high performance computing on graphics processing units: hgpu.org

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Multi-GPU parallelization of a 3D Bayesian CT algorithm and its application on real foam reconstruction with incomplete data set

Nicolas Gac, Alexandre Vabre, Ali Mohammad Djafari

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Tags: Algorithms, Bayesian, Computed tomography, CT, CUDA, GPU cluster, Image reconstruction, Medicine, nVidia, Tesla C1060, Tomography

March 31, 2012 by hgpu

Optimized Strategies for Mapping Three-dimensional FFTs onto CUDA GPUs

Jing Wu, Joseph JaJa

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Tags: Algorithms, Computer science, CUDA, FFT, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Performance, Tesla C1060, Tesla C2050

March 30, 2012 by hgpu

Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees

James Bergstra, Nicolas Pinto, David Cox

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Tags: Computer science, CUDA, Filtering, Machine learning, Matrix multiplication, nVidia, nVidia GeForce GTX 295, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Optimization, Tesla C1060, Tesla C2070

March 29, 2012 by hgpu

Accelerating large-scale simulations of cortical neuronal network development

Fumitaka Kawasaki, Michael Stiber

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Tags: CNN, Computer science, CUDA, Neural networks, Neuroscience, nVidia, Tesla C1060

March 23, 2012 by hgpu

Advanced Programming Platform for efficient use of Data Parallel Hardware

Luis Cabellos

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Tags: Computer science, Distributed computing, nVidia, OpenCL, Tesla C1060

March 23, 2012 by hgpu

CUDA 2D Stencil Computations for the Jacobi Method

Jose Maria Cecilia, Jose Manuel Garcia, Manuel Ujaldon

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Differential equations, Linear Algebra, nVidia, Optimization, Tesla C1060, Tesla C870

March 16, 2012 by hgpu

On the Use of Small 2D Convolutions on GPUs

Shams A.H. Al Umairy, Alexander S. van Amesfoort, Irwan D. Setija, Martijn C. van Beurden, Henk J. Sips

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Tags: Computer science, CUDA, Electrodynamics, FFT, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, Optimization, Physics, Tesla C1060

March 15, 2012 by hgpu

Iterative Statistical Kernels on Contemporary GPUs

Thilina Gunarathne, Bimalee Salpitikorala, Arun Chauhan, Geoffrey Fox

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Tags: Algorithms, Cloud, Clustering, Computer science, nVidia, OpenCL, Sparse matrix, Tesla C1060, Tesla C2070, Tesla M2050

March 15, 2012 by hgpu

A Study of CUDA Acceleration and Impact of Data Transfer Overhead in Heterogeneous Environment

Fahian Ahmed, Saddam Quirem, Bum Joo Shin, Duk Joo Son, Young Choon Woo, Byeong Kil Lee, Wan Choi

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Tags: Algorithms, Computer science, CUDA, Databases, Heterogeneous systems, nVidia, Optimization, Security, Tesla C1060

March 9, 2012 by hgpu

Fast Multipole Methods and High Performance Computing

Cristopher Ratkevich Cecka

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Tags: CUDA, Electrodynamics, Fast multipole method, nVidia, Package, Tesla C1060, Tesla C2050, Thesis

February 26, 2012 by hgpu

Speculative Parallelization on GPGPUs

Min Feng, Rajiv Gupta, Laxmi N. Bhuyan

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Tags: Compilers, Computer science, CUDA, nVidia, Performance, Poster, Tesla C1060

February 23, 2012 by hgpu

GPU-Based Iterative Relative Fuzzy Connectedness Image Segmentation

Ying Zhuge, Jayaram K. Udupa, Krzysztof C. Ciesielski, Alexandre X. Falcao, Paulo A. V. Miranda, Robert W. Miller

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Tags: Algorithms, CUDA, Image processing, nVidia, Tesla C1060

February 22, 2012 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

Multi-GPU parallelization of a 3D Bayesian CT algorithm and its application on real foam reconstruction with incomplete data set

Optimized Strategies for Mapping Three-dimensional FFTs onto CUDA GPUs

Machine Learning for Predictive Auto-Tuning with Boosted Regression Trees

Accelerating large-scale simulations of cortical neuronal network development

Advanced Programming Platform for efficient use of Data Parallel Hardware

CUDA 2D Stencil Computations for the Jacobi Method

On the Use of Small 2D Convolutions on GPUs

A Study of CUDA Acceleration and Impact of Data Transfer Overhead in Heterogeneous Environment

Fast Multipole Methods and High Performance Computing

Speculative Parallelization on GPGPUs

GPU-Based Iterative Relative Fuzzy Connectedness Image Segmentation

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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