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Parallel Sparse Coding for Seafloor Image Analysis

Genlang Chen, Chenggang Lai, Miaoqing Huang

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Tags: CUDA, Image processing, Intel Xeon Phi, Machine learning, nVidia, Tesla K20

July 3, 2015 by hgpu

Heterogeneous Network Embedding via Deep Architectures

Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang

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Tags: Computer science, CUDA, Data mining, Deep learning, Machine learning, nVidia, Tesla K40

July 1, 2015 by hgpu

Alpha-Beta Divergences Discover Micro and Macro Structures in Data

Karthik Narayan, Ali Punjani, Pieter Abbeel

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Tags: Computer science, Computer vision, CUDA, Machine learning, nVidia, Tesla K40, Visualization

June 24, 2015 by hgpu

Generating Efficient Tensor Contractions for GPUs

Thomas Nelson, Axel Rivera, Prasanna Balaprakash, Mary Hall, Paul D. Hovland, Elizabeth Jessup, Boyana Norris

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Tags: Algorithms, Chemistry, Computer science, CUDA, Fluid dynamics, Machine learning, nVidia, OpenACC, Optimization

June 22, 2015 by hgpu

Automatic Selection of Sparse Matrix Representation on GPUs

Naser Sedaghati, Te Mu, Louis-Noel Pouchet, Srinivasan Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, Physics, Sparse matrix, Tesla K20, Tesla K40

June 14, 2015 by hgpu

CGO: G: Intelligent Heuristic Construction with Active Learning

William F. Ogilvie, Pavlos Petoumenous, Zheng Wang, Hugh Leather

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Tags: Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Optimization

June 8, 2015 by hgpu

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun

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

June 7, 2015 by hgpu

Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability

Thomas L. Falch, Anne C. Elster

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Tags: ATI, ATI Radeon HD 7970, Benchmarking, Computer science, Deep learning, Heterogeneous systems, Machine learning, Neural networks, nVidia, OpenCL, Tesla K40

June 5, 2015 by hgpu

Blocks and Fuel: Frameworks for deep learning

Bart van Merrienboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-Farley, Jan Chorowski, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Linear Algebra, Machine learning, MPI, Neural networks, nVidia, Package, Python, Symbolic Computation, Visualization

June 5, 2015 by hgpu

DjiNN and Tonic: DNN as a Service and Its Implications for Future Warehouse Scale Computers

Johann Hauswald, Yiping Kang, Michael A. Laurenzano, Quan Chen, Cheng Li, Trevor Mudge, Ronald G. Dreslinski, Jason Mars, Lingjia Tang

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Tags: CUDA, Deep learning, Image processing, Machine learning, Neural networks, nVidia, Speech recognition, Tesla K40

May 25, 2015 by hgpu

ACCTuner: OpenACC Auto-Tuner For Accelerated Scientific Applications

Fatemah Ramzy AlZayer

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Tags: Algorithms, Computer science, Machine learning, Nearest neighbour, nVidia, OpenACC, Tesla K20, Tesla K40, Thesis

May 25, 2015 by hgpu

Using Butterfly-Patterned Partial Sums to Optimize GPU Memory Accesses for Drawing from Discrete Distributions

Guy L. Steele Jr. (Oracle Labs), Jean-Baptiste Tristan

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Tags: Computer science, CUDA, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce Titan

May 16, 2015 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

Parallel Sparse Coding for Seafloor Image Analysis

Heterogeneous Network Embedding via Deep Architectures

Alpha-Beta Divergences Discover Micro and Macro Structures in Data

Generating Efficient Tensor Contractions for GPUs

Automatic Selection of Sparse Matrix Representation on GPUs

CGO: G: Intelligent Heuristic Construction with Active Learning

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability

Blocks and Fuel: Frameworks for deep learning

DjiNN and Tonic: DNN as a Service and Its Implications for Future Warehouse Scale Computers

ACCTuner: OpenACC Auto-Tuner For Accelerated Scientific Applications

Using Butterfly-Patterned Partial Sums to Optimize GPU Memory Accesses for Drawing from Discrete Distributions

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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