With software complexity growing at a very fast pace comes software reliability issues. Today software failures pose a significant cost to our economy — NIST estimates around $60B per year. Pervasive parallelism is likely to further increase the chances of software failures. While one could argue that the best way to address these challenges is to improve software engineering, it is likely that the problem will haunt us for a while due to legacy systems and the time required to fundamentally change the way software is developed. For that reason, we believe that a way of alleviating the problem is to design architectures and systems that significantly decrease the probability that software bugs manifest themselves and lead to a failure.
We are developing systems that can detect and survive a broad class of concurrency bugs. Concurrency bugs manifest nondeterministically, i.e., they depend upon the occurrence of certain bad interleavings. Interestingly, a given memory semantics exposed to the software can yield multiple valid global interleavings of memory operations. One can allow only a subset of these interleavings to avoid concurrency bugs while still exposing the same memory semantics to the software. We leverage this property and avoid concurrency bugs by monitoring when one is likely to happen and choosing a less dangerous interleaving. Since we don’t want bugs to go simply unnoticed, we are also developing mechanism to reports bugs back to the programmer.
Our first step towards that goal was Atom-Aid, which is a multiprocessor architecture that can both detect and avoid atomicity violations. We are now expanding the class of bugs we can detect and avoid and are developing new machine learning-based techniques.
More information on our concurrency error detection research can be found here.
More information on our research on avoiding failures in concurrent programs can be found here.
Current contacts: Brandon Lucia and Luis Ceze.
Software Tools
We recently released a source version of a tool we developed called Recon. Recon is useful for finding concurrent programming mistakes, and using reconstructed execution fragments to explain their causes to programmers.
Publications
Cooperative Empirical Failure Avoidance for Multithreaded Programs, (ASPLOS 2013), Brandon Lucia and Luis Ceze
IFRit: Interference-Free Regions for Dynamic Data-Race Detection, (OOPSLA 2012), Laura Effinger-Dean, Brandon Lucia, Luis Ceze, Dan Grossman, Hans-J. Boehm
Isolating and Understanding Concurrency Errors with Reconsructed Execution Fragments, (PLDI 2011), Brandon Lucia, Benjamin P. Wood, and Luis Ceze.
ColorSafe: Architectural Support for Debugging and Dynamically Avoiding Multi-variable Atomicity Violations (ISCA 2010), Brandon Lucia, Luis Ceze and Karin Strauss.
Finding Concurrency Bugs with Context-Aware Communication Graphs, (MICRO 2009), Brandon Lucia, Luis Ceze.
Atom-Aid: Detecting and Surviving Atomicity Violations (ISCA 2008), Brandon Lucia, Joseph Devietti, Karin Strauss and Luis Ceze. Selected to appear in IEEE Micro Top Picks 2009.