high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

A Runtime Controller for OpenCL Applications on Heterogeneous System Architectures

Cristiana Bolchini, Stefano Cherubin, Gianluca C. Durelli, Simone Libutti, Antonio Miele, Marco D. Santambrogio

View

Download (PDF)

Tags: ARM, Computer science, Heterogeneous systems, OpenCL, Operating systems

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

Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

Roberto DiCecco, Griffin Lacey, Jasmina Vasiljevic, Paul Chow, Graham Taylor, Shawki Areibi

View

Download (PDF)

Source codes

Tags: Benchmarking, Caffe, Computer science, Deep learning, FPGA, Machine learning, Neural networks, OpenCL, Package

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

Programming Models and Tools for Many-Core Platforms

Alessandro Capotondi

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, OpenCL, OpenMP, SoC, Thesis

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

Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs

R. Tapiador, A. Rios-Navarro, A. Linares-Barranco, Minkyu Kim, Deepak Kadetotad, Jae-sun Seo

View

Download (PDF)

Tags: Artificial intelligence, Caffe, Computer science, Deep learning, FPGA, Neural networks, OpenCL

September 30, 2016 by hgpu

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

September 27, 2016 by hgpu

FastCollect: Offloading Generational Garbage Collection to Integrated GPUs

Abhinav, Rupesh Nasre

View

Download (PDF)

Tags: Algorithms, Benchmarking, Computer science, Intel HD Graphics 4000, Java, OpenCL, Performance

September 27, 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_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

* * *

high performance computing on graphics processing units: hgpu.org

Programming

A Runtime Controller for OpenCL Applications on Heterogeneous System Architectures

Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

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

Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks

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

Programming Models and Tools for Many-Core Platforms

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

Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

FastCollect: Offloading Generational Garbage Collection to Integrated GPUs

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

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

ROCm's implementation of Gromacs

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

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

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

Most viewed papers (last 30 days)