high performance computing on graphics processing units: hgpu.org

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High-Performance Reverse Time Migration on GPU

Javier Cabezas, Mauricio Araya-Polo, Isaac Gelado, Nacho Navarro, Enric Morancho, Jose M. Cela

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Tags: CUDA, Earth and Space Sciences, Finite difference, Geoscience, nVidia, nVidia GeForce GTX 280, Partial differential equations, PDEs

July 22, 2011 by hgpu

Discrete fourier transform on multicore

Franz Franchetti, Markus Puschel, Yevgen Voronenko, Srinivas Chellappa, Jose M. F. Moura

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Tags: Cell processor, CUDA, FFT, nVidia, nVidia GeForce GTX 280, Signal processing

July 19, 2011 by hgpu

An auto-tuning framework for parallel multicore stencil computations

Shoaib Kamil, Cy Chan, Leonid Oliker, John Shalf, Samuel Williams

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Tags: Code generation, Computer science, CUDA, Fortran, nVidia, nVidia GeForce GTX 280

July 18, 2011 by hgpu

Accelerating high-level engineering computations by automatic compilation of Geometric Algebra to hardware accelerators

Jens Huthmann, Peter Muller, Florian Stock, Dietmar Hildenbrand, Andreas Koch

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Tags: CAD, Computer science, FPGA, nVidia, nVidia GeForce GTX 280

July 18, 2011 by hgpu

Implementing LNS using filtering units of GPUs

Mark Arnold, Sylvain Collange, David Defour

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Tags: CUDA, Filtering, nVidia, nVidia GeForce GTX 280, Signal processing

July 17, 2011 by hgpu

Highly parallel decoding of space-time codes on graphics processing units

Kalyana C. Bollapalli, Yiyue Wu, Kanupriya Gulati, Sunil Khatri, A. Robert Calderbank

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Tags: CUDA, nVidia, nVidia GeForce GTX 280, Signal processing, Tesla C1060, Wireless baseband architecture

July 17, 2011 by hgpu

Mars: Accelerating MapReduce with Graphics Processors

Wenbin Fang, Bingsheng He, Qiong Luo, N.K. Govindaraju

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Tags: Computer science, CUDA, MapReduce, nVidia, nVidia GeForce GTX 280, Package, Presentation

July 17, 2011 by hgpu

Quality comparison and acceleration for digital hologram generation method based on segmentation

Hoonjong Kang, Fahri Yaras, Levent Onural

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Tags: Computer holography, Computer science, CUDA, Image reconstruction, nVidia, nVidia GeForce GTX 280

July 17, 2011 by hgpu

High-performance bankruptcy prediction model using Graphics Processing Units

Bernardete Ribeiro, Noel Lopes, Catarina Silva

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Tags: Finance, Neural networks, nVidia GeForce 8600 GT, nVidia GeForce GTX 280, Package

July 16, 2011 by hgpu

Computing spike-based convolutions on GPUs

Jayram Moorkanikara Nageswaran, Nikil Dutt, Yingxue Wang, Tobi Delbrueck

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Tags: Computer science, CUDA, Neural networks, nVidia, nVidia GeForce GTX 280, Sensing

July 14, 2011 by hgpu

Large-scale transient stability simulation on graphics processing units

Vahid Jalili-Marandi, Venkata Dinavahi

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Tags: Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce GTX 280, Power Systems Simulation

July 14, 2011 by hgpu

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general-purpose computing on graphics processing units

Jun Igarashi, Osamu Shouno, Tomoki Fukai, Hiroshi Tsujino

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Tags: Computer science, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 280, Visualization

July 12, 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

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high performance computing on graphics processing units: hgpu.org

High-Performance Reverse Time Migration on GPU

Discrete fourier transform on multicore

An auto-tuning framework for parallel multicore stencil computations

Accelerating high-level engineering computations by automatic compilation of Geometric Algebra to hardware accelerators

Implementing LNS using filtering units of GPUs

Highly parallel decoding of space-time codes on graphics processing units

Mars: Accelerating MapReduce with Graphics Processors

Quality comparison and acceleration for digital hologram generation method based on segmentation

High-performance bankruptcy prediction model using Graphics Processing Units

Computing spike-based convolutions on GPUs

Large-scale transient stability simulation on graphics processing units

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general-purpose computing on graphics processing units

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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