high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming » OpenCL

FPGA in HPC: High Level Synthesys of OpenCL kernels for Molecular Dynamics

Paolo Gorlani

View

Download (PDF)

Tags: Computer science, FPGA, Molecular dynamics, OpenCL, Thesis

March 22, 2018 by hgpu

HPVM: Heterogeneous Parallel Virtual Machine

Maria Kotsifakou, Prakalp Srivastava, Matthew D. Sinclair, Rakesh Komuravelli, Vikram Adve, Sarita Adve

View

Download (PDF)

Tags: Code generation, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 680, OpenCL, performance portability, Virtualization

March 17, 2018 by hgpu

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

Stylianos I. Venieris, Alexandros Kouris, Christos-Savvas Bouganis

View

Download (PDF)

Tags: Artificial intelligence, Computer science, Deep learning, Design space exploration, FPGA, OpenCL, survey

March 17, 2018 by hgpu

Improved OpenCL-based Implementation of Social Field Pedestrian Model

Bin Yu, Ke Zhu, Kaiteng Wu, Michael Zhang

View

Download (PDF)

Tags: Algorithms, Computer science, OpenCL

March 17, 2018 by hgpu

Portable Real-Time DCT Based Steganography Using OpenCL

Ante Poljicak, Guillermo Botella, Carlos Garcia, Luka Kedmenec, Manuel Prieto-Matias

View

Download (PDF)

Tags: APU, ARM, ATI, ATI Radeon HD 6870, Image processing, nVidia, nVidia GeForce GTX 980, OpenCL, real time systems

March 10, 2018 by hgpu

OpenCL-ready High Speed FPGA Network for Reconfigurable High Performance Computing

Ryohei Kobayashi, Yuma Oobata, Norihisa Fujita, Yoshiki Yamaguchi, Taisuke Boku

View

Download (PDF)

Tags: Algorithms, Computer science, FPGA, Heterogeneous systems, OpenCL

March 10, 2018 by hgpu

OpenCL Acceleration for TensorFlow

Mehdi Goli, Luke Iwanski, John Lawson, Uwe Dolinsky, Andrew Richards

View

Download (PDF)

Tags: ARM, Computer science, Deep learning, Machine learning, OpenCL, Performance, SYCL, TensorFlow

March 3, 2018 by hgpu

Evaluating High-Level Synthesis Techniques for Scalable Hardware-Accelerated Computing

Alfonso Rodriguez, Cesar Castanares, Teresa Riesgo, Eduardo de la Torre

View

Download (PDF)

Tags: ARM, Computer science, FPGA, nVidia, nVidia GeForce GTX Titan X, OpenCL

February 17, 2018 by hgpu

The performances of R GPU implementations of the GMRES method

Bogdan Oancea, Richard Pospisil

View

Download (PDF)

Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 840 M, OpenCL, Performance, R

February 17, 2018 by hgpu

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Philippe Tillet, David Cox

View

Download (PDF)

Source codes

Tags: Auto-Tuning, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 980 Ti, OpenCL, Package, Performance, PTX, Tesla P100

February 17, 2018 by hgpu

Accelerating Interpreted Programming Languages on GPUs with Just-In-Time Compilation and Runtime Optimisations

Juan Jose Fumero Alfonso

View

Download (PDF)

Tags: AMD Radeon R9 295X2, ATI, Computer science, Java, nVidia, nVidia GeForce GTX Titan Black, OpenCL, R, Thesis

February 15, 2018 by hgpu

TVM: End-to-End Optimization Stack for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Computer science, CUDA, Deep learning, FPGA, Machine learning, nVidia, nVidia GeForce GTX 1080, OpenCL, Package, performance portability, TensorFlow, Tesla K80

February 15, 2018 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

FPGA in HPC: High Level Synthesys of OpenCL kernels for Molecular Dynamics

HPVM: Heterogeneous Parallel Virtual Machine

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

Improved OpenCL-based Implementation of Social Field Pedestrian Model

Portable Real-Time DCT Based Steganography Using OpenCL

OpenCL-ready High Speed FPGA Network for Reconfigurable High Performance Computing

OpenCL Acceleration for TensorFlow

Evaluating High-Level Synthesis Techniques for Scalable Hardware-Accelerated Computing

The performances of R GPU implementations of the GMRES method

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Accelerating Interpreted Programming Languages on GPUs with Just-In-Time Compilation and Runtime Optimisations

TVM: End-to-End Optimization Stack for Deep Learning

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)