high performance computing on graphics processing units: hgpu.org

James Bergstra, Olivier Breuleux, Frederic Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley, Yoshua Bengio

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Tags: Algorithms, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 285, Package, Python

December 12, 2011 by hgpu

A Common GPU n-Dimensional Array for Python and C

Frederic Bastien, Arnaud Bergeron, Pascal Vincent, Yoshua Bengio

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Python

December 12, 2011 by hgpu

Bringing Parallel Performance to Python with Domain-Specific Selective Embedded Just-in-Time Specialization

Shoaib Kamil, Derrick Coetzee, Armando Fox

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, Python

December 12, 2011 by hgpu

Self-Supervised Clustering for Codebook Construction: An Application to Object Localization

Arturo Ribes, Senshan Ji, Arnau Ramisa, Ramon Lopez De Mantaras

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Tags: Clustering, Computer science, Computer vision, CUDA, nVidia, SIFT

December 11, 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

Collision-Driven Volumetric Deformation on the GPU

Divergence Analysis with Affine Constraints

Graphics Processing Units: More Than the Pathway to Realistic Video-Games

Design and study of a massively multi threaded shared memory architecture

A Rigid Body Physics Engine for Interactive Applications

Parallel Evaluation of a Spatial Traversability Cost Function on GPU for Efficient Path Planning

GPU Programming in a High Level Language: Compiling X10 to CUDA

A fast and intuitive visual programming language (VPL) for constructing Computer Vision and Image processing systems on GPUs

Theano: A CPU and GPU Math Compiler in Python

A Common GPU n-Dimensional Array for Python and C

Bringing Parallel Performance to Python with Domain-Specific Selective Embedded Just-in-Time Specialization

Self-Supervised Clustering for Codebook Construction: An Application to Object Localization

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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