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hgpu.org » Analytical model

An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness

Sunpyo Hong, Hyesoon Kim

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Tags: Analytical model, Computer science, CUDA, Memory level parallelism, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 8800 GTX, nVidia Quadro FX 5600, Warp level parallelism

October 29, 2010 by hgpu

An adaptive performance modeling tool for GPU architectures

Sara S. Baghsorkhi, Matthieu Delahaye, Sanjay J. Patel, William D. Gropp, Wen mei

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Tags: Analytical model, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Performance

October 28, 2010 by hgpu

An integrated GPU power and performance model

Sunpyo Hong, Hyesoon Kim

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Tags: Analytical model, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 280, Performance

October 28, 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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