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Array Languages Make Neural Networks Fast

Artjoms Šinkarovs, Hans-Nikolai Vießmann, Sven-Bodo Scholz

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Tags: Computer science, CUDA, High-level Languages, nVidia, Package, Python, PyTorch, TensorFlow, Tesla K20

December 15, 2019 by hgpu

PyTorch: An Imperative Style, High-Performance Deep Learning Library

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Yang, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia Quadro GP100, Package, Python, PyTorch

December 8, 2019 by hgpu

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

Juan Ruiz-Rosero, Gustavo Ramirez-Gonzalez, Rahul Khanna

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Tags: Computer science, OpenCL, Package

December 8, 2019 by hgpu

FBLAS: Streaming Linear Algebra Kernels on FPGA

Tiziano De Matteis, Johannes de Fine Licht, Torsten Hoefler

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Tags: BLAS, Computer science, FPGA, Linear Algebra, OpenCL, Package

December 1, 2019 by hgpu

FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things

Xiaying Wang, Michele Magno, Lukas Cavigelli, Luca Benini

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Tags: Algorithms, Computer science, IoT, Machine learning, Neural networks, Package

December 1, 2019 by hgpu

Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA

Jacob Austin, Rafael Corrales-Fatou, Sofia Wyetzner, Hod Lipson

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Package, Robotics

December 1, 2019 by hgpu

Benchmarking Deep Learning Models on Jetson TX2

Lucas Pedro Bordignon, Aldo von Wangenheim

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Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, Image recognition, nVidia, nVidia Jetson TX2, Overview, Package, Tutorial

November 24, 2019 by hgpu

Hacking Neural Networks: A Short Introduction

Michael Kissner

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Security

November 24, 2019 by hgpu

A Computing Kernel for Network Binarization on PyTorch

Xianda Xu, Marco Pedersoli

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080 Ti, Package

November 17, 2019 by hgpu

Deep Learning Based FPGA-CPU Acceleration

R. Luis Jalabert

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Tags: Code generation, Computer science, Deep learning, FPGA, Machine learning, Neural networks, Package, Thesis

November 17, 2019 by hgpu

Framework for Parallel Kernels Auto-tuning

Filip Petrovič

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Tags: Auto-Tuning, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 750, nVidia GeForce GTX 970, OpenCL, Package, Performance, Thesis

November 10, 2019 by hgpu

Study of OpenCL Processing Models for FPGA Devices

Piotr Szkotak, Pawel Russek, Kazimierz Wiatr

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Tags: Computer science, FPGA, OpenCL, Package, Security

November 10, 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 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

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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