scholarly journals Evolution of the Linux Kernel Variability Model

2010 ◽  
pp. 136-150 ◽  
Author(s):  
Rafael Lotufo ◽  
Steven She ◽  
Thorsten Berger ◽  
Krzysztof Czarnecki ◽  
Andrzej Wąsowski
Keyword(s):  
Linux Kernel ◽  
Variability Model

Machine learning for load balancing in the Linux kernel

2020 ◽  
Author(s):  
Jingde Chen ◽  
Subho S. Banerjee ◽  
Zbigniew T. Kalbarczyk ◽  
Ravishankar K. Iyer
Keyword(s):  
Machine Learning ◽  
Load Balancing ◽  
Linux Kernel

scholarly journals A study of persistent memory bugs in the Linux kernel

2021 ◽  
Author(s):  
Duo Zhang ◽  
Om Rameshwar Gatla ◽  
Wei Xu ◽  
Mai Zheng
Keyword(s):  
Linux Kernel ◽  
Persistent Memory

RCU Usage In the Linux Kernel

2020 ◽  
Vol 54 (1) ◽  
pp. 47-63
Author(s):  
Paul E. McKenney ◽  
Joel Fernandes ◽  
Silas Boyd-Wickizer ◽  
Jonathan Walpole
Keyword(s):  
Linux Kernel

scholarly journals Thread-Aware Mechanism to Enhance Inter-Node Load Balancing for Multithreaded Applications on NUMA Systems

2021 ◽  
Vol 11 (14) ◽  
pp. 6486
Author(s):  
Mei-Ling Chiang ◽  
Wei-Lun Su
Keyword(s):  
Load Balancing ◽  
Critical Path ◽  
Selection Procedure ◽  
Remote Memory ◽  
Data Mapping ◽  
Linux Kernel ◽  
Access Memory ◽  
Selection Policy ◽  
Benchmark Suite ◽  
Thread Mapping

NUMA multi-core systems divide system resources into several nodes. When an imbalance in the load between cores occurs, the kernel scheduler’s load balancing mechanism then migrates threads between cores or across NUMA nodes. Remote memory access is required for a thread to access memory on the previous node, which degrades performance. Threads to be migrated must be selected effectively and efficiently since the related operations run in the critical path of the kernel scheduler. This study focuses on improving inter-node load balancing for multithreaded applications. We propose a thread-aware selection policy that considers the distribution of threads on nodes for each thread group while migrating one thread for inter-node load balancing. The thread is selected for which its thread group has the least exclusive thread distribution, and thread members are distributed more evenly on nodes. This has less influence on data mapping and thread mapping for the thread group. We further devise several enhancements to eliminate superfluous evaluations for multithreaded processes, so the selection procedure is more efficient. The experimental results for the commonly used PARSEC 3.0 benchmark suite show that the modified Linux kernel with the proposed selection policy increases performance by 10.7% compared with the unmodified Linux kernel.

scholarly journals Effects of BMI and LDL-cholesterol change pattern on cardiovascular disease in normal adults and diabetics

2020 ◽  
Vol 8 (2) ◽  
pp. e001340
Author(s):  
Tae Mi Youk ◽  
Min Jin Kang ◽  
Sun Ok Song ◽  
Eun-Cheol Park
Keyword(s):  
Cardiovascular Disease ◽  
Ldl Cholesterol ◽  
Density Lipoprotein ◽  
Diabetic Control ◽  
Individual Variability ◽  
Control Group ◽  
Simple Method ◽  
Change Pattern ◽  
Patients With Diabetes ◽  
Variability Model

IntroductionTo examine how the risk of cardiovascular disease changes according to degree of change in body mass index (BMI) and low-density lipoprotein (LDL)-cholesterol in patients with diabetes using the health medical examination cohort database of the National Health Insurance Service in Korea. In comparison, the pattern in a non-diabetic control group was also examined.Research design and methodsThe study samples were 13 800 patients with type 2 diabetes and 185 898 non-diabetic controls, and their baseline characteristics and repeatedly measured BMI and LDL-cholesterol until occurrence of cardiovascular disease were collected in longitudinal data. We used the variability model that is joint of mixed effects and regression model, then estimated parameters about variability by Bayesian methods.ResultsThe risk of cardiovascular disease was increased significantly with high average real variability (ARV) of BMI in the patients with diabetes, but the risk of cardiovascular disease was not increased according to degree of ARV in non-diabetic controls. The Bayesian variability model was used to analyze the effects of BMI and LDL-cholesterol change pattern on development of cardiovascular disease in diabetics, showing that variability did not have a statistically significant effect on cardiovascular disease. This shows the danger of the former simple method when interpreting only the mean of the absolute value of the variation.ConclusionsThe approach of simple SD in previous studies for estimation of individual variability does not consider the order of observation. However, the Bayesian method used in this study allows for flexible modeling by superimposing volatility assessments on multistage models.

Mini-ATX Computer System in Vehicle

2015 ◽  
Vol 1115 ◽  
pp. 484-487 ◽  
Author(s):  
Muhammad Sami ◽  
Akram M. Zeki
Keyword(s):  
Operating System ◽  
Computer System ◽  
Linux Kernel

The aim of this study is to create and assemble the system with customizing/building Linux kernel and environments to be compatible and efficient on mini-ITX computer. The objective of the study is to create/customizing lightweight operating system using GNU/Linux to be used on computer to be used on vehicle. The system would also optimize the size and functionalities most probably would be implemented on car computer system.Keywords: mini-ATX, CarPC, Linux, Ubuntu, Qt, QML

Compartmental modeling of the hepatic transport of taurocholate in the rat in vivo

1989 ◽  
Vol 257 (2) ◽  
pp. G210-G220 ◽  
Author(s):  
X. Deroubaix ◽  
T. Coche ◽  
E. Depiereux ◽  
E. Feytmans
Keyword(s):  
Excretion Rate ◽  
Compartmental Analysis ◽  
Hepatic Transport ◽  
Lumped Model ◽  
Perfusion Model ◽  
Direct Measurements ◽  
Distribution In Blood ◽  
Variability Model ◽  
Good Agreement

Compartmental analysis was used to study the hepatobiliary transport of taurocholate (TC) in the rat in vivo. The available data are the following: [14C]TC kinetics in blood and bile, weighting factors associated with these data and computed from a theoretical variability model, and TC excretion rate in bile. The lumped model that best fits the data contains five compartments: three compartments for TC distribution in blood and two compartments for the liver. It includes a compartmental representation of the laminar flow of bile in the collecting catheter. This model overestimates TC concentration in blood. A perfusion model that includes a compartment representing explicitly the sinusoidal TC concentration gradient was developed. TC concentration in blood estimated by this model is in good agreement with direct measurements, showing that the perfused model has a better descriptive capacity than the lumped model. The amounts of TC estimated in the two hepatic compartments are similar to values previously published.

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