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Parallel Support Vector Machines in Practice

Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Tesla C2075

April 6, 2014 by hgpu

Parallel Spectral Graph Partitioning on CUDA

Orhan Firat, Alptekin Temizel

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Tags: Algorithms, CUDA, Image processing, Machine learning, nVidia, Tesla C2070

February 23, 2014 by hgpu

Fast American Basket Option Pricing on a multi-GPU Cluster

Michael Benguigui, Francoise Baude

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Tags: Finance, GPU cluster, Machine learning, nVidia, OpenCL, Option pricing, Tesla M2050, Tesla M2075, Tesla S1070

February 17, 2014 by hgpu

Implementing the Projected Spatial Rich Features on a GPU

Andrew D. Ker

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Tags: Computer science, CUDA, Machine learning, nVidia, Tesla K20

February 11, 2014 by hgpu

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Ying Zhang, Saizheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

February 2, 2014 by hgpu

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Gary Macindoe

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, Machine learning, Matrix decomposition, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla S1070, Thesis

January 29, 2014 by hgpu

Implementing Neural Networks Efficiently

Ronan Collobert, Koray Kavukcuoglu, Clement Farabet

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Tags: Algorithms, Computer science, CUDA, Machine learning, Neural networks, nVidia, Tesla M2090

December 29, 2013 by hgpu

BIDMach: Large-scale Learning with Zero Memory Allocation

John Canny, Huasha Zhao

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Tags: Benchmarking, Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 690

December 25, 2013 by hgpu

Large-Scale Paralleled Sparse Principal Component Analysis

W. Liu, H. Zhang, D. Tao, Y. Wang, K. Lu

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Numerical Analysis, nVidia, Tesla C2050

December 24, 2013 by hgpu

Single Server Multi-GPU Training of ConvNets

Omry Yadan, Keith Adams, Yaniv Taigman, Marc'Aurelio Ranzato

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Tags: Computer science, Computer vision, Machine learning, Neural networks

December 23, 2013 by hgpu

Fast Training of Convolutional Networks through FFTs

Michael Mathieu, Mikael Henaff, Yann LeCun

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Tags: Algorithms, Computer science, Computer vision, CUDA, FFT, Machine learning, nVidia

December 23, 2013 by hgpu

Augur: a Modeling Language for Data-Parallel Probabilistic Inference

Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Pocock, Stephen J. Green, Guy L. Steele Jr

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Tags: Bayesian, CUDA, Machine learning, Mathematics, nVidia, nVidia GeForce GTX Titan, Programming Languages, Statistics

December 13, 2013 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

Parallel Support Vector Machines in Practice

Parallel Spectral Graph Partitioning on CUDA

Fast American Basket Option Pricing on a multi-GPU Cluster

Implementing the Projected Spatial Rich Features on a GPU

Optimized Deep Learning Architectures with Fast Matrix Operation Kernels on Parallel Platform

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Implementing Neural Networks Efficiently

BIDMach: Large-scale Learning with Zero Memory Allocation

Large-Scale Paralleled Sparse Principal Component Analysis

Single Server Multi-GPU Training of ConvNets

Fast Training of Convolutional Networks through FFTs

Augur: a Modeling Language for Data-Parallel Probabilistic Inference

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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