high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Solving Batched Linear Programs on GPU and Multicore CPU

Amit Gurung, Rajarshi Ray

View

Tags: Computer science, CUDA, nVidia, Package, Performance, Tesla K40

September 27, 2016 by hgpu

A Novel GPU-based Parallel Implementation Scheme and Performance Analysis of Robot Forward Dynamics Algorithms

Yajue Yang, Yuanqing Wu, Jia Pan

View

Tags: Algorithms, Computer science, CUDA, nVidia, Robotics, Tesla K40

September 27, 2016 by hgpu

Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, Jeffrey Dean

View

Tags: Computer science, CUDA, Deep learning, LSTM, Machine learning, NLP, nVidia, Tesla K80

September 27, 2016 by hgpu

Tuning Stencil Codes in OpenCL for FPGAs

Qi Jia, Huiyang Zhou

View

Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, Package, Stencil computation

September 22, 2016 by hgpu

Bridging the Semantic Gaps of GPU Acceleration for Scaleout CNN-based Big Data Processing: Think Big, See Small

Mingcong Song, Yang Hu, Yunlong Xu, Chao Li, Huixiang Chen, Jingling Yuan, Tao Li

View

Tags: Analytical model, big data, Computer science, CUDA, Deep learning, Distributed computing, Neural networks, nVidia, Tesla K20

September 22, 2016 by hgpu

Characterization of Speech Recognition Systems on GPU Architectures

Albert Segura Salvador

View

Tags: Algorithms, Computer science, CUDA, Deep learning, GPGPU-sim, nVidia, nVidia GeForce GTX 980, Speech recognition, Thesis

September 22, 2016 by hgpu

Efficient dictionary learning implementation on the GPU using OpenCL

Paul Irofti

View

Tags: Algorithms, Computer science, Machine learning, OpenCL, Signal processing

September 22, 2016 by hgpu

MCS 572: Introduction to Supercomputing

Jan Verschelde

View

Tags: Algorithms, Computer science, CUDA, MPI, Numerical Analysis, nVidia, nVidia GeForce 9400 M, OpenMP, Package, Tesla C2050, Tesla C2070, Tesla K20, Tutorial

September 22, 2016 by hgpu

Acceleration of Block-Aware Matrix Factorization on Heterogeneous Platforms

Gregory W. Somers

View

Tags: ATI, ATI Radeon HD 7950, Computer science, Factorization, Heterogeneous systems, Linear Algebra, OpenCL, Thesis

September 20, 2016 by hgpu

A Compiler for Throughput Optimization of Graph Algorithms on GPUs

Sreepathi Pai, Keshav Pingali

View

Tags: Compilers, Computer science, CUDA, Graph theory, nVidia, Tesla K40

September 20, 2016 by hgpu

Feynman Machine: The Universal Dynamical Systems Computer

Eric Laukien, Richard Crowder, Fergal Byrne

View

Tags: AMD Radeon R9 290, Artificial intelligence, ATI, Computer science, Deep learning, Mathematics, Neuroscience, OpenCL

September 20, 2016 by hgpu

Runtime Support for Adaptive Power Capping on Heterogeneous SoCs

Yun Wu, Dimitrios S. Nikolopoulos, Roger Woods

View

Tags: ARM, Benchmarking, Computer science, Energy-efficient computing, FPGA, Heterogeneous systems, OpenCL, OpenMP, SoC

September 20, 2016 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

GPU plugin for PySCF

Python-Based Quantum Chemistry Calculations with GPU Acceleration

QArray

QArray: a GPU-accelerated constant capacitance model simulator for large quantum dot arrays

Celerity: High-level C++ for Accelerator Clusters

Balancing Tracking Granularity and Parallelism in Many-Task Systems: The Horizons Approach

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

* * *

* * *

HGPU group © 2010-2024 hgpu.org

All rights belong to the respective authors

Login | Sitemap | Feedback | Policy

Contact us: