high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

GPU-Acceleration of In-Memory Data Analytics

Evangelia Sitaridi

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Databases, nVidia, String matching, Tesla C2070, Tesla K40, Thesis

August 18, 2016 by hgpu

GPU-accelerated Gibbs Sampling

Alexander Terenin, Shawfeng Dong, David Draper

View

Download (PDF)

Tags: Bayesian, big data, CUDA, nVidia, Statistics, Tesla K20

August 18, 2016 by hgpu

SkePU 2: Language Embedding and Compiler Support for Flexible and Type-Safe Skeleton Programming

August Ernstsson

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL, OpenMP, Programming techniques, Tesla K20, Thesis

August 18, 2016 by hgpu

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, Lei Zhang

View

Download (PDF)

Source codes

Tags: CNN, CUDA, Deep learning, Image processing, Matlab, nVidia, nVidia GeForce GTX Titan X, Package, Signal denoising

August 18, 2016 by hgpu

Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

Balint Pal Toth, Balint Czeba

View

Download (PDF)

Tags: CNN, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan X, Speech recognition

August 16, 2016 by hgpu

Learning Structured Sparsity in Deep Neural Networks

Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia Quadro K600, Tesla K40

August 16, 2016 by hgpu

Near Memory Similarity Search on Automata Processors

Vincent T. Lee, Justin Kotalik, Carlo C. Del Mundo, Armin Alaghi, Luis Ceze, Mark Oskin

View

Download (PDF)

Tags: ARM, Computer science, CUDA, FPGA, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, nVidia Jetson TK1

August 16, 2016 by hgpu

A Comparison of Potential Interfaces for Batched BLAS Computations

Samuel D. Relton, Pedro Valero-Lara, Mawussi Zounon

View

Download (PDF)

Source codes

Tags: BLAS, Computer science, CUDA, Linear Algbera, nVidia, Package, Tesla K40

August 11, 2016 by hgpu

CaffePresso: An Optimized Library for Deep Learning on Embedded Accelerator-based platforms

Gopalakrishna Hegde, Siddhartha, Nachiappan Ramasamy, Nachiket Kapre

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, DSP, FPGA, nVidia, nVidia Tegra TX1

August 11, 2016 by hgpu

Daino: A High-level Framework for Parallel and Efficient AMR on GPUs

Mohamed Wahib, Naoya Maruyama, Takayuki Aoki

View

Download (PDF)

Tags: Adaptive Mesh Refinement, CUDA

August 5, 2016 by wahibium

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf Schluter, Hermann Ney

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 980, Package, RNN, Speech recognition

August 4, 2016 by hgpu

A survey of sparse matrix-vector multiplication performance on large matrices

Max Grossman, Christopher Thiele, Mauricio Araya-Polo, Florian Frank, Faruk O. Alpak, Vivek Sarkar

View

Download (PDF)

Tags: Computer science, CUDA, Linear Algebra, Numerical Analysis, nVidia, Performance, Sparse matrix, survey, Tesla M2070

August 4, 2016 by hgpu

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

GPU-Acceleration of In-Memory Data Analytics

GPU-accelerated Gibbs Sampling

SkePU 2: Language Embedding and Compiler Support for Flexible and Type-Safe Skeleton Programming

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

Learning Structured Sparsity in Deep Neural Networks

Near Memory Similarity Search on Automata Processors

A Comparison of Potential Interfaces for Batched BLAS Computations

CaffePresso: An Optimized Library for Deep Learning on Embedded Accelerator-based platforms

Daino: A High-level Framework for Parallel and Efficient AMR on GPUs

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

A survey of sparse matrix-vector multiplication performance on large matrices

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Most viewed papers (last 30 days)