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A Survey Of Techniques for Managing and Leveraging Caches in GPUs

Sparsh Mittal

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Tags: GPGPU, GPGPU architecture, GPU, Hardware, Hardware Architecture

November 18, 2014 by sparsh0mittal

A Performance Analysis Framework for Identifying Potential Benefits in GPGPU Applications

Jaewoong Sim, Aniruddha Dasgupta, Hyesoon Kim, and Richard Vuduc

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Tags: Analytical model, CUDA, GPGPU architecture, nVidia, Performance benefit prediction, Performance prediction, Tesla C2050

March 30, 2012 by Moaddeli

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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