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On Simplifying and Optimizing Programs for Heterogeneous Computing Systems

Ivan Grasso

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Tags: AMD FirePro S9000, ATI, ATI Radeon HD 5870, Compilers, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Tesla K20, Thesis

July 25, 2017 by hgpu

OpenCL Library for Parallel Graph Search Algorithms

Christian Fischer

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Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, OpenCL, Path problems, Thesis

July 25, 2017 by hgpu

GPU-acceleration for Large-scale Tree Boosting

Huan Zhang, Si Si, Cho-Jui Hsieh

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Tags: Algorithms, AMD Radeon RX 480, ATI, Computer science, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package

July 2, 2017 by hgpu

Scalar collapse in AdS with an OpenCL open source code

Steven L. Liebling, Gaurav Khanna

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Tags: AMD Radeon R9 295X2, ATI, General Relativity and Quantum Cosmology, Heterogeneous systems, High Energy Physics - Theory, nVidia, OpenCL, Package, Physics, Tesla K40

June 25, 2017 by hgpu

Efficient OpenCL-based concurrent tasks offloading on accelerators

A.J. Lazaro-Munoz, J.M. Gonzalez-Linares, J. Gomez-Luna, N. Guil

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Tags: AMD Radeon R9, ATI, Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Task scheduling, Tesla K20

June 17, 2017 by hgpu

Neneta: Heterogeneous Computing Complex-Valued Neural Network Framework

Vladimir Lekic, Zdenka Babic

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Tags: ATI, ATI Radeon HD 5770, Computer science, Deep learning, Heterogeneous systems, Neural networks, OpenCL

June 5, 2017 by hgpu

Accelerating Discrete Wavelet Transforms on GPUs

David Barina, Michal Kula, Michal Matysek, Pavel Zemcik

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Tags: ATI, ATI Radeon HD 6970, Computer science, Discrete Wavelet Transform, HLSL, Image processing, nVidia, nVidia GeForce GTX Titan X, OpenCL, Pixel shaders

May 24, 2017 by hgpu

Implementing Efficient, Portable Computations for Machine Learning

Matthew Walter Moskewicz

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Tags: Algorithms, AMD R9 Nano, ATI, Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Thesis

May 24, 2017 by hgpu

GPU System Call

Jan Vesely, Arkaprava Basu, Abhishek Bhattacharjee, Gabriel Loh, Mark Oskin, Steven K. Reinhardt

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Tags: ATI, C++ AMP, Computer science, Operating systems

May 22, 2017 by hgpu

CLBlast: A Tuned OpenCL BLAS Library

Cedric Nugteren

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Tags: AMD Radeon R9 M370X, ARM, ATI, BLAS, Computer science, Intel HD 5100, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX Titan X, OpenCL, Package

May 18, 2017 by hgpu

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

Konstantinos Krommydas

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Tags: ATI, ATI Radeon HD 6550, ATI Radeon HD 7660, ATI Radeon HD 7970, Code generation, Compilers, Computer science, FPGA, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Package, Performance, Tesla K20, Thesis

May 9, 2017 by hgpu

Accelerating Discrete Wavelet Transforms on Parallel Architectures

David Barina, Michal Kula, Michal Matysek, Pavel Zemcik

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Tags: Algorithms, ATI, ATI Radeon HD 6970, Discrete Wavelet Transform, Image processing, nVidia, nVidia GeForce GTX Titan X, OpenCL, OpenGL, Performance, Pixel shaders

April 30, 2017 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

On Simplifying and Optimizing Programs for Heterogeneous Computing Systems

OpenCL Library for Parallel Graph Search Algorithms

GPU-acceleration for Large-scale Tree Boosting

Scalar collapse in AdS with an OpenCL open source code

Efficient OpenCL-based concurrent tasks offloading on accelerators

Neneta: Heterogeneous Computing Complex-Valued Neural Network Framework

Accelerating Discrete Wavelet Transforms on GPUs

Implementing Efficient, Portable Computations for Machine Learning

GPU System Call

CLBlast: A Tuned OpenCL BLAS Library

Towards Enhancing Performance, Programmability, and Portability in Heterogeneous Computing

Accelerating Discrete Wavelet Transforms on Parallel Architectures

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

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