STAPL: An Adaptive, Generic Parallel C++ Library

Abstract

Abstract. The Standard Template Adaptive Parallel Library (STAPL) is a parallel library designed as a superset of the ANSI C++ Standard Template Library (STL). It is sequentially consistent for functions with the same name, and executes on uni-or multi-processor systems that utilize shared or distributed memory. STAPL is implemented using simple parallel extensions of C++ that currently provide a SPMD model of parallelism, and supports nested parallelism. The library is intended to be general purpose, but emphasizes irregular programs to allow the exploitation of parallelism for applications which use dynamically linked data structures such as particle transport calculations, molecular dynamics, geometric modeling, and graph algorithms. STAPL provides several different algorithms for some library routines, and selects among them adaptively at runtime. STAPL can replace STL automatically by invoking a preprocessing translation phase. In the applications studied, the performance of translated code was within 5% of the results obtained using STAPL directly. STAPL also provides functionality to allow the user to further optimize the code and achieve additional performance gains. We present results obtained using STAPL for a molecular dynamics code and a particle transport code. MotivationIn sequential computing, standardized libraries have proven to be valuable tools for simplifying the program development process by providing routines for common operations that allow programmers to concentrate on higher level problems. Similarly, libraries of elementary, generic, parallel algorithms provide important building blocks for parallel applications and specialized libraries [7,6,20]. Due to the added complexity of parallel programming, the potential impact of libraries could be even more profound than for sequential computing. Indeed, we believe parallel libraries are crucial for moving parallel computing into the mainstream since they offer the only viable means for achieving scalable performance across a variety of applications and architectures with programming efforts comparable to those of developing sequential codes. In particular, properly designed parallel libraries could insulate less experienced users

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