high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

Richard Membarth, Oliver Reiche, Frank Hannig, Jurgen Teich, Mario Korner, Wieland Eckert

View

Download (PDF)

Source codes

Tags: Algorithms, AMD Radeon R9 290X, ARM, ATI, Compilers, Computer science, CUDA, Heterogeneous systems, Image processing, Intel Xeon Phi, nVidia, OpenCL, Package, Tesla K20

November 19, 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

Performance Analysis of Parallel Sorting Algorithms using GPU Computing

Neetu Faujdar, SP Ghrera

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Performance, Sorting

November 19, 2016 by hgpu

Lattice QCD simulations using the OpenACC platform

Pushan Majumdar

View

Download (PDF)

Tags: CUDA, HEP, High Energy Physics - Lattice, nVidia, OpenACC, Physics, QCD, Tesla X2090

November 19, 2016 by hgpu

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Pedro Bruel, Marcos Amaris, Alfredo Goldman

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 980, Package, Performance, Tesla K40

November 16, 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

Data Acquisition with GPUs: The DAQ for the Muon g-2 Experiment at Fermilab

W. Gohn

View

Download (PDF)

Tags: CUDA, Data acquisition, nVidia, Physics, Tesla K40

November 16, 2016 by hgpu

Automatic code generation methods applied to numerical linear algebra in high performance computing

M. Ian Masliah

View

Download (PDF)

Tags: Algorithms, Code generation, Computer science, CUDA, Heterogeneous systems, Linear Algebra, Mixed precision, nVidia, nVidia Tegra TX1, Programming techniques, Tesla C2075, Tesla K40, Thesis

November 16, 2016 by hgpu

CUDA-API-wrappers: Thin C++-flavored wrappers for the CUDA runtime API

Eyal Rozenberg

Source codes

Tags: CUDA, nVidia, Package

November 13, 2016 by epk

Fractal Art Generation using GPUs

Will.D. Mayfield, Justin.C. Eiland, Taylor.J. Hutyra, Matt.C. Paulsen, Bryant.M. Wyatt

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Graphics, Image generation, nVidia, nVidia GeForce GTX Titan

November 13, 2016 by hgpu

Shuffle Reduction Based Sparse Matrix-Vector Multiplication on Kepler GPU

Yuan Tao, Huang Zhi-Bin

View

Download (PDF)

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

November 10, 2016 by hgpu

Memory layout in GPU implementation of lattice Boltzmann method for sparse 3D geometries

Tadeusz Tomczak, Roman G. Szafran

View

Download (PDF)

Tags: cfd, CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia GeForce GTX Titan

November 10, 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

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

How to scale distributed deep learning?

Performance Analysis of Parallel Sorting Algorithms using GPU Computing

Lattice QCD simulations using the OpenACC platform

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Efficient Communications in Training Large Scale Neural Networks

Data Acquisition with GPUs: The DAQ for the Muon g-2 Experiment at Fermilab

Automatic code generation methods applied to numerical linear algebra in high performance computing

CUDA-API-wrappers: Thin C++-flavored wrappers for the CUDA runtime API

Fractal Art Generation using GPUs

Shuffle Reduction Based Sparse Matrix-Vector Multiplication on Kepler GPU

Memory layout in GPU implementation of lattice Boltzmann method for sparse 3D geometries

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)