high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia

SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs

Kaiwei Li, Jianfei Chen, Wenguang Chen, Jun Zhu

View

Tags: Algorithms, Computer science, CUDA, Information Retrieval, Latent Dirichlet allocation, Machine learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX Titan X

October 12, 2016 by hgpu

Implementation of Frequency Domain Convolution for the Caffe-Framework

Philipp Harzig

View

Download (PDF)

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

October 8, 2016 by hgpu

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

Pilar Cossio, David Rohr, Fabio Baruffa, Markus Rampp, Volker Lindenstruth, Gerhard Hummer

View

Download (PDF)

Source codes

Tags: Bayesian, Biology, Biomolecules, CUDA, Electron microscopy, Heterogeneous systems, Medicine, Microscopy, MPI, nVidia, nVidia GeForce GTX Titan, OpenMP, Package, Tesla K20, Tomography

October 8, 2016 by hgpu

Rinnegan: Efficient Resource Use in Heterogeneous Architectures

Sankaralingam Panneerselvam, Michael Swift

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 650, nVidia GeForce GTX 670, OpenCL

October 8, 2016 by hgpu

GPU Concurrency Choices in Graph Analytics

Masab Ahmad, Omer Khan

View

Download (PDF)

Tags: Algorithms, Computer science, Graph theory, nVidia, nVidia GeForce GTX 750 Ti, nVidia GeForce GTX 970, OpenCL

October 8, 2016 by hgpu

Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

Jiaquan Gao, Panpan Qi, Guixia He

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla C2050, Tesla K20

October 4, 2016 by hgpu

APL on GPUs: A TAIL from the Past, Scribbled in Futhark

Troels Henriksen, Martin Dybdal, Henrik Urms, Anna Sofie Kiehn, Daniel Gavin, Hjalte Abelskov, Martin Elsman, Cosmin Oancea

View

Download (PDF)

Source codes

Tags: Compilers, Computer science, nVidia, nVidia GeForce GTX 780 Ti, OpenCL, Package, PyOpenCL, Python, Visualization

October 4, 2016 by hgpu

Training a Feedback Loop for Hand Pose Estimation

Markus Oberweger, Paul Wohlhart, Vincent Lepetit

View

Download (PDF)

Source codes

Tags: Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX 780 Ti, Package, Theano

October 4, 2016 by hgpu

GPU-based timetable generation

Ricardo Filipe Pereira Neves

View

Download (PDF)

Tags: Computer science, CUDA, Genetic algorithm, nVidia, nVidia GeForce GTX 770, Thesis

September 30, 2016 by hgpu

Distributed Training of Deep Neuronal Networks: Theoretical and Practical Limits of Parallel Scalability

Janis Keuper

View

Download (PDF)

Tags: Caffe, Computer science, CUDA, Deep learning, Intel Xeon Phi, Neural networks, nVidia, Tesla K80

September 30, 2016 by hgpu

Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform

Sebastian Cammerer, Benedikt Leible, Matthias Stahl, Jakob Hoydis, Stephan ten Brink

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980 Ti, Signal processing

September 30, 2016 by hgpu

Efficient and portable acceleration of quantum chemical many-body methods in mixed floating point precision using OpenACC compiler directives

Janus Juul Eriksen

View

Download (PDF)

Tags: Chemical Physics, Chemistry, Mixed precision, nVidia, OpenACC, Physics, Tesla K40

September 27, 2016 by hgpu

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

ThunderKittens: Tile primitives for speedy kernels

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

NVIDIA Nemotron Parse 1.1

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

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs

Implementation of Frequency Domain Convolution for the Caffe-Framework

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

Rinnegan: Efficient Resource Use in Heterogeneous Architectures

GPU Concurrency Choices in Graph Analytics

Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

APL on GPUs: A TAIL from the Past, Scribbled in Futhark

Training a Feedback Loop for Hand Pose Estimation

GPU-based timetable generation

Distributed Training of Deep Neuronal Networks: Theoretical and Practical Limits of Parallel Scalability

Combining Belief Propagation and Successive Cancellation List Decoding of Polar Codes on a GPU Platform

Efficient and portable acceleration of quantum chemical many-body methods in mixed floating point precision using OpenACC compiler directives

Recent source codes

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

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

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

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

Most viewed papers (last 30 days)