"The STAPL Parallel Container Framework,"
Ph.D. Dissertation, Department of Computer Science and Engineering, Texas A&M University, College Station, TX (2010)
The Standard Template Adaptive Parallel Library (stapl) is a
parallel programming infrastructure that extends C++ with support
for parallelism. stapl provides a run-time system, a collection of
distributed data structures (pContainers) and parallel
algorithms (pAlgorithms), and a generic methodology for
extending them to provide customized functionality.
Parallel containers are data structures addressing issues
related to data partitioning, distribution, communication,
synchronization, load balancing, and thread safety. This
dissertation presents the STAPL Parallel Container Framework
(PCF), which 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
stapl PCF provides a large number of basic data parallel structures
(e.g., pArray, pList, pVector, pMatrix, pGraph, pMap, pSet).
The stapl PCF is distinguished from existing work by oﬀering a
class hierarchy and a composition mechanism which allows users
to extend and customize the current container base for
improved application expressivity and performance. We evaluate
the performance of the stapl pContainers on various parallel
machines including a massively parallel CRAY XT4 system and an IBM
P5-575 cluster. We show that the pContainer methods, generic
pAlgorithms, and diﬀerent applications, all provide good
scalability on more than 10 processors.
Associated Project(s):SHIELD (Smuggled HEU Interdiction through Enhanced anaLysis and Detection): A Framework for Developing Novel Detection Systems Focused on Interdicting Shielded HEU