Automatically Classifying Benign and Harmful Data

Races Using Replay AnalysisS. Narayanasamy, Z. Wang, J. Tigani, A. Edwards, B. Calder

UCSD and MicrosoftPLDI 2007

Data Races hard to debug◦ Difficult to detect◦ Even more difficult to reproduce

Data Race Detectors help in detection◦ LockSet, Happens-Before and Atomicity Violation

But they tend to overdo it◦ Up to 90% false alarms

Especially with LockSet

We need a tool that detects and reliably classifies

all harmful Data Races

Motivation

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Offline Dynamic Happens-BeforeData Race Detection

◦ Step 1: Trace Capturing◦ Step 2: Offline Happens-Before Analysis◦ Step 3: Replay Critical Segments◦ Step 4: Auto Classify harmful vs. benign races

Algorithm Overview

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iDNA captures the execution of an application

Simply records initial state,◦ Registers and PC

load values,◦ Only those needed absolutely◦ 1st load after a store, DMA etc…

and a global clock (sequensers)◦ Inserted in the thread’s replay log for

Synchronization events System calls

1. Trace Capturing & Replaying

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2. Offline Happens-Before Analysis Good old Happens-

Before◦ Two conflicting

accesses At least one write Not ordered

Detects only the data races that happened

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When a data race is detected replay the affected segments twice◦ 1st with the actual order

Given by the load values◦ 2nd reverse the racing accesses

Store the replay result◦ No-State-Change: If all live-outs are the same◦ State-Change: If at least 1 live-out changed◦ Replay Failure: If disaster encountered

Load null or unencountered address Branch someplace else

3. Replay Critical Segments

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3. Replay Critical Segments cont.

Replay Failure Potentially Harmful Data Race

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Repeat step 3 for each instance of a data race

Potentially Benign Data Race◦ every replay results to No-State-Change

Potentially Harmful Data Race◦ ≥1 replay results in State-Change or Replay Failure

State-Change shows that something would be different if things took the other path

Replay Failure indicates that a program changed that much, so we cannot simulate the other state

◦ Concrete proof that something definitely changed Easier for the programmers to accept it

4. Automatic Classification

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18 different executions of various services in Windows Vista and Internet Explorer

Happens-Before returns 16,642 data races◦ 68 unique

Trace capture◦ 0.8 bits per instruction

96 MB per 1,000,000,000 instructions Only 1st loads and synchronizers captured

◦ 0.3 if compressed with zip

Evaluation

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Results for Internet Explorer◦ P4 Xeon 2.2 GHz, 1 GB of RAM

Start adding…◦ 6x for capturing◦ 10x for replaying (unnecessary)◦ 45x offline Happens-Before Data Race Detection◦ 280x replay analysis

2,196 dynamic data races

Slowdowns

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Potentially Benign

Potentially Harmful To-

talReal Benign Real Harmful Real Benign Real

HarmfulNo-

State-Change

32 0 32

State Change 15 2 17

Replay Failure 14 5 19

Total 32 0 29 7 68

Data Race Classifications

Impossible State

Impossible State

Automatically Classified

Manually Classified

All harmful races identified correctly0 false negatives

Half benign races identified correctly.

Half still persist

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32 Real Benign races classified as such◦ Every instance

must return No-State-Changed

◦ The more instances, the more confidence in the classification

True Negatives

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7 Real bugs, correctly identified

At least 1 State-Change or Replay Failure required

True PositivesDangerous Zone

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29 Benign races incorrectly classified as harmful ◦ Approximate

Computation (23/29) Statistics etc

◦ Replayer Limitation (6/29) At least 1 instance

caused replay failure The final outcome is the

same

False Positives

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User Constructed◦ Garbage collector does not

use locks Double Checks

◦ If (a) {lock(…); if(a) {…}} Both Values Valid

◦ Use cache? High Perf? Redundant Writes

◦ Rewrite the same value Disjoint bit manipulation

◦ Modify different bits in same variable

# Race

sUser Constructed Synchronization 8Double Checks 3Both Values Valid 5Redundant Writes 13Disjoint bit manipulation 9Approx. Computation 23

False Positives (cont.)

23 false positives that were not caused by replay failure

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Interesting approach to identify benign races

It would be interesting to apply it to LockSet◦ LockSet has far more false positives◦ But it can detect bugs that did not happen in

production runs

A grand total overhead is missing

Conclusion

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Questions?

Thank You!!!