Eliminating Data Race Warnings Using CSP

2016 ◽  
pp. 30-43
Author(s):  
Martin Wittiger
Keyword(s):  
Data Race

A Study on Race Condition and Dynamic Data Race Detection Techniques

2019 ◽  
Vol 7 (6) ◽  
pp. 41-46
Author(s):  
Mithilesh Kumar Dubey ◽  
Devesh Lowe ◽  
Bhavna Galhotra
Keyword(s):  
Data Race ◽  
Dynamic Data ◽  
Data Race Detection ◽  
Detection Techniques ◽  
Race Detection ◽  
Race Condition

Symbolic data race detection for Habanero programs

2019 ◽  
Vol 44 (4) ◽  
pp. 18-18 ◽  
Author(s):  
Egor Namakonov ◽  
Eric Mercer ◽  
Pavel Parizek ◽  
Kyle Storey
Keyword(s):  
Data Race ◽  
Data Race Detection ◽  
Race Detection ◽  
Symbolic Data

scholarly journals Fence Placement for Legacy Data-Race-Free Programs via Synchronization Read Detection

2016 ◽  
Vol 12 (4) ◽  
pp. 1-23
Author(s):  
Andrew J. Mcpherson ◽  
Vijay Nagarajan ◽  
Susmit Sarkar ◽  
Marcelo Cintra
Keyword(s):  
Data Race ◽  
Legacy Data

scholarly journals Correct program parallelisations

2021 ◽  
Author(s):  
S. Blom ◽  
S. Darabi ◽  
M. Huisman ◽  
M. Safari
Keyword(s):  
Composition Operators ◽  
Parallel Programs ◽  
Parallel Program ◽  
Tool Support ◽  
Intermediate Representation ◽  
Data Race ◽  
Sequential Program ◽  
Functional Correctness ◽  
Correct Program ◽  
Basic Blocks

AbstractA commonly used approach to develop deterministic parallel programs is to augment a sequential program with compiler directives that indicate which program blocks may potentially be executed in parallel. This paper develops a verification technique to reason about such compiler directives, in particular to show that they do not change the behaviour of the program. Moreover, the verification technique is tool-supported and can be combined with proving functional correctness of the program. To develop our verification technique, we propose a simple intermediate representation (syntax and semantics) that captures the main forms of deterministic parallel programs. This language distinguishes three kinds of basic blocks: parallel, vectorised and sequential blocks, which can be composed using three different composition operators: sequential, parallel and fusion composition. We show how a widely used subset of OpenMP can be encoded into this intermediate representation. Our verification technique builds on the notion of iteration contract to specify the behaviour of basic blocks; we show that if iteration contracts are manually specified for single blocks, then that is sufficient to automatically reason about data race freedom of the composed program. Moreover, we also show that it is sufficient to establish functional correctness on a linearised version of the original program to conclude functional correctness of the parallel program. Finally, we exemplify our approach on an example OpenMP program, and we discuss how tool support is provided.

172 Sleep Disparities in Preschool-Aged Children

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A70-A70
Author(s):  
Jennifer Holmes ◽  
Olivia Hanron ◽  
Rebecca Spencer
Keyword(s):  
Sleep Duration ◽  
Ethnic Disparities ◽  
Data Race ◽  
Older Children ◽  
Hispanic Children ◽  
Sleep Habits ◽  
Chi Square ◽  
White Children ◽  
Race Ethnicity ◽  
And Gender

Abstract Introduction Sleep is known to be associated with socioeconomic status (SES) in older children and adults with those from lower SES households often experiencing poorer sleep quality. Whether this disparity exists in early childhood is relatively unknown, despite being an important age marked by sleep transitions and the establishment of lifelong sleep habits. Furthermore, it is a critical period for cognitive development and learning, which are supported by sleep. Here, we explore associations between sleep and SES in a preschool population. We hypothesized that children from lower SES households would exhibit shorter overnight sleep, longer and more frequent naps, and shorter 24-hr sleep. Additionally, we considered racial and ethnic disparities in sleep which can be confounded with SES in some samples. Methods Child (n=441; M age=51.9mo; 45.4% female) sleep was measured objectively using actigraph watches, worn for 3-16 days (M=9.5 days). Caregivers reported child demographics and household data. Race/ethnicity of our sample was 72% White, 10.2% Black, 17.8% other or more than one race, and 28.4% identified as Hispanic. 20.1% of our sample was categorized as low SES. Effects of SES and race/ethnicity on continuous sleep measures were assessed using multiple regression models, with age and gender as covariates. Nap habituality was assessed using chi-square tests. Results Lower SES was associated with shorter nighttime sleep duration, longer nap duration, and shorter 24-hr sleep duration (p’s<.001). Children from lower SES households were also more likely to nap habitually (p=.04) as were Hispanic children (p<.001). Hispanic children also tended to have longer nap bouts (p=.002). Hispanic and Black children on average had shorter overnight sleep durations than White children (p’s<.04), but their 24-hr sleep did not differ. Conclusion SES-related sleep disparities were present in this preschool population, with lower SES children exhibiting poorer sleep. When controlling for SES, Hispanic children tended to sleep less overnight which was compensated for by longer, more frequent naps. This underscores the necessity of naps for some children to achieve adequate sleep. Future directions will explore the relationship between parenting factors and sleep, such as bedtime routines and parent knowledge surrounding child sleep needs. Support (if any) NIH R01 HL111695

Automated data race bugs addition

2020 ◽  
Author(s):  
Hongliang Liang ◽  
Mingyue Li ◽  
Jianli Wang
Keyword(s):  
Data Race

A time-aware type system for data-race protection and guaranteed initialization

2010 ◽  
Vol 45 (10) ◽  
pp. 634-651 ◽  
Author(s):  
Nicholas D. Matsakis ◽  
Thomas R. Gross
Keyword(s):  
Type System ◽  
Data Race ◽  
Time Aware
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