high performance computing on graphics processing units: hgpu.org

Niels Drost, Jason Maassen, Maarten A.J. van Meersbergen, Henri E. Bal, F. Inti Pelupessy, Simon Portegies Zwart, Michael Kliphuis, Henk A. Dijkstra, Frank J. Seinstra

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Tags: Astrophysics, Cloud, Computer science, CUDA, Earth and Space Sciences, Heterogeneous systems, nVidia, Physics, Solar and Stellar Astrophysics, Tesla C2050

March 6, 2012 by hgpu

A 3D radiative transfer framework. VIII. OpenCL implementation

P. H. Hauschildt, E. Baron

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Tags: Astrophysics, ATI, ATI Radeon HD 4870, Earth and Planetary Astrophysics, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce 8600 M, nVidia GeForce 8800 GT, nVidia GeForce GT 120, nVidia GeForce GTX 285, nVidia Quadro FX 4800, OpenCL, Solar and Stellar Astrophysics, Tesla C2050

February 17, 2012 by hgpu

How well do STARLAB and NBODY compare? II: Hardware and accuracy

P. Anders, H. Baumgardt, E. Gaburov, S. Portegies Zwart

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Tags: Algorithms, Astrophysics, Cosmology and Extragalactic Astrophysics, Galaxy Astrophysics, GPU cluster, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, nVidia GeForce 9800 GX2

January 30, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Implementation of digital down converter in GPU

A Distributed GPU-based Framework for real-time 3D Volume Rendering of Large Astronomical Data Cubes

Application of GPUs for the Calculation of Two Point Correlation Functions in Cosmology

Radio Astronomy Beam Forming on Many-Core Architectures

An Efficient Work-Distribution Strategy for Gridding Radio-Telescope Data on GPUs

Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations

Genetic Algorithm Modeling with GPU Parallel Computing Technology

A pilgrimage to gravity on GPUs

Bonsai: A GPU Tree-Code

A 3D radiative transfer framework. VIII. OpenCL implementation

How well do STARLAB and NBODY compare? II: Hardware and accuracy

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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