Python, Tesla T10

October 5, 2011 by hgpu

A Complete Descritpion of the UnPython and Jit4GPU Framework

Rahul Garg, Jose Nelson Amaral

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Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 5850, ATI Stream, Compilers, Computer science, OpenMP, Optimization, Performance, Python

October 2, 2011 by hgpu

CUDA-level performance with python-level productivity for Gaussian mixture model applications

H. Cook, E. Gonina, S. Kamil, G. Friedland, D. Patterson, A. Fox

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, OpenCL, Python

September 7, 2011 by hgpu

Compiling Python to a hybrid execution environment

Rahul Garg, Jose Nelson Amaral

Tags: ATI, ATI CAL, BLAS, Compilers, Computer science, OpenCL, Python

August 21, 2011 by hgpu

CANSCID-CUDA

Michael Steffen, Veerendra Allada, Phillip Jones and Joseph Zambreno

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Tags: Computer science, CUDA, nVidia, Python, String matching, Tesla

April 8, 2011 by hgpu

PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation

Andreas Klockner, Nicolas Pinto, Yunsup Lee, Bryan Catanzaro, Paul Ivanov, Ahmed Fasih

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Tags: Code generation, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8600 GT, nVidia GeForce 9400 M, nVidia GeForce GTX 295, nVidia GeForce GTX 480, OpenCL, Package, PyOpenCL, Python, Software Engineering, Tesla C1060

March 30, 2011 by hgpu

Fast computing of scattering maps of nanostructures using graphical processing units

Vincent Favre-Nicolin, Johann Coraux, Marie-Ingrid Richard, Hubert Renevier

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Tags: Condensed matter, CUDA, Materials Science, Mesoscale and Nanoscale Physics, nVidia, nVidia GeForce GTX 295, Package, Physics, Python

February 11, 2011 by hgpu

ABC-SysBio–approximate Bayesian computation in Python with GPU support

Juliane Liepe, Chris Barnes, Erika Cule, Kamil Erguler, Paul Kirk, Tina Toni, Michael P. H. Stumpf

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Tags: Bioinformatics, Biology, CUDA, nVidia, Package, Python

November 9, 2010 by hgpu

MDLab: A molecular dynamics simulation prototyping environment

Trevor Cickovski, Santanu Chatterjee, Jacob Wenger, Christopher R. Sweet, Jesus A. Izaguirre

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Tags: Chemistry, Computational chemistry, CUDA, Molecular dynamics, nVidia, Package, Python

November 6, 2010 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

gpustats: GPU Library for Statistical Computing in Python

Python for Development of OpenMP and CUDA Kernels for Multidimensional Data

Democratic Population Decisions Result in Robust Policy-Gradient Learning: A Parametric Study with GPU Simulations

High-Order Discontinuous Galerkin Methods by GPU Metaprogramming

A Complete Descritpion of the UnPython and Jit4GPU Framework

CUDA-level performance with python-level productivity for Gaussian mixture model applications

Compiling Python to a hybrid execution environment

CANSCID-CUDA

PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation

Fast computing of scattering maps of nanostructures using graphical processing units

ABC-SysBio–approximate Bayesian computation in Python with GPU support

MDLab: A molecular dynamics simulation prototyping environment

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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