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Exploiting Heterogeneous Computing Platforms By Cataloging Best Solutions For Resource Intensive Seismic Applications

Thomas Grosser, Alexandros Gremm, Sebastian Veith, Gerald Heim, Wolfgang Rosenstiel, Victor Medeiros, Manoel Eusebio de Lima

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Tags: Earth and Space Sciences, FPGA, Geoscience, Heterogeneous systems, nVidia, OpenCL, Optimization, Seismic modeling, Seismology, Tesla T10P

September 25, 2011 by hgpu

A CUDA SIMT Interpreter for Genetic Programming

W. B. Langdon

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Tags: Computer science, CUDA, Genetic programming, nVidia, Package, Tesla T10P

February 15, 2011 by hgpu

Model-driven autotuning of sparse matrix-vector multiply on GPUs

Jee W. Choi, Amik Singh, Richard W. Vuduc

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Performance, Sparse matrix, Tesla C1060, Tesla C870, Tesla T10P

February 5, 2011 by hgpu

A CUDA SIMT interpreter for genetic programming. Revised

William B. Langdon

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Tags: Computer science, CUDA, Genetic programming, nVidia, Package, Tesla T10P

January 21, 2011 by hgpu

Exploring new architectures in accelerating CFD for Air Force applications

Jack Dongarra, Shirley Moore, Gregory Peterson, Stanimire Tomov

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Tags: Algorithms, Fluid dynamics, FPGA, nVidia, nVidia Quadro FX 5600, Tesla T10P

January 11, 2011 by hgpu

Power Consumption of GPUs from a Software Perspective

Sylvain Collange, David Defour, Arnaud Tisserand

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce 9800 GX2, Tesla C870, Tesla D870, Tesla T10P

January 7, 2011 by hgpu

Solving Sparse Linear Systems on NVIDIA Tesla GPUs

Mingliang Wang, Hector Klie, Manish Parashar, Hari Sudan

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Sparse matrix, Tesla T10P

November 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

Exploiting Heterogeneous Computing Platforms By Cataloging Best Solutions For Resource Intensive Seismic Applications

A CUDA SIMT Interpreter for Genetic Programming

Model-driven autotuning of sparse matrix-vector multiply on GPUs

A CUDA SIMT interpreter for genetic programming. Revised

Exploring new architectures in accelerating CFD for Air Force applications

Power Consumption of GPUs from a Software Perspective

Solving Sparse Linear Systems on NVIDIA Tesla GPUs

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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