Automatic data layout for distributed memory machines

The goal of languages like Fortran D or High Performance Fortran (HPF) is to provide a simple yet efficient machine-independent parallel programming model. Besides the algorithm selection, the data layout choice is the key intellectual challenge in writing an efficient program in such languages. The performance of a data layout depends on the target compilation system, the target machine, the problem size, and the number of available processors. This makes the choice of a good layout extremely difficult for most users of such languages. This thesis discusses the design and implementation of a data layout selection tool that generates Fortran D or HPF style data layout specifications automatically. Because the tool is not embedded in the target compiler and will be run only a few times during the tuning phase of an application, it can use techniques that may be considered too computationally expensive for inclusion in today's compilers. The proposed framework for automatic data layout selection builds and examines explicit search spaces of candidate data layouts. A candidate layout is an efficient layout for some part of the program. After the generation of search spaces, a single candidate layout is selected for each program part, resulting in a data layout for the entire program. A good overall data layout may require the remapping of arrays between program parts. A performance estimator based on a compiler model, an execution model, and a machine model is used to predict the execution time of each candidate layout and the costs of possible remappings between candidate data layouts. The machine model uses the novel training set approach which determines the costs of arithmetic operations and simple communication patterns. In the proposed framework, instances of NP-complete problems are solved during the construction of candidate layout search spaces and the final selection of candidate layouts from each search space. Rather than resorting to heuristics prematurely, the framework capitalizes on state-of-the-art 0-1 integer programming technology to compute optimal solutions of these NP-complete problems. A prototype of the data layout assistant tool has been implemented. Experiments indicate that good data layouts can be determined efficiently