GPUs offer high-performance floating-point computation at commodity prices, but their usage is hindered by programming models which expose the user to irregularities in the current shared-memory environments and require learning new interfaces and semantics.
This thesis will demonstrate that the message-passing paradigm can be conceptually cleaner than the current data-parallel models for programming GPUs because it can hide the quirks of current GPU shared-memory environments, as well as GPU-specific features, behind a well-established and well-understood interface. This will be shown by demonstrating a proof-of-concept MPI implementation which provides cleaner, simpler code with a reasonable performance cost. This thesis will also demonstrate that, although there is a virtualization constraint imposed by MPI, this constraint is harmless as long as the virtualization was already chosen to be optimal in terms of a strong execution model and nearly-optimal execution time. This will be demonstrated by examining execution times with varying virtualization using a computationally-expensive micro-kernel.
| Identifer | oai:union.ndltd.org:uky.edu/oai:uknowledge.uky.edu:gradschool_theses-1618 |
| Date | 01 January 2009 |
| Creators | Young, Bobby Dalton |
| Publisher | UKnowledge |
| Source Sets | University of Kentucky |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | University of Kentucky Master's Theses |
Page generated in 0.0029 seconds