high performance computing on graphics processing units: hgpu.org

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Mixing Low-Precision Formats in Multiply-Accumulate Units for DNN Training

Mariko Tatsumi, Silviu-Ioan Filip, Caroline White, Olivier Sentieys, Guy Lemieux

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Tags: Computer science, CUDA, Deep learning, FPGA, nVidia, Precision

December 11, 2022 by hgpu

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

Boyuan Feng, Yuke Wang, Tong Geng, Ang Li, Yufei Ding

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Tags: Algorithms, Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce RTX 3090, Package, Precision, Python, Tesla A100

June 27, 2021 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

FPGA Implementation of Reduced Precision Convolutional Neural Networks

Muhammad Mohid Nabil

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Tags: Computer science, CUDA, Deep learning, FPGA, HLS, Image recognition, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, Precision, Thesis

May 12, 2019 by hgpu

Quasi-maximum Accuracy Floating-point Computations with GPGPU for Applications in Digital Signal Processing

Adam Dabrowski, Pawel Pawlowski, Mateusz Stankiewicz, Filip Misiorek

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Tags: CUDA, nVidia, nVidia Quadro FX 580, Precision, Signal processing

December 24, 2011 by hgpu

Precision and Performance: Floating Point and IEEE 754 Compliance for NVIDIA GPUs

Nathan Whitehead, Alex Fit-Florea

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Tags: Computer science, CUDA, nVidia, Precision

October 26, 2011 by hgpu

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

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Mixing Low-Precision Formats in Multiply-Accumulate Units for DNN Training

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

Tools for Reduced Precision Computation: A Survey

FPGA Implementation of Reduced Precision Convolutional Neural Networks

Quasi-maximum Accuracy Floating-point Computations with GPGPU for Applications in Digital Signal Processing

Precision and Performance: Floating Point and IEEE 754 Compliance for NVIDIA GPUs

Recent source codes

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

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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