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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

Introduction to GPU Programming with GLSL

R. Marroquim, A. Maximo

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Tags: ATI, Computer science, GLSL, High-level Languages, nVidia, OpenGL, Package, Rendering, Tutorial

March 25, 2011 by hgpu

Language virtualization for heterogeneous parallel computing

Hassan Chafi, Zach DeVito, Adriaan Moors, Tiark Rompf, Arvind K. Sujeeth, Pat Hanrahan, Martin Odersky, Kunle Olukotun

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Tags: Algorithms, Code generation, Computer science, Heterogeneous systems, High-level Languages

March 17, 2011 by hgpu

A Domain-Specific Approach To Heterogeneous Parallelism

Hassan Chafi, Arvind K. Sujeeth, Kevin J. Brown, HyoukJoong Lee, Anand R. Atreya, Kunle Olukotun

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Tags: Code generation, Compilers, Computer science, CUDA, Heterogeneous systems, High-level Languages, nVidia, nVidia GeForce GTX 275

February 27, 2011 by hgpu

Many-core GPU computing with NVIDIA CUDA

Mark Harris

Tags: Computer science, CUDA, High-level Languages, nVidia, Programming Languages

February 24, 2011 by hgpu

Data parallel loop statement extension to CUDA: GpuC

Zeki Bozkus, Rajeev Thakur, William Gropp, Ewing Lusk

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, OpenMP, Optimization

February 21, 2011 by hgpu

MCUDA: An Efficient Implementation of CUDA Kernels on Multi-cores

John A. Stratton, Sam S. Stone, and Wen-mei W. Hwu

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, Package

February 20, 2011 by hgpu

Compiler Support for High-level GPU Programming

Tianyi David Han and Tarek S. Abdelrahman

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8800 GTX, Package

February 19, 2011 by hgpu

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

Pei-Hung Lin, Jagan Jayaraj, and Paul R. Woodward

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Tags: Cell processor, Code generation, Compilers, Computer science, CUDA, Fortran, High-level Languages, Memory model, nVidia, Tesla C1060

February 17, 2011 by hgpu

GpuC: Data parallel language extension to CUDA

Zeki Bozkus

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Tags: Compilers, Computer science, CUDA, High-level Languages, nVidia, OpenMP, Optimization

February 16, 2011 by hgpu

Copperhead: Compiling an embedded data parallel language

Bryan Catanzaro, Michael Garland and Kurt Keutzer

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Tags: Code generation, Compilers, Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 480, Package

February 13, 2011 by hgpu

Compiling an Array Language to a Graphics Processor

Bradford Larsen

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce 8800 GT, Thesis

February 11, 2011 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

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

Introduction to GPU Programming with GLSL

Language virtualization for heterogeneous parallel computing

A Domain-Specific Approach To Heterogeneous Parallelism

Many-core GPU computing with NVIDIA CUDA

Data parallel loop statement extension to CUDA: GpuC

MCUDA: An Efficient Implementation of CUDA Kernels on Multi-cores

Compiler Support for High-level GPU Programming

A Strategy for Automatically Generating High Performance CUDA Code for a GPU Accelerator from a Specialized Fortran Code Expression

GpuC: Data parallel language extension to CUDA

Copperhead: Compiling an embedded data parallel language

Compiling an Array Language to a Graphics Processor

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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