Writing efficient parallel programs is a difficult and error-prone process. The Standard Template Adaptive Parallel Library (STAPL) is being developed to make this task easier for programmers with little experience in parallel programming. STAPL is a C++ library for writing parallel programs using a generic programming approach similar to writing sequential programs using the C++ Standard Template Library (STL). STAPL provides a collection of parallel containers (pContainers) to store data in a distributed fashion and a collection of pViews to abstract details of the data distribution. STAPL algorithms are written in terms of PARAGRAPHs which are high level descriptions of task dependence graphs.
Scheduling plays a very important role in the efficient execution of parallel programs. In this thesis, we present our work to enable efficient scheduling of parallel programs written using STAPL. We abstract the scheduling activities associated with PARAGRAPHs in a software module called the scheduler which is customizable and extensible. We provide support for static scheduling of PARAGRAPHs and develop mechanisms based on migration of tasks and data to support dynamic scheduling strategies for PARAGRAPHs with arbitrary dependencies. We also provide implementations of different scheduling strategies that can be used to improve the performance of applications suffering from load imbalance.
The scheduling infrastructure developed in this thesis is highly customizable and can be used to execute a variety of parallel computations. We demonstrate its usefulness by improving the performance of two applications: a widely used synthetic benchmark (UTS) and a Parallel Motion Planning application. The experiments are conducted on an Opteron cluster and a massively parallel Cray XE6 machine. Experimental results up to 6144 processors are presented.
- Amato, Nancy Professor - Term Appointment