high performance computing on graphics processing units: hgpu.org

Xavier Andrade, Joseba Alberdi-Rodriguez, David A. Strubbe, Micael J. T. Oliveira, Fernando Nogueira, Alberto Castro, Javier Muguerza, Agustin Arruabarrena, Steven G. Louie, Alan Aspuru-Guzik, Angel Rubio, Miguel A. L. Marques

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Tags: ATI, ATI Radeon HD 5870, Density-functional theory, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Physics

April 12, 2012 by hgpu

Efficient computational noise in GLSL

Ian McEwan, David Sheets, Stefan Gustavson, Mark Richardson

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Tags: 3D Graphics and Realism, ATI, ATI Radeon HD 3650, ATI Radeon HD 4850, ATI Radeon HD 5870, Computer science, GLSL, nVidia, nVidia GeForce 7600 GS, nVidia GeForce GTX 260, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenGL, Package

April 9, 2012 by hgpu

OpenCL framework for a CPU, GPU, and FPGA Platform

Taneem Ahmed

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Tags: ATI, ATI Radeon HD 5450, Computer science, FPGA, Heterogeneous systems, OpenCL, Thesis

April 6, 2012 by hgpu

Multicore Processing for Clustering Algorithms

RekhanshRao, Kapil Kumar Nagwanshi, SipiDubey

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Clustering, Computer science, Data mining, nVidia, OpenCL

March 29, 2012 by hgpu

Dynamic Translation of Runtime Environments for Heterogeneous Computing

Rodrigo Dominguez

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Design space exploration, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, OpenCL, Thesis

March 27, 2012 by hgpu

GPU Accelerated Computation of Fast Spectral Transforms

Gajic Dusan B., Stankovic Radomir S.

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Tags: Algorithms, ATI, ATI Radeon HD 5650, Computer science, FFT, OpenCL

March 12, 2012 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

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high performance computing on graphics processing units: hgpu.org

Comparison of Different Parallel Implementaions of the 2+1-Dimensional KPZ Model and the 3-Dimensional KMC Model

Multicore Processing for Classification and Clustering Algorithms

Algorithm Construction for GPGPU

MetaCL – A Model-Based Approach to Programming Heterogeneous Architectures Using OpenCL

Auto-tuning interactive ray tracing using an analytical GPU architecture model

Acceleration of CFD and data analysis using graphics processors

TDDFT in massively parallel computer architectures: the OCTOPUS project

Efficient computational noise in GLSL

OpenCL framework for a CPU, GPU, and FPGA Platform

Multicore Processing for Clustering Algorithms

Dynamic Translation of Runtime Environments for Heterogeneous Computing

GPU Accelerated Computation of Fast Spectral Transforms

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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