high performance computing on graphics processing units: hgpu.org

hgpu.org » CUDA

Accelerating Spark RDD Operations with Local and Remote GPU Devices

Yasuhiro Ohno, Shin Morishima, Hiroki Matsutani

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 980 Ti, Spark

December 20, 2016 by hgpu

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Daniel Winkler, Michael Meister, Massoud Rezavand, Wolfgang Rauch

View

Download (PDF)

Tags: Algorithms, CUDA, Fluid dynamics, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Physics

December 20, 2016 by hgpu

Efficient Realization of Householder Transform through Algorithm-Architecture Co-design for Acceleration of QR Factorization

Farhad Merchant, Tarun Vatwani, Anupam Chattopadhyay, Soumyendu Raha, S K Nandy, Ranjani Narayan

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Factorization, FPGA, Linear Algebra, Mathematical Software, Matrix multiplication, nVidia, Performance, Tesla C2050

December 17, 2016 by hgpu

Speedup for quantum optimal control from GPU-based automatic differentiation

Nelson Leung, Mohamed Abdelhafez, Jens Koch, David I. Schuster

View

Download (PDF)

Source codes

Tags: Algorithms, CUDA, Deep learning, Mesoscale and Nanoscale Physics, Numerical simulation, nVidia, Package, Physics, Python, Quantum Physics, TensorFlow, Tesla K40

December 17, 2016 by hgpu

Parallel Level set algorithm with MPI and accelerated on GPU

Zhenlin Wang

View

Download (PDF)

Source codes

Tags: Computational Physics, CUDA, Finite difference, MPI, nVidia, Package, PDEs, Physics

December 17, 2016 by hgpu

Towards Comprehensive Parametric Code Generation Targeting Graphics Processing Units in Support of Scientific Computation

Ning Xie

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, nVidia, OpenMP, Tesla M2050, Thesis

December 14, 2016 by hgpu

nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware

Sven Koitka, Christoph M. Friedrich

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Factorization, nVidia, nVidia GeForce GTX Titan X, Package, Tesla K80

December 14, 2016 by hgpu

GaDei: On Scale-up Training As A Service For Deep Learning

Wei Zhang, Minwei Feng, Yunhui Zheng, Yufei Ren, Yandong Wang, Ji Liu, Peng Liu, Bing Xiang, Li Zhang, Bowen Zhou

View

Download (PDF)

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

December 14, 2016 by hgpu

cusFFT: A High-Performance Sparse Fast Fourier Transform Algorithm on GPUs

Cheng Wang, Sunita Chandrasekaran, Barbara Chapman

View

Download (PDF)

Tags: CUDA, FFT, Tesla K20

December 10, 2016 by hgpu

Implementing and Evaluating Candidate-Based Invariant Generation

Adam Betts, Nathan Chong, Pantazis Deligiannis, Alastair F. Donaldson, Jeroen Ketema

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, nVidia, OpenCL, Software Engineering

December 10, 2016 by hgpu

GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

Syed Tahir Hussain Rizvi, Gianpiero Cabodi, Denis Patti, Gianluca Francini

View

Download (PDF)

Tags: CNN, Computer science, CUDA, Deep learning, Heterogeneous systems, Image processing, Matrix multiplication, Neural networks, nVidia, nVidia Jetson TK1

December 10, 2016 by hgpu

Parallelization and Performance of the NIM Weather Model for CPU, GPU and MIC Processors

Mark Govett, Jim Rosinski, Jacques Middlecoff, Tom Henderson, Jin Lee, Alexander MacDonald, Paul Madden, Julie Schramm, Antonio Duarte

View

Download (PDF)

Tags: CUDA, Earth and Space Sciences, Fortran, Intel Xeon Phi, nVidia, OpenACC, Tesla K80, Weather prediction, Weather Prediction Model

December 6, 2016 by hgpu

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Accelerating Spark RDD Operations with Local and Remote GPU Devices

gpuSPHASE – A shared memory caching implementation for 2D SPH using CUDA

Efficient Realization of Householder Transform through Algorithm-Architecture Co-design for Acceleration of QR Factorization

Speedup for quantum optimal control from GPU-based automatic differentiation

Parallel Level set algorithm with MPI and accelerated on GPU

Towards Comprehensive Parametric Code Generation Targeting Graphics Processing Units in Support of Scientific Computation

nmfgpu4R: GPU-Accelerated Computation of the Non-Negative Matrix Factorization (NMF) Using CUDA Capable Hardware

GaDei: On Scale-up Training As A Service For Deep Learning

cusFFT: A High-Performance Sparse Fast Fourier Transform Algorithm on GPUs

Implementing and Evaluating Candidate-Based Invariant Generation

GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

Parallelization and Performance of the NIM Weather Model for CPU, GPU and MIC Processors

Recent source codes

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

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Most viewed papers (last 30 days)