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Training Neural Networks Without Gradients: A Scalable ADMM Approach

Gavin Taylor, Ryan Burmeister, Zheng Xu, Bharat Singh, Ankit Patel, Tom Goldstein

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Tags: Computer science, CUDA, Machine learning, MPI, Neural networks, nVidia, Tesla K40, Torch

May 9, 2016 by hgpu

Parallelizing Word2Vec in Shared and Distributed Memory

Shihao Ji, Nadathur Satish, Sheng Li, Pradeep Dubey

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia GeForce GTX Titan X, Tesla K40

May 9, 2016 by hgpu

Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

Jiantao Qiu, Jie Wang, Song Yao, Kaiyuan Guo, Boxun Li, Erjin Zhou, Jincheng Yu, Tianqi Tang, Ningyi Xu, Sen Song, Yu Wang, Huazhong Yang

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Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, FPGA, Neural networks, nVidia, nVidia Jetson TK1, Tesla K40

May 7, 2016 by hgpu

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

Andreas Klockner, Lucas C. Wilcox, T. Warburton

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Tags: Algorithms, AMD Radeon R9 Fury, ATI, Code generation, Computer science, Fortran, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Performance, Prefetch, Programming Languages, Python, Tesla K40

April 29, 2016 by hgpu

On the design of sparse hybrid linear solvers for modern parallel architectures

Stojce Nakov

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Linear Algebra, MPI, nVidia, Sparse linear iterative solvers, Sparse matrix, Tesla K40, Tesla M2070, Thesis

April 29, 2016 by hgpu

GPU-Aware Non-contiguous Data Movement In Open MPI

Wei Wu, George Bosilca, Rolf vandeVaart, Sylvain Jeaugey, Jack Dongarra

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Tags: Computer science, CUDA, MPI, nVidia, Tesla K40

April 26, 2016 by hgpu

Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems

M. Abalenkovs, A. Abdelfattah, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, I. Yamazaki, A. YarKhan

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Tags: Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, OpenCL, Programming techniques, Review, Tesla K40

April 19, 2016 by hgpu

A Unified, Hardware-Fitted, Cross-GPU Performance Model

James Stevens, Andreas Klockner

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Tags: AMD Radeon R9 Fury, ATI, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla C2070, Tesla K40

April 19, 2016 by hgpu

LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors

Yongchao Liu, Srinivas Aluru

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, PTX, Tesla K40

April 19, 2016 by hgpu

Efficiency of general Krylov methods on GPUs – An experimental study

Hartwig Anzt, Jack Dongarra, Moritz Kreutzer, Gerhard Wellein, Martin Kohler

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Tags: Computer science, CUDA, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, Package, Sparse matrix, Tesla K40

April 16, 2016 by hgpu

GPU-FV: Realtime Fisher Vector and Its Applications in Video Monitoring

Wenying Ma, Liangliang Cao, Lei Yu, Guoping Long, Yucheng Li

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Tags: Computer science, Computer vision, CUDA, Deep learning, nVidia, Package, Tesla K40

April 14, 2016 by hgpu

GPIC – GPU Power Iteration Cluster

Gustavo R.L Silva, Rafael R. Medeiros, Antonio P. Braga, Douglas A.G. Vieira

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Tags: Computer science, CUDA, GPU cluster, nVidia, Tesla K40

April 14, 2016 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

Training Neural Networks Without Gradients: A Scalable ADMM Approach

Parallelizing Word2Vec in Shared and Distributed Memory

Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

Array Program Transformation with Loo.py by Example: High-Order Finite Elements

On the design of sparse hybrid linear solvers for modern parallel architectures

GPU-Aware Non-contiguous Data Movement In Open MPI

Parallel Programming Models for Dense Linear Algebra on Heterogeneous Systems

A Unified, Hardware-Fitted, Cross-GPU Performance Model

LightScan: Faster Scan Primitive on CUDA Compatible Manycore Processors

Efficiency of general Krylov methods on GPUs – An experimental study

GPU-FV: Realtime Fisher Vector and Its Applications in Video Monitoring

GPIC – GPU Power Iteration Cluster

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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