<p>NestStep is a collection of parallel extensions to existing programming languages. These extensions supports a shared memory model and nested parallelism. NestStep is based the Bulk-Synchronous Programming model. Most of the communication of data in NestStep takes place in a</p><p>combine/commit phase, which is essentially a reduction followed by a broadcast.</p><p>The primary aim of the project that this thesis is based on was to develop a runtime system for NestStep-C, the extensions for the C programming language. The secondary aim was to find which tree structure among a selected few is the best for communicating data in the combine/commit phase.</p><p>This thesis includes information about NestStep, how to interface with the NestStep runtime system, some example applications and benchmarks for determining the best tree structure. A binomial tree structure and trees similar to it was empirically found to yield the best performance.</p>

This dissertation maps various kernels and applications to a spectrum of programming models and architectures and also presents architecture-aware algorithms for different systems. The kernels and applications discussed in this dissertation have widely varying computational characteristics. For example, we consider both dense numerical computations and sparse graph algorithms. This dissertation also covers emerging applications from image processing, complex network analysis, and computational biology. We map these problems to diverse multicore processors and manycore accelerators. We also use new programming models (such as Transactional Memory, MapReduce, and Intel TBB) to address the performance and productivity challenges in the problems. Our experiences highlight the importance of mapping applications to appropriate programming models and architectures. We also find several limitations of current system software and architectures and directions to improve those. The discussion focuses on system software and architectural support for nested irregular parallelism, Transactional Memory, and hybrid data transfer mechanisms. We believe that the complexity of parallel programming can be significantly reduced via collaborative efforts among researchers and practitioners from different domains. This dissertation participates in the efforts by providing benchmarks and suggestions to improve system software and architectures.

NestStep is a collection of parallel extensions to existing programming languages. These extensions supports a shared memory model and nested parallelism. NestStep is based the Bulk-Synchronous Programming model. Most of the communication of data in NestStep takes place in a combine/commit phase, which is essentially a reduction followed by a broadcast. The primary aim of the project that this thesis is based on was to develop a runtime system for NestStep-C, the extensions for the C programming language. The secondary aim was to find which tree structure among a selected few is the best for communicating data in the combine/commit phase. This thesis includes information about NestStep, how to interface with the NestStep runtime system, some example applications and benchmarks for determining the best tree structure. A binomial tree structure and trees similar to it was empirically found to yield the best performance.