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The GPU Computing Revolution: From Multi-Core CPUs To Many-Core Graphics Processors

Simon McIntosh-Smith

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, Review, Tesla C2050

October 19, 2011 by hgpu

Characterization of FPGA-based High Performance Computers

Karl Savio Pimenta Pereira

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Tags: Algorithms, Benchmarking, Chemistry, Computer science, CUDA, DSP, FFT, FPGA, Molecular dynamics, nVidia, nVidia GeForce GTX 280, OpenCL, Tesla C2050, Thesis

October 18, 2011 by hgpu

Design and Performance of the OP2 Library for Unstructured Mesh Applications

Carlo Bertolli, Adam Betts, Gihan Mudalige, Mike Giles, Paul Kelly

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Source codes

Tags: Code generation, Compilers, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, OpenCL, OpenMP, Package, Tesla C2050

October 18, 2011 by hgpu

Experience Applying Fortran GPU Compilers to Numerical Weather Prediction

T. Henderson, J. Middlecoff, J. Rosinski, M. Govett, P. Madden

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Tags: CUDA, Earth and Space Sciences, Fortran, nVidia, nVidia GeForce GTX 280, Tesla C2050, Weather prediction

October 18, 2011 by hgpu

Fast N-body Simulations on GPUs

Rio Yokota, Lorena A. Barba

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Tags: Algorithms, Astrophysics, Computer science, CUDA, Fast multipole method, Heterogeneous systems, Mathematical Software, N-body simulation, Numerical Analysis, nVidia, Tesla C2050, Tesla M2050

October 18, 2011 by hgpu

Fast Implementation of DGEMM on Fermi GPU

Guangming Tan, Linchuan Li, Sean Triechle, Everett Phillips, Yungang Bao, Ninghui Sun

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Source codes

Tags: Algorithms, Benchmarking, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, Optimization, Performance, Tesla C2050

October 17, 2011 by hgpu

Magneto-hydrodynamics simulation in astrophysics

Bijia Pang

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Tags: Algorithms, Astrophysics, CUDA, Fortran, Heterogeneous systems, Magnetohydrodynamics, nVidia, OpenCL, Physics, Tesla C1060, Tesla C2050, Thesis

October 17, 2011 by hgpu

Hard-Sphere Collision Simulations with Multiple GPUs, PCIe Extension Buses and GPU-GPU Communications

K.A. Hawick, D.P.Playne

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Tags: Algorithms, CUDA, GPU cluster, Gravitation, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Physics, Tesla C2050, Tesla C2070

October 16, 2011 by hgpu

Bit-Packed Damaged Lattice Potts Model Simulations with CUDA and GPUs

Ken A. Hawick, Mitchell G.B. Johnson

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Tags: CUDA, Data parallelism, Ising model, Materials Science, Nearest neighbour, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 295, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Phase transition, Physics, Probability, Tesla C2050, Tesla C2070

October 16, 2011 by hgpu

Effects of compression on data intensive algorithms

Ahmed Adnan Aqrawi

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Tags: Algorithms, Compression, Computer science, CUDA, Filtering, Image processing, nVidia, Seismology, Tesla C1060, Tesla C2050, Thesis

October 15, 2011 by hgpu

Bandwidth Reduction Through Multithreaded Compression of Seismic Images

Ahmed A. Aqrawi, Anne C. Elster

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Tags: Algorithms, Compression, Computer science, CUDA, Image processing, nVidia, Seismology, Tesla C2050

October 15, 2011 by hgpu

GPU fluids in production: a compiler approach to parallelism

Dan Bailey, Ian Masters, Matt Warner

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Tags: Code generation, Compilers, Computer science, Image processing, nVidia, Performance, Presentation, PTX, Tesla C2050

October 15, 2011 by hgpu

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

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

See all packages

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high performance computing on graphics processing units: hgpu.org

The GPU Computing Revolution: From Multi-Core CPUs To Many-Core Graphics Processors

Characterization of FPGA-based High Performance Computers

Design and Performance of the OP2 Library for Unstructured Mesh Applications

Experience Applying Fortran GPU Compilers to Numerical Weather Prediction

Fast N-body Simulations on GPUs

Fast Implementation of DGEMM on Fermi GPU

Magneto-hydrodynamics simulation in astrophysics

Hard-Sphere Collision Simulations with Multiple GPUs, PCIe Extension Buses and GPU-GPU Communications

Bit-Packed Damaged Lattice Potts Model Simulations with CUDA and GPUs

Effects of compression on data intensive algorithms

Bandwidth Reduction Through Multithreaded Compression of Seismic Images

GPU fluids in production: a compiler approach to parallelism

Recent source codes

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

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

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