Data races indicate serious concurrency bugs such as order, atomicity, and sequential consistency violations. Races are difficult to find and fix, often manifesting only in deploy...
Michael D. Bond, Katherine E. Coons, Kathryn S. Mc...
Many applications need to respond to incremental modifications to data. Being incremental, such modification often require incremental modifications to the output, making it po...
—Current trends in desktop processor design have been toward many-core solutions with increased parallelism. As the number of supported threads grows in these processors, it may ...
To make efficient use of CMPs with tens to hundreds of cores, it is often necessary to exploit fine-grain parallelism. However, managing tasks of a few thousand instructions is ...
Daniel Sanchez, Richard M. Yoo, Christos Kozyrakis
The Local Outlier Factor (LOF) is a very powerful anomaly detection method available in machine learning and classification. The algorithm defines the notion of local outlier in...