Graduate Thesis Or Dissertation
 

Towards architecture-adaptable parallel programming

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https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/s1784q07n

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  • There is a software gap in parallel processing. The short lifespan and small installation base of parallel architectures have made it economically infeasible to develop platform-specific parallel programming environments that deliver performance and programmability. One obvious solution is to build architecture-independent programming environments. But the architecture independence usually comes at the expense of performance, since the most efficient parallel algorithm for solving a problem often depends on the target platform. Thus, unless a parallel programming system has the ability to adapt the algorithm to the architecture, it will not be effectively machine-independent. This research develops a new methodology for architecture-adaptable parallel programming. The methodology is built on three key ideas: (1) the use of a database of parameterized algorithmic templates to represent computable functions; (2) frame-based representation of processing environments; and (3) the use of an analytical performance prediction tool for automatic algorithm design. This methodology pursues a problem-oriented approach to parallel processing as opposed to the traditional algorithm-oriented approach. This enables the development of software environments with a high level of abstraction. The users state the problem to be solved using a high-level notation; they are freed from the esoteric tasks of parallel algorithm design and implementation. This methodology has been validated in the format of a prototype of a system capable of automatically generating an efficient parallel program when presented with a well-defined problem and the description of a target platform. The use of object technology has made the system easily extensible. The templates are designed using a parallel adaptation of the well-known divide-and-conquer paradigm. The prototype system has been used to solve several numerical problems efficiently on a wide spectrum of architectures. The target platforms include multicomputers (Thinking Machines CM-5 and Meiko CS-2), networks of workstations (IBM RS/6000s connected by FDDI), multiprocessors (Sequent Symmetry, SGI Power Challenge, and Sun SPARCServer), and a hierarchical system consisting of a cluster of multiprocessors on Myrinet.
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