test_moissonnage_selune
type: Agent

Throughput-oriented analytical models for performance estimation on programmable hardware accelerators
type: Distribution
access URL: https://theses.hal.science/tel-00854019
format: HTML
issued: 2026-05-10T00:43:12.136979
modified: 2026-05-10T00:43:12.121718

Throughput-oriented analytical models for performance estimation on programmable hardware accelerators
type: Dataset
contact point: Organization
  fn: CCSD
keyword: performance-analysis
keyword: fermi-gpu
keyword: cuda
keyword: theses
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: gpgpu
keyword: infoeu-reposemanticsdoctoralthesis
keyword: multi-coeurs
keyword: processeurs-graphiques
keyword: performance-prediction
keyword: kepler-gpu
keyword: performance-upper-bound
landing page: https://theses.hal.science/tel-00854019
description: In this thesis work, we have mainly worked on two topics of GPU performance analysis. First, we have developed an analytical method and a timing estimation tool (TEG) to predict CUDA application's performance for GT200 generation GPUs. TEG can predict GPU applications' performance in cycle-approximate level. Second, we have developed an approach to estimate GPU applications' performance upper bound based on application analysis and assembly code level benchmarking. With the performance upper bound of an application, we know how much optimization space is left and can decide the optimization effort. Also with the analysis we can understand which parameters are critical to the performance.
identifier: NNT: 2013REN1S014
issued: 2026-05-10T00:43:12.134143
language: en
modified: 2026-05-10T00:43:12.134147