high performance computing on graphics processing units: hgpu.org

hgpu.org » Deep learning

A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications

Matthew W. Moskewicz, Ali Jannesari, Kurt Keutzer

View

Download (PDF)

Source codes

Tags: AMD R9 Nano, ARM, ATI, Code generation, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

November 23, 2016 by hgpu

Deep Tensor Convolution on Multicores

David Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit

View

Download (PDF)

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

November 23, 2016 by hgpu

GA3C: GPU-based A3C for Deep Reinforcement Learning

Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, TensorFlow

November 23, 2016 by hgpu

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, Yanzhi Wang

View

Download (PDF)

Tags: ASIC, CNN, Computer science, Deep learning, FPGA, Neural networks, nVidia, Tesla C2075

November 22, 2016 by hgpu

How to scale distributed deep learning?

Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, GPU cluster, nVidia, Performance, Tesla K20, Tesla K80

November 19, 2016 by hgpu

Efficient Communications in Training Large Scale Neural Networks

Linnan Wang, Wei Wu, George Bosilca, Richard Vuduc, Zenglin Xu

View

Download (PDF)

Tags: Caffe, Computer science, CUDA, Deep learning, MPI, Neural networks, nVidia, OpenMPI, Performance

November 16, 2016 by hgpu

PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks

Dong Wang, Jianjing An, Ke Xu

View

Download (PDF)

Source codes

Tags: CNN, Computer science, Deep learning, DSP, FPGA, Hardware Architecture, Neural networks, OpenCL, Package

November 10, 2016 by hgpu

Tamp: A Library for Compact Deep Neural Networks with Structured Matrices

Bingchen Gong, Brendan Jou, Felix Yu, Shih-Fu Chang

View

Download (PDF)

Source codes

Tags: Caffe, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, TensorFlow

November 8, 2016 by hgpu

Accelerate Deep Learning Inference with MCTS in the game of Go on the Intel Xeon Phi

Ching-Nung Lin, Shi-Jim Yen

View

Download (PDF)

Tags: Computer science, Deep learning, Intel Xeon Phi, Neural networks

November 8, 2016 by hgpu

Molecular Activity Prediction using Deep Learning Software Library

Yoshiki Kato, Shinji Hamada, Hitoshi Goto

View

Download (PDF)

Source codes

Tags: Bioinformatics, Chemistry, Computer science, CUDA, Deep learning, nVidia, Package, Python, Tesla K20

November 5, 2016 by hgpu

Extensions and Limitations of the Neural GPU

Eric Price, Wojciech Zaremba, Ilya Sutskever

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Deep learning, nVidia, Package, Python

November 3, 2016 by hgpu

Classification Performance of Convolutional Neural Networks

Niklas Mattsson

View

Download (PDF)

Tags: Computer science, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 960, nVidia Tegra TX1, Performance, Python, Theano, Thesis

November 1, 2016 by hgpu

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

Geak: Introducing Triton Kernel AI Agent & Evaluation Benchmarks

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

OpenDwarfs 2025: Modernizing the OpenDwarfs Benchmark Suite for Heterogeneous Computing

Specx: Speculative task-based runtime system

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications

Deep Tensor Convolution on Multicores

GA3C: GPU-based A3C for Deep Reinforcement Learning

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

How to scale distributed deep learning?

Efficient Communications in Training Large Scale Neural Networks

PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks

Tamp: A Library for Compact Deep Neural Networks with Structured Matrices

Accelerate Deep Learning Inference with MCTS in the game of Go on the Intel Xeon Phi

Molecular Activity Prediction using Deep Learning Software Library

Extensions and Limitations of the Neural GPU

Classification Performance of Convolutional Neural Networks

Recent source codes

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

GEAK-agent: LLM-based AI agent, which can write correct and efficient GPU kernels automatically

OpenDwarfs 2025: re-engineered version of the OpenDwarfs benchmark suite, for compatibility with modern platforms

Specx: Speculative task-based runtime system

Mutual-Supervised Learning for Sequential-to-Parallel Code Translation

Most viewed papers (last 30 days)