Comparison of GPU and CPU efficiency while solving heat conduction problems
Abstract
Overview of GPU usage while solving different engineering problems, comparison between CPU and GPU computations and overview of the heat conduction problem are provided in this paper. The Jacobi iterative algorithm was implemented by using Python, TensorFlow GPU library and NVIDIA CUDA technology. Numerical experiments were conducted with 6 CPUs and 4 GPUs. The fastest used GPU completed the calculations 19 times faster than the slowest CPU. On average, GPU was from 9 to 11 times faster than CPU. Significant relative speed-up in GPU calculations starts when the matrix contains at least 4002 floating-point numbers.
Article in English.
GPU ir CPU efektyvumo palyginimas sprendžiant šilumos laidumo uždavinius
Santrauka
Šiame straipsnyje apžvelgtas GPU taikymas įvairiems inžineriniams uždaviniams spręsti, palyginti skaičiavimai naudojant CPU ir GPU, aprašytas šilumos laidumo uždavinys. Įgyvendintas Jakobio metodas naudojant „Python“, „TensorFlow GPU“ biblioteką ir NVIDIA CUDA technologijas. Atlikti skaitiniai eksperimentai naudojant šešis CPU ir keturis GPU įtaisus. Greičiausias nagrinėtas GPU įvykdė skaičiavimus 19 kartų greičiau negu lėčiausias CPU. Naudojant GPU, vidutiniškai skaičiavimai buvo atliekami nuo 9 iki 11 kartų greičiau nei su CPU. Didelis santykinis GPU pagreitėjimas vyko, kai lygiagrečiai buvo apdorojama bent 4002 realiųjų skaičių.
Reikšminiai žodžiai: CUDA, GPU, Jakobio metodas, lygiagretieji skaičiavimai, šilumos laidumo uždavinys.
Keywords:
CUDA, GPU, Jacobi iterative algorithm, parallel computing, heat conduction problemHow to Cite
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Copyright (c) 2020 The Author(s). Published by Vilnius Gediminas Technical University.
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This work is licensed under a Creative Commons Attribution 4.0 International License.