Mining Maximum Frequent Access Patterns in Web Logs Based on Unique Labeled Tree

2006 ◽  
pp. 73-82
Author(s):  
Ling Zhang ◽  
Jian-ping Yin ◽  
Yu-bin Zhan
Keyword(s):  
Web Logs ◽  
Access Patterns ◽  
Labeled Tree

A Top Down Algorithm for Mining Web Access Patterns from Web Logs

2005 ◽  
pp. 838-843
Author(s):  
Guo Jian-Kui ◽  
Ruan Bei-jun ◽  
Cheng Zun-ping ◽  
Su Fang-zhong ◽  
Wang Ya-qin ◽  
...  
Keyword(s):  
Top Down ◽  
Web Logs ◽  
Web Access ◽  
Access Patterns

Evaluation of access patterns from log Ontology with DL-safe rules

2009 ◽  
Vol 29 (5) ◽  
pp. 1401-1404
Author(s):  
Ming SUN ◽  
Bo CHEN ◽  
Ming-tian ZHOU
Keyword(s):  
Access Patterns

scholarly journals Machine Learning–enabled Scalable Performance Prediction of Scientific Codes

2021 ◽  
Vol 31 (2) ◽  
pp. 1-28
Author(s):  
Gopinath Chennupati ◽  
Nandakishore Santhi ◽  
Phill Romero ◽  
Stephan Eidenbenz
Keyword(s):  
Machine Learning ◽  
Performance Prediction ◽  
Prediction Models ◽  
Radiation Transport ◽  
Discrete Event ◽  
Basic Block ◽  
Distribution Models ◽  
Scientific Application ◽  
High Level ◽  
Access Patterns

Hardware architectures become increasingly complex as the compute capabilities grow to exascale. We present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input and predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, and (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks and present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs. We further extend PPT-AMMP to predict the performance of a scientific application code, namely, the radiation transport mini-app SNAP. To this end, we analyze multi-variate regression models that accurately predict the reuse profiles and the basic block counts. We validate predicted SNAP runtimes against actual measured times.

Sign in / Sign up

Export Citation Format

Share Document