high performance computing on graphics processing units: hgpu.org

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Sparse Matrix-Vector Multiplication on GPU

Arash Ashari

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX Titan, Sparse matrix, Tesla K10, Thesis

February 9, 2015 by hgpu

Extending the Gotran framework: LATEX and GPU acceleration

George Bahijevitch Bahij

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Tags: Computational modelling, Computer science, CUDA, Differential equations, nVidia, nVidia GeForce GT 650 M, nVidia GeForce GTX Titan, ODEs, Ordinary differential equations, Python, Thesis

February 6, 2015 by hgpu

Nucleation Studies on Graphics Processing Units

Benjamin J. Block

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Tags: Anisotropy, ATI, ATI Radeon HD 6970, Biology, Chemistry, Differential equations, Ising model, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 690, nVidia GeForce GTX Titan, OpenCL, Physics, Tesla K20, Thesis

February 6, 2015 by hgpu

Scaling Recurrent Neural Network Language Models

Will Williams, Niranjani Prasad, David Mrva, Tom Ash, Tony Robinson

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX Titan

February 3, 2015 by hgpu

Optimized Data Transfers Based on the OpenCL Event Management Mechanism

Hiroyuki Takizawa, Shoichi Hirasawa, Makoto Sugawara, Isaac Gelado, Hiroaki Kobayashi, Wen-mei W. Hwu

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Tags: Computer science, nVidia, nVidia GeForce GTX Titan, OpenCL, Programming techniques, Tesla C1060, Tesla C2070

February 1, 2015 by hgpu

Performance Analysis and Optimization of a Distributed Processing Framework for Data Mining Accelerated with Graphics Processing Units

Edgar Isaac Hiroshi Leon Saiki

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Tags: Algorithms, Computer science, CUDA, Data mining, MapReduce, nVidia, nVidia GeForce GTX Titan, Tesla K20, Thesis

February 1, 2015 by hgpu

Performance Analysis of Join Algorithms on GPUs

Ran Rui, Hao Li, Yi-Cheng Tu

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Tags: Algorithms, Computer science, CUDA, Databases, Join, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, Performance

January 26, 2015 by hgpu

GPU concurrency: Weak behaviours and programming assumptions

Jade Alglave, Mark Batty, Alastair F. Donaldson, Ganesh Gopalakrishnan, Jeroen Ketema, Daniel Poetzl, Tyler Sorensen, John Wickerson

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Tags: ATI, ATI Radeon HD 6570, ATI Radeon HD 7970, Computer science, Memory, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 540 M, nVidia GeForce GTX 660, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, OpenCL, Performance, PTX, Tesla C2075

January 21, 2015 by hgpu

CURFIL: Random Forests for Image Labeling on GPU

Hannes Schulz, Benedikt Waldvogel, Rasha Sheikh, Sven Behnke

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Source codes

Tags: Computer science, Computer vision, CUDA, nVidia, nVidia GeForce GTX 675 M, nVidia GeForce GTX Titan, Package, Tesla K20

January 16, 2015 by hgpu

Simulating and Visualizing Real-Time Crowds on GPU Clusters

Benjamin Hernandez, Hugo Perez, Isaac Rudomin, Sergio Ruiz, Oriam DeGyves, Leonel Toledo

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Tags: Algorithms, Computer science, CUDA, GPU cluster, MPI, nVidia, nVidia GeForce GTX Titan, nVidia Jetson TK1, Rendering, Tesla M2090, Visualization

January 8, 2015 by hgpu

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille

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Tags: Algorithms, CNN, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX Titan

December 26, 2014 by hgpu

SignalPU: A programming model for DSP applications on parallel and heterogeneous clusters

Farouk Mansouri, Sylvain Huet, Dominique Houzet

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Tags: Algorithms, CUDA, DSP, Heterogeneous systems, nVidia, nVidia GeForce GT 610, nVidia GeForce GTX Titan, nVidia Quadro 4000, Signal processing

December 20, 2014 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

Sparse Matrix-Vector Multiplication on GPU

Extending the Gotran framework: LATEX and GPU acceleration

Nucleation Studies on Graphics Processing Units

Scaling Recurrent Neural Network Language Models

Optimized Data Transfers Based on the OpenCL Event Management Mechanism

Performance Analysis and Optimization of a Distributed Processing Framework for Data Mining Accelerated with Graphics Processing Units

Performance Analysis of Join Algorithms on GPUs

GPU concurrency: Weak behaviours and programming assumptions

CURFIL: Random Forests for Image Labeling on GPU

Simulating and Visualizing Real-Time Crowds on GPU Clusters

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

SignalPU: A programming model for DSP applications on parallel and heterogeneous clusters

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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