SHF: Small: Adapting Dataflow Analysis for Efficient and Precise Parallel Program Monitoring

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Due to the industry-wide shift to multi-core processing, futureapplications will only run faster if they are written as parallelsoftware (i.e. where the work for a single program is accomplished bysimultaneously performing different parts of that work on differentprocessors). Unfortunately history has taught us that writingparallel software is a very error-prone task for programmers. Toaddress this challenge, this research project is creating a powerfulnew mechanism for identifying and hopefully fixing bugs in parallelsoftware on large-scale production systems. Hopefully the resultingframework will not only support the parallel execution of existingmonitoring tools, but it will also spawn the creation of new classesof powerful tools that can recognize subtle bugs in parallel software.Such tools can help programmers continue to reap the performancebenefits of future microprocessors, thereby continuing to enable allof the benefits that increasingly-faster computation has had on theeconomy, science and technology, and our everyday lives in anInternet-connected world.More specifically, the focus of this project is on sophisticatedonline program monitoring tools that model various aspects of programcorrectness at an instruction-by-instruction granularity. To strike apractical balance between performance, precision, and convenience forthe tool developer, this research has developed a novel framework thatintroduces explicit "windows of uncertainty" combined with a newapproach for avoiding a state space explosion in the analysis(inspired by Tarjan's interval analysis approach to dataflow analysis).