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Importance of Data Loading Pipeline in Training Deep Neural Networks

Mahdi Zolnouri, Xinlin Li, Vahid Partovi Nia

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan, Package, Tesla V100

May 10, 2020 by hgpu

Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels

Kaijie Fan, Biagio Cosenza, Ben Juurlink

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Tags: Computer science, Energy-efficient computing, Machine learning, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla P100

May 10, 2020 by hgpu

An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms

J. Santamaría, M. L. Rivero-Cejudo, M. A. Martos-Fernández, F. Roca

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Tags: Computer science, Computer vision, Evolutionary Computations, Image registration, Metaheuristics, Neural and Evolutionary Computing, Overview

May 4, 2020 by hgpu

Tools for GPU Computing – Debugging and Performance Analysis of Heterogenous HPC Applications

Michael Knobloch, Bernd Mohr

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Tags: Computer science, CUDA, HPC, nVidia, OpenACC, OpenCL, Performance

May 3, 2020 by hgpu

AutoParBench: A Unified Test Framework for OpenMP-based Parallelizers

Gleison Souza Diniz Mendonça, Chunhua Liao, Fernando Magno Quintão Pereira

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Tags: Benchmarking, Code generation, Computer science, OpenMP, Package

May 3, 2020 by hgpu

Leveraging Data-Flow Information for Efficient Scheduling of Task-Parallel Programs on Heterogeneous Systems

Osman Seckin Simsek

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Tags: Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan V, OpenCL, performance portability, Tesla K20, Thesis

May 3, 2020 by hgpu

Tools for Reduced Precision Computation: A Survey

Stefano Cherubin, Giovanni Agosta

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Tags: Approximate computing and storage, Computer science, CUDA, HPC, Mixed precision, nVidia, OpenCL, Precision, survey

May 3, 2020 by hgpu

Automatic Parallelization for Heterogeneous Embedded Systems

Rokiatou Diarra

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Quadro M 2000 M, nVidia Tegra TX1, OpenACC, OpenCL, Thesis

April 26, 2020 by hgpu

Accelerating Winograd Convolutions using Symbolic Computation and Meta-programming

Arya Mazaheri, Tim Beringer, Matthew Moskewicz, Felix Wolf, Ali Jannesari

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Tags: AMD Radeon RX 580, ATI, Code generation, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, OpenCL, Performance, performance portability, Symbolic Computation, Vulkan

April 26, 2020 by hgpu

Evaluating FPGA Accelerator Performance with a Parameterized OpenCL Adaptation of the HPCChallenge Benchmark Suite

Marius Meyer, Tobias Kenter, Christian Plessl

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Tags: Benchmarking, Computer science, FPGA, HLS, OpenCL, Package

April 26, 2020 by hgpu

GEVO: GPU Code Optimization using Evolutionary Computation

Jhe-Yu Liou, Xiaodong Wang, Stephanie Forrest, Carole-Jean Wu

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Tags: Computer science, CUDA, Evolutionary Computations, Machine learning, Neural and Evolutionary Computing, nVidia, Package, PTX, Tesla P100

April 26, 2020 by hgpu

Cpp-Taskflow: A General-purpose Parallel and Heterogeneous Task Programming System at Scale

Tsung-Wei Huang, Dian-Lun Lin, Yibo Lin, Chun-Xun Lin

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce RTX 2080, Package, Performance

April 26, 2020 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

Applications

Importance of Data Loading Pipeline in Training Deep Neural Networks

Accurate Energy and Performance Prediction for Frequency-Scaled GPU Kernels

An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms

Tools for GPU Computing – Debugging and Performance Analysis of Heterogenous HPC Applications

AutoParBench: A Unified Test Framework for OpenMP-based Parallelizers

Leveraging Data-Flow Information for Efficient Scheduling of Task-Parallel Programs on Heterogeneous Systems

Tools for Reduced Precision Computation: A Survey

Automatic Parallelization for Heterogeneous Embedded Systems

Accelerating Winograd Convolutions using Symbolic Computation and Meta-programming

Evaluating FPGA Accelerator Performance with a Parameterized OpenCL Adaptation of the HPCChallenge Benchmark Suite

GEVO: GPU Code Optimization using Evolutionary Computation

Cpp-Taskflow: A General-purpose Parallel and Heterogeneous Task Programming System at Scale

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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