The Standard Template Adaptive Parallel Library (STAPL) is a collection of generic data structures and algorithms that provides a high productivity, parallel programming infrastructure with an approach that draws heavily in design from the C++ Standard Template Library (STL). By abstracting much of the complexities of parallelism from the end user, STAPL provides a platform for high productivity by enabling the user to focus on algorithmic design instead of lower level parallel implementation issues. At the same time the scalable performance of the STAPL library has to be portable across parallel systems and application domains. To ensure this performance portability, STAPL incorporates adaptive mechanisms that automatically tunes its behavior. In this talk, we provide an overview of the major STAPL components and discuss its programming model. We conclude with some performance data.

Lawrence Rauchwerger is a Professor Computer Science at Texas A&M University and the co-Director of the Parasol Lab. He received an Engineer degree from the Polytechnic Institute Bucharest, a M.S. in Electrical Engineering from Stanford University and a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. He has held Visiting Faculty positions at the University of Illinois, Bell Labs, IBM T.J. Watson, and INRIA, Paris. Rauchwerger's approach to auto-parallelization, thread-level speculation and parallel code development has influenced industrial products at corporations like IBM, Intel and Sun. Rauchwerger is an IEEE Fellow, an NSF CAREER award recipient and has chaired various IEEE and ACM conferences.

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