high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

Joao Paulo Tarasconi Ruschel

View

Download (PDF)

Source codes

Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, OpenCL, OpenMP, Package, Performance, Tesla K80, Thesis

January 26, 2017 by hgpu

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

Rui Fan, Ke Xu, Jichang Zhao

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Package

January 26, 2017 by hgpu

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, TensorFlow, Tesla K40

January 26, 2017 by hgpu

DeepBach: a Steerable Model for Bach chorales generation

Gaetan Hadjeres, Francois Pachet

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Keras, nVidia, nVidia GeForce GTX 980 Ti, Package

January 23, 2017 by hgpu

A task-driven implementation of a simple numerical solver for hyperbolic conservation laws

Mohamed Essadki, Jonathan Jung, Adam Larat, Milan Pelletier, Vincent Perrier

View

Download (PDF)

Tags: Computer science, CUDA, Mathematical Software, Numerical Analysis, nVidia, Task scheduling, Tesla K40

January 23, 2017 by hgpu

GPGPU Performance Estimation with Core and Memory Frequency Scaling

Qiang Wang, Xiaowen Chu

View

Download (PDF)

Tags: Computer science, CUDA, Hardware, nVidia, nVidia GeForce GTX 980, Performance

January 23, 2017 by hgpu

OpenNMT: Open-Source Toolkit for Neural Machine Translation

Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, Alexander M. Rush

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia GeForce GTX 1080, Package, Python, Torch

January 19, 2017 by hgpu

Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

Yasuaki Mitani, Fumihiko Ino, Kenichi Hagihara

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Package, String matching

January 19, 2017 by hgpu

Light Loss-Less Data Compression, with GPU Implementation

Shunji Funasaka, Koji Nakano, Yasuaki Ito

View

Download (PDF)

Tags: Algorithms, Compression, Computer science, CUDA, nVidia, nVidia GeForce GTX 1080

January 19, 2017 by hgpu

Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models

Monica Denham, Karina Laneri, Juan Manuel Morales

View

Download (PDF)

Source codes

Tags: Biology, CUDA, Earth and Space Sciences, nVidia, nVidia GeForce GTX 780, nVidia GeForce GTX Titan, Package, Tesla C2070, Tesla K40

January 16, 2017 by hgpu

Decoding with Finite-State Transducers on GPUs

Arturo Argueta, David Chiang

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Machine learning, NLP, nVidia, nVidia GeForce GTX Titan X, Package, Speech recognition, Tesla K40, Tesla P100

January 16, 2017 by hgpu

DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning

Tian Zhao, Xiaobing Huang, Yu Cao

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, DSL, Java, Machine learning, nVidia, Package, Programming Languages, Scala, Tesla K40

January 10, 2017 by hgpu

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

DeepBach: a Steerable Model for Bach chorales generation

A task-driven implementation of a simple numerical solver for hyperbolic conservation laws

GPGPU Performance Estimation with Core and Memory Frequency Scaling

OpenNMT: Open-Source Toolkit for Neural Machine Translation

Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

Light Loss-Less Data Compression, with GPU Implementation

Using efficient parallelization in Graphic Processing Units to parameterize stochastic fire propagation models

Decoding with Finite-State Transducers on GPUs

DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning

Recent source codes

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

Most viewed papers (last 30 days)