high performance computing on graphics processing units: hgpu.org

hgpu.org » Artificial intelligence

Fast Locality Sensitive Hashing for Beam Search on GPU

Xing Shi, Shizhen Xu, Kevin Knight

View

Tags: Algorithms, Artificial intelligence, Computer science, CUDA, Data Structures and Algorithms, Hashing, NLP, nVidia, Tesla K20

June 9, 2018 by hgpu

NengoDL: Combining deep learning and neuromorphic modelling methods

Daniel Rasmussen

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package, Python

June 2, 2018 by hgpu

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Shaohuai Shi, Qiang Wang, Xiaowen Chu, Bo Li

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, Performance, Tesla K80

May 12, 2018 by hgpu

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

Nicolas Weber, Florian Schmidt, Mathias Niepert, Felipe Huici

View

Download (PDF)

Tags: AI, Artificial intelligence, cache, CNN, Computer science, cpu, CUDA, Deep learning, GPU, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 1080 Ti

April 25, 2018 by hgpu

CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

Xiaolin Wang

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, OpenCV, Package

April 22, 2018 by hgpu

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

Stylianos I. Venieris, Alexandros Kouris, Christos-Savvas Bouganis

View

Download (PDF)

Tags: Artificial intelligence, Computer science, Deep learning, Design space exploration, FPGA, OpenCL, survey

March 17, 2018 by hgpu

TVM: End-to-End Optimization Stack for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Computer science, CUDA, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, performance portability, TensorFlow, Tesla K80

February 15, 2018 by hgpu

Deep In-GPU Experience Replay

Ben Parr

View

Download (PDF)

Tags: Artificial intelligence, Computer science, nVidia, nVidia GeForce GTX Titan X

January 13, 2018 by hgpu

Investigating Half Precision Arithmetic to Accelerate Dense Linear System Solvers

Azzam Haidar, Panruo Wu, Stanimire Tomov, Jack Dongarra

View

Download (PDF)

Tags: Algorithms, Artificial intelligence, BLAS, Computer science, Linar Algebra, Mixed precision, Neural networks, nVidia, Tesla P100

December 10, 2017 by hgpu

STAR-RT: Visual attention for real-time video game playing

Iuliia Kotseruba, John K. Tsotsos

View

Download (PDF)

Tags: AMD FirePro W7000, Artificial intelligence, ATI, Computer science, Games, GLSL, OpenCL, OpenGL

December 3, 2017 by hgpu

fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs

Stylianos I. Venieris, Christos-Savvas Bouganis

View

Download (PDF)

Tags: Artificial intelligence, Computer science, Deep learning, FPGA, Neural networks

November 30, 2017 by hgpu

Device Placement Optimization with Reinforcement Learning

Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

View

Download (PDF)

Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Heterogeneous systems, Neural networks, nVidia, TensorFlow, Tesla K80

June 17, 2017 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

Understanding GEMM Performance and Energy on NVIDIA Ada Lovelace: A Machine Learning-Based Analytical Approach

HiRep

Scaling SU(2) to 1000 GPUs using HiRep

Gaia AVU-GSR

Performance portability via C++ PSTL, SYCL, OpenMP, and HIP: the Gaia AVU-GSR case study

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

gpuPairHMM: High-speed Pair-HMM Forward Algorithm for DNA Variant Calling on GPUs

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Fast Locality Sensitive Hashing for Beam Search on GPU

NengoDL: Combining deep learning and neuromorphic modelling methods

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

TVM: End-to-End Optimization Stack for Deep Learning

Deep In-GPU Experience Replay

Investigating Half Precision Arithmetic to Accelerate Dense Linear System Solvers

STAR-RT: Visual attention for real-time video game playing

fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs

Device Placement Optimization with Reinforcement Learning

Recent source codes

tdg-benchs: benchmarks used to test the performance of taskgraph

SW#SYCL

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

HiRep

Gaia AVU-GSR

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

Most viewed papers (last 30 days)