Grid computing, or using several potentially heterogeneous computers to execute an application, is becoming increasingly popular. Its principle advantage is that it can be used to solve computationally intensive problems by simultaneously using machines across the Internet. However, a grid poses significant software challenges for parallel computing; in particular, fundamental parallel computing problems are made more difficult.
This project focuses on the data distribution problem, which is to distribute data to processors to minimize application completion time. We propose to to re-examine the data distribution problem on one specific instance of a computational grid: a local area network of heterogeneous computers, where heterogeneity arises from either different architectures or multiple users. Either way, processors in such a cluster can have different relative CPU speeds, different amounts of available physical memory, and different effective I/O latencies.
In such an environment, data distribution is significantly more complicated. One reason is that while the standard data distribution problem assumes the target machine contains uniform processors, we make no such assumption with a heterogeneous cluster. In particular, computation, communication, \emph{and} I/O costs can vary across machines. The other reason is that execution on a heterogeneous cluster increases the likelihood that a scientific parallel program will use an out of core data set.
Accordingly, we propose to address the out-of-core, heterogeneous data distribution problem which is to solve the data distribution problem on a heterogeneous cluster. We will achieve this through the design and implementation of what we call Heterogeneous MPI (HC-MPI), which will be constructed as extensions and analysis built into MPI. The extensions will (1) assist users in writing adaptable programs that HC-MPI can easily modify and (2) allow HC-MPI to collect enough program information to automatically determine an effective data distribution.
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David Lowenthal