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CPU and/or GPU: Revisiting the GPU Vs. CPU Myth

Kishore Kothapalli, Dip Sankar Banerjee, P. J. Narayanan, Surinder Sood, Aman Kumar Bahl, Shashank Sharma, Shrenik Lad, Krishna Kumar Singh, Kiran Matam, Sivaramakrishna Bharadwaj, Rohit Nigam, Parikshit Sakurikar, Aditya Deshpande, Ishan Misra, Siddharth Choudhary, Shubham Gupta

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Tags: Computer science, Databases, Hybrid computing, Image processing, nVidia, nVidia GeForce GT 520, Sparse matrix, Tesla T10

March 14, 2013 by hgpu

GPU implementation of a deep learning network for image recognition tasks

Sean Patrick Parker

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Tags: Computer science, Computer vision, CUDA, Image recognition, Machine learning, nVidia, Thesis

March 12, 2013 by hgpu

Parallelization Research of Circle Detection Based on Hough Transform

Suping Wu, Xiangjiao Liu

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 260

March 12, 2013 by hgpu

Just-in-time Acceleration of JavaScript

Uday Pitambare, Arun Chauhan, Saurabh Malviya

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Tags: Computer science, Java, nVidia, Performance, PTX, Tesla C1060

March 12, 2013 by hgpu

Comprehensive Analysis of High-Performance Computing Methods for Filtered Back-Projection

Christian B. Mendl, Steven Eliuk, Michelle Noga, Pierre Boulanger

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Tags: Computed tomography, CT, CUDA, DirectX, Filtering, HLSL, Medicine, nVidia, nVidia Quadro FX 4000, Tomography

March 12, 2013 by hgpu

Parallel spatial data structures for interactive rendering

Ismael Garcia Fernandez

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Tags: 3D Graphics and Realism, Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX 480, Rendering, Thesis

March 12, 2013 by hgpu

A Scalable Heterogeneous Parallelization Framework for Iterative Local Searches

Martin Burtscher, Hassan Rabeti

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Tags: Computer science, CUDA, Heterogeneous systems, MPI, nVidia, Search, Tesla M2090

March 12, 2013 by hgpu

3D Modeling, Distance and Gradient Computation for Motion Planning: A Direct GPGPU Approach

Rene Wagner, Udo Frese, Berthold Bauml

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 680

March 12, 2013 by hgpu

Automatic efficient data layout for multithreaded stencil codes on CPUs and GPUs

Julien Jaeger, Denis Barthou

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia Quadro FX 5800

March 12, 2013 by hgpu

Morph Algorithms on GPUs

Rupesh Nasre, Martin Burtscher, Keshav Pingali

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Tags: Algorithms, Computer science, CUDA, Graph theory, nVidia, Performance, Programming techniques, Tesla C2070

March 12, 2013 by hgpu

Speeding Up Model Building for ECGA on CUDA Platform

Chung-Yu Shao, Tian-Li Yu

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Tags: Algorithms, Computer science, CUDA, Evolutionary Computations, Neural and Evolutionary Computing, nVidia, Tesla C2050

March 9, 2013 by hgpu

Signal Processing and General Purpose Computing on GPU

Muge Guher

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Tags: CUDA, nVidia, Signal processing, Tesla C2050

March 9, 2013 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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