'Race detection' diaporamas de présentation

LineUp : Automatic Thread Safety Checking

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Effective Static Race Detection for Java

Effective Static Race Detection for Java. Mayur, Alex, John @ CS Department Stanford University. Presented by Roy Ganor 14/2/08 Point-To Analysis Seminar Tel Aviv University. A Few Definitions (not!). Motivation ? Concurrent = Hard. sync ( l ) {. sync ( l ) {. t2 = x ; t2 = t2 + 1;

499 views • 35 slides

Dataflow Analysis for Concurrent Programs using Datarace Detection

Dataflow Analysis for Concurrent Programs using Datarace Detection. Ravi Chugh, Jan W. Voung, Ranjit Jhala, Sorin Lerner LBA Reading Group Michelle Goodstein 6/5/08. Outline. Motivation Overview of Radar Radar Formalization Radar Optimizations Radar(Relay) Evaluation & Results

455 views • 33 slides

W4118 Operating Systems

W4118 Operating Systems. Instructor: Junfeng Yang. Last lecture: synchronization in Linux. Low-level atomic operations: Memory barrier avoids compile-time, or runtime instruction re-ordering Atomic operations memory bus lock, read-modify-write ops Interrupt/softirq disabling/enabling

327 views • 32 slides

Compiler-assisted debugging of concurrent programs

Compiler-assisted debugging of concurrent programs. Nick Roberts nroberts. Vijay Ramamurthy vijayram. 15-745 Spring 2019. We can adapt techniques from optimizing compilers to help identify likely bugs. Register allocation, CSE, parallelization. Pointer Analysis.

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Software Model Checking

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Demand-Driven Software Race Detection using Hardware Performance Counters

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SOS: Saving Time in Dynamic Race Detection with Stationary Analysis

SOS: Saving Time in Dynamic Race Detection with Stationary Analysis. Du Li , Witawas Srisa -an, Matthew B. Dwyer. D ata Race. T wo concurrent accesses t o the same data, and at least one access is a write O ne of the most common concurrency bugs. T hread 1. T hread 2. Release L.

398 views • 28 slides

Detecting and surviving data races using complementary schedules

Detecting and surviving data races using complementary schedules. Kaushik Veeraraghavan Peter Chen , Jason Flinn, Satish Narayanasamy University of Michigan. Multicores/multiprocessors are ubiquitous. Most desktops, laptops & cellphones use multiprocessors

355 views • 28 slides

Detecting and surviving data races using complementary schedules

Detecting and surviving data races using complementary schedules. Kaushik Veeraraghavan Peter Chen , Jason Flinn, Satish Narayanasamy University of Michigan. Multicores/multiprocessors are ubiquitous. Most desktops, laptops & cellphones use multiprocessors

348 views • 28 slides

IteRace

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Jipeng Huang , Michael D. Bond Ohio State University

Efficient Context Sensitivity for Dynamic Analyses via Calling Context Uptrees and Customized Memory Management. Jipeng Huang , Michael D. Bond Ohio State University. Example: dynamic race detector. Thread A. Thread B. race!. w rite x. write x. Example: dynamic race detector.

506 views • 24 slides

A Case for Unlimited Watchpoints

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Parallelizing Data Race Detection

Parallelizing Data Race Detection. Benjamin Wester Facebook David Devecsery , Peter Chen, Jason Flinn , Satish Narayanasamy University of Michigan. Data Races. t 1. t 2. TIME. lock( l ) x = 1 unlock( l ) x = 3. lock( l ) x = 2 unlock( l ). Race Detection. Instrumentation +

224 views • 22 slides

Hybrid Dynamic Data Race Detection

Hybrid Dynamic Data Race Detection. Robert O'Callahan and Jong-Deok Choi 2011.11.23 OSLab Meng Ying. Contents. 1. 3. 2. 8. 5. 6. 7. 4. Introduction. Lockset-Based Race Detection. H a ppens-Before Race Detection. Relationship Between Happens-Before

453 views • 20 slides