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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

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GPJSON: GPU-based JSON data processing system accessible via all GraalVM languages

GPU-based JSON data processing using structural indexes

NEPTUNE: Network- and GPU-aware management of serverless functions at the edge

NEPTUNE: Network- and GPU-aware Management of Serverless Functions at the Edge

HeCBench: a collection of Heterogeneous Computing benchmarks written with CUDA, HIP, SYCL (DPC++), and OpenMP-4.5

Experience of Migrating a Parallel Graph Coloring Program from CUDA to SYCL

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

WARPSPEED: An analytic Data Volume and Performance Estimator for GPU Kernels

Analytical Performance Estimation during Code Generation on Modern GPUs

Celerity HLA: A high-level API on top of Celerity to support functional, C++20 range-adaptors-like patterns

The Celerity High-level API: C++20 for Accelerator Clusters

GPU-Sched

CASE: A Compiler-Assisted SchEduling Framework for Multi-GPU Systems

Efficient Execution of OpenMP on GPUs

Explicit Caching HYB based SpMV

Explicit caching HYB: a new high-performance SpMV framework on GPGPU

matrix_inversion algorithm

Performance study on GPU offloading techniques using the Gauss matrix inverse algorithm

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high performance computing on graphics processing units: hgpu.org

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

GPJSON: GPU-based JSON data processing system accessible via all GraalVM languages

NEPTUNE: Network- and GPU-aware management of serverless functions at the edge

HeCBench: a collection of Heterogeneous Computing benchmarks written with CUDA, HIP, SYCL (DPC++), and OpenMP-4.5

cuPSO: GPU Parallelization for Particle Swarm Optimization Algorithms

WARPSPEED: An analytic Data Volume and Performance Estimator for GPU Kernels

Celerity HLA: A high-level API on top of Celerity to support functional, C++20 range-adaptors-like patterns

GPU-Sched

Efficient Execution of OpenMP on GPUs

Explicit Caching HYB based SpMV

matrix_inversion algorithm

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