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Synergia CUDA: GPU-accelerated accelerator modeling package

Q. Lu, J. Amundson

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Tags: CUDA, FFT, GPU cluster, MPI, nVidia, Package, Particle-in-cell methods, Physics, Tesla C1070, Tesla K20

June 17, 2014 by hgpu

GPU implementation of a hybrid lattice Boltzmann method for non-isothermal flows

Nachiket Sahasrabudhe, Mahesh Mynam, Ajay Nandgaonkar, Gayathri Jayaraman

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Tags: Algorithms, CUDA, Finite difference, Fluid dynamics, Lattice Boltzmann model, nVidia, Tesla C1070

September 4, 2012 by hgpu

An Energy-Efficient Heterogeneous System for Embedded Learning and Classification

Abhinandan Majumdar, Srihari Cadambi, Srimat T. Chakradhar

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Tags: Computer science, Energy-efficient computing, FPGA, Heterogeneous systems, nVidia, nVidia ION, Tesla C1070

January 20, 2012 by hgpu

Acceleration of Streamed Tensor Contraction Expressions on GPGPU-Based Clusters

Wenjing Ma, Sriram Krishnamoorthy, Oreste Villa, Karol Kowalski

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Tags: Computational chemistry, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, Presentation, Tesla C1070, Tesla S1070

July 7, 2011 by hgpu

Astrophysical Particle Simulations with Custom GPU Clusters

R. Spurzem, P. Berczik, K. Nitadori, G. Marcus, A. Kugel, R. Manner, I. Berentzen, R. Klessen, R. Banerjee

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Tags: Astrophysics, CUDA, GPU cluster, MPI, N-body simulation, nVidia, Tesla C1070, Tesla C870

May 19, 2011 by hgpu

Modeling the propagation of elastic waves using spectral elements on a cluster of 192 GPUs

Dimitri Komatitsch, Dominik Goddeke, Gordon Erlebacher, David Michea

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Tags: CUDA, Earth and Space Sciences, Geoscience, GPU cluster, MPI, nVidia, Tesla C1070

November 18, 2010 by hgpu

GPU-based ultrafast IMRT plan optimization

Chunhua Men,Xuejun Gu,Dongju Choi,Amitava Majumdar,Ziyi Zheng,Klaus Mueller,Steve B. Jiang

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Tags: CUDA, Medicine, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla C1060, Tesla C1070

October 27, 2010 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

Synergia CUDA: GPU-accelerated accelerator modeling package

GPU implementation of a hybrid lattice Boltzmann method for non-isothermal flows

An Energy-Efficient Heterogeneous System for Embedded Learning and Classification

Acceleration of Streamed Tensor Contraction Expressions on GPGPU-Based Clusters

Astrophysical Particle Simulations with Custom GPU Clusters

Modeling the propagation of elastic waves using spectral elements on a cluster of 192 GPUs

GPU-based ultrafast IMRT plan optimization

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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