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Advancing the distributed Multi-GPU ChASE library through algorithm optimization and NCCL library

Xinzhe Wu, Edoardo Di Napoli

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Tags: Algorithm optimization, Computer science, CUDA, MPI, nVidia, nVidia A100, OpenMP, OpenMPI, Package

October 8, 2023 by hgpu

EDSSA: An Encoder-Decoder Semantic Segmentation Networks Accelerator on OpenCL-Based FPGA Platform

Hongzhi Huang, Yakun Wu, Mengqi Yu, Xuesong Shi, Fei Qiao, Li Luo, Qi Wei, Xinjun Liu

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Tags: Algorithm optimization, Computer science, Computer vision, FPGA, Neural networks, OpenCL

July 26, 2020 by hgpu

Implementing Push-Pull Efficiently in GraphBLAS

Carl Yang, Aydin Buluc, John D. Owens

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Tags: Algorithm optimization, Algorithms, BLAS, Computer science, CUDA, Graph theory, Linear Algebra, nVidia, Sparse matrix, Tesla K40

April 15, 2018 by hgpu

Accelerated Combinatorial Optimization using Graphics Processing Units and C++ AMP

Alexandru Voicu

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Tags: Algorithm optimization, Ant colony optimization, C++ AMP

September 8, 2014 by AlexVlx

Investigating performance variations of an optimized GPU-ported granulometry algorithm

Vincent Boulos, Vincent Fristot, Dominique Houzet, Luc Salvo, Pierre Lhuissier

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Tags: Algorithm optimization, Algorithms, CUDA, FEM, Finite element method, Image processing, Materials Science, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, nVidia Quadro FX 4000

February 22, 2013 by hgpu

Is the game worth the candle? Evaluation of OpenCL for object detection algorithm optimization

Floris De Smedt, Lars Stuyf, Sander Beckers, Joost Vennekens, Gorik De Samblanx, Toon Goedeme

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Tags: Algorithm optimization, Algorithms, Computer science, Computer vision, nVidia, nVidia GeForce GTX 295, OpenCL

October 5, 2012 by hgpu

Image processing algorithm optimization with CUDA for Pure Data

Rudi Giot, Abilio Rodrigues e Sousa

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Tags: Algorithm optimization, Algorithms, CUDA, Image processing, nVidia, Optimization

December 24, 2011 by hgpu

Automatic transformation and optimization of applications on GPUs and GPU clusters

Wenjing Ma

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Tags: Algorithm optimization, Algorithms, Benchmarking, Code generation, Computer science, CUDA, Data mining, GPU cluster, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce 9800 GX2, Optimization, Tesla T10, Thesis

November 9, 2011 by hgpu

A control-structure splitting optimization for GPGPU

Snaider Carrillo, Jakob Siegel, Xiaoming Li

Tags: Algorithm optimization, Computer science, CUDA, nVidia, Programming techniques

November 1, 2010 by hgpu

Performance study of interference on GPU and CPU resources with multiple applications

Shinichi Yamagiwa, Koichi Wada

Tags: Algorithm optimization, Computer science, Performance

October 29, 2010 by hgpu

Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

Victor W. Lee,Changkyu Kim,Jatin Chhugani,Michael Deisher,Daehyun Kim,Anthony D. Nguyen,Nadathur Satish,Mikhail Smelyanskiy,Srinivas Chennupaty,Per Hammarlund,Ronak Singhal,Pradeep Dubey

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Tags: Algorithm optimization, Computer science, nVidia, nVidia GeForce GTX 280, Performance

October 27, 2010 by hgpu

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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