Talk:COMP 3000 Essay 2 2010 Question 6
Actual group members
- Nicholas Shires nshires@connect.carleton.ca
- Andrew Zemancik andy.zemancik@gmail.com
- Austin Bondio -> abondio2@connect.carleton.ca
- David Krutsko :: dkrutsko at connect.carleton.ca
If everyone could just post there names and contact information.--Azemanci 02:57, 15 November 2010 (UTC)
Who's Doing What
Research Problem
I'll do 'Research Problem' and help out with the 'Critique' section, the professor said that part was pretty big Nshires 20:45, 21 November 2010 (UTC)
Contribution
What are the research contribution(s) of this work? Specifically, what are the key research results, and what do they mean? (What was implemented? Why is it any better than what came before?)
Ill do Contribution: Achamney 03:50, 22 November 2010 (UTC)
I've noticed a couple things for controversy, even though its not my topic
The biggest thing i saw was that dataCollider reports non-erroneous operations 90% of the time. This makes the user have to sift through all of the reports to separate the problems from the benign races. Achamney 17:18, 22 November 2010 (UTC)
Proving that DataCollider is better:
The key part of the contribution of this essay is its competition. The research team for DataCollider looked at several other implementations of race condition testers to find ways of improving their own program, or to look for different ways of solving the same problem.
Some of the programs that were referenced were:
Eraser: A Dynamic Data Race Detector for Multithreaded Programs
RaceTrack: Efficient Detection of Data Race Conditions via Adaptive Tracking
PACER: Proportional Detection of Data Races
LiteRace: Effective Sampling for Lightweight Data-Race Detection
FastTrack: Efficient and Precise Dynamic Race Detection
MultiRace: Efficient on-the-fly data race detection in multithreaded C++ programs
RacerX: Effective, Static Detection of Race Conditions and Deadlocks
Eraser: A Dynamic Data Race Detector for Multithreaded Programs
lock-set based reasoning
PACER: Proportional Detection of Data Races
happens-before reasoning
LiteRace: Effective Sampling for Lightweight Data-Race Detection
happens-before reasoning
FastTrack: Efficient and Precise Dynamic Race Detection
happens-before reasoning
RaceTrack: Efficient Detection of Data Race Conditions via Adaptive Trackins
combo of lock-set and happens-before reasoning
MultiRace: Efficient on-the-fly data race detection in multithreaded C++ programs
combo of lock-set and happens-before reasoning
Background Concepts
Hey guys, sorry I'm late to the party. I'll get started with Background Concepts. - Austin Bondio 15:33, 23 November 2010 (UTC)
Critique
I'll work on the critique which will probably need more then one person and I'll also fill out the paper information section.--Azemanci 18:42, 23 November 2010 (UTC)