high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

Assessing the feasibility of OpenCL CPU implementations for agent-based simulations

Nuno Fachada, Agostinho C. Rosa

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Tags: Agent-based modeling, AMD, cpu, Intel, OpenCL, simulation

July 24, 2019 by fakenmc

Sorting on FPGAs using Merge Trees

Nikola Samardzic

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Tags: Algorithms, Computer science, FPGA, Sorting

July 21, 2019 by hgpu

A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

Peng Chen, Mohamed Wahib, Shinichiro Takizawa, Ryousei Takano, Satoshi Matsuoka

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Tags: Computer science, CUDA, nVidia, Package, Stencil computation, Tesla P100, Tesla V100

July 21, 2019 by hgpu

A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition

Wei Zhang, Xiaodong Cui, Ulrich Finkler, George Saon, Abdullah Kayi, Alper Buyuktosunoglu, Brian Kingsbury, David Kung, Michael Picheny

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Tags: Algorithms, Computer science, CUDA, Deep learning, Distributed computing, GPU cluster, nVidia, OpenMPI, Speech recognition, Tesla V100

July 21, 2019 by hgpu

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

Prasad Bhavana, Vikas Kumar, Vineet Padmanabhan

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Tags: AI, Algorithms, Computer science, CUDA, Factorization, Machine learning, nVidia, Package, Tesla M40

July 21, 2019 by hgpu

On the Portability of CPU-Accelerated Applications via Automated Source-to-Source Translation

Paul Sathre, Mark Gardner, Wu-chun Feng

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Tags: AMD FirePro S9150, ATI, Compilers, Computer science, CUDA, FPGA, nVidia, OpenCL, Package, performance portability, Tesla K80

July 14, 2019 by hgpu

HashGraph – Scalable Hash Tables Using A Sparse Graph Data Structure

Oded Green

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Tags: Algorithms, Computer science, CUDA, Databases, Graph theory, nVidia, nVidia Quadro GV100

July 14, 2019 by hgpu

A Translation Framework from RVC-CAL Dataflow Programs to OpenCL/SYCL based Implementations

Florian Krebs

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Tags: Compilers, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 950 M, OpenCL, SYCL, Thesis

July 14, 2019 by hgpu

Implementation of high speed hash function Keccak on GPU

Thuong Nguyen Dat, Keisuke Iwai, Takashi Matsubara, Takakazu Kurokawa

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX 1080, Security

July 14, 2019 by hgpu

Profiling based Out-of-core Hybrid Method for Large Neural Networks

Yuki Ito, Haruki Imai, Tung Le Duc, Yasushi Negishi, Kiyokuni Kawachiya, Ryo Matsumiya, Toshio Endo

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Performance, Tesla V100

July 14, 2019 by hgpu

GPU-based Parallel Computation Support for Stan

Rok Cesnovar, Steve Bronder, Davor Sluga, Jure Demsar, Tadej Ciglaric, Sean Talts, Erik Strumbelj

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Tags: AMD Radeon R9 Fury, ATI, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan XP, OpenCL, Package, Tesla V100

July 10, 2019 by hgpu

Exploring Portability and Performance of OpenCL FPGA Kernels on Intel HARPv2

Anthony M. Cabrera, Roger D. Chamberlain

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

July 7, 2019 by hgpu

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

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

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

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

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high performance computing on graphics processing units: hgpu.org

Programming

Assessing the feasibility of OpenCL CPU implementations for agent-based simulations

Sorting on FPGAs using Merge Trees

A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels

A Highly Efficient Distributed Deep Learning System For Automatic Speech Recognition

Block based Singular Value Decomposition approach to matrix factorization for recommender systems

On the Portability of CPU-Accelerated Applications via Automated Source-to-Source Translation

HashGraph – Scalable Hash Tables Using A Sparse Graph Data Structure

A Translation Framework from RVC-CAL Dataflow Programs to OpenCL/SYCL based Implementations

Implementation of high speed hash function Keccak on GPU

Profiling based Out-of-core Hybrid Method for Large Neural Networks

GPU-based Parallel Computation Support for Stan

Exploring Portability and Performance of OpenCL FPGA Kernels on Intel HARPv2

Recent source codes

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

ROCm's implementation of Gromacs

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

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