The Standard Template Adaptive Parallel Library (STAPL) is a C++ parallel programming library that provides a collection of distributed data structures (pContainers) and parallel algorithms (pAlgorithms) and a generic methodology for extending them to provide customized functionality. STAPL algorithms are written in terms of views, which provide a generic access interface to pContainer data by abstracting common data structure concepts. Briefly, views allow the same pContainer to present multiple interfaces, e.g., enabling the same pMatrix to be ‘viewed ’ (or used) as a row-major or column-major matrix, or even as a vector. In this paper, we describe the stapl View concept and its properties. stapl Views generalize the iterator concept — a View corresponds to a collection of elements and provides an ADT for the data it represents. stapl Views enable parallelism by providing random access to the elements, and support for managing the tradeoff between the expressivity of the views and the performance of the parallel execution. Views trade additional parallelism enabling information for reduced genericity. We illustrate the expressivity enabled by Views for several examples and examine the performance overhead incurred when using Views. 1

The Standard Template Adaptive Parallel Library (STAPL) is a parallel programming infrastructure that extends C++ with support for parallelism. It includes a collection of distributed data structures called pContainers that are thread-safe, concurrent objects, i.e., shared objects that provide parallel methods that can be invoked concurrently. In this work, we present the STAPL Parallel Container Framework (PCF), that is designed to facilitate the development of generic parallel containers. We introduce a set of concepts and a methodology for assembling a pContainer from existing sequential or parallel containers, without requiring the programmer to deal with concurrency or data distribution issues. The PCF provides a large number of basic parallel data structures (e.g., pArray, pList, pVector, pMatrix, pGraph, pMap, pSet). The PCF provides a class hierarchy and a composition mechanism that allows users to extend and customize the current container base for improved application expressivity and performance. We evaluate STAPL pContainer performance on a CRAY XT4 massively parallel system and show that pContainer methods, generic pAlgorithms, and different applications provide good scalability on more than 16,000 processors. D.1.3 [Concurrent Pro-