A Directive Generation Approach to High Code-Maintainability for Various HPC Systems

Abstract

The emergence of various high-performance computing (HPC) systems compels users to write a code considering the characteristic of each HPC system. To describe the system-dependent information without drastic code modifications, the directive sets such as the OpenMP directive set and the OpenACC directive set are proofed to be useful. However, the code becomes complex to achieve high performance on various HPC systems because different directive sets are required for various HPC systems. Thus, the code-maintainability and readability are degraded. This paper proposes a directive generation approach that generates various kinds of directive sets using user-defined rules. Instead of using several kinds of directive sets, users only have to write special placeholders that are utilized to specify a unique code pattern where several directives are inserted. Then, the special placeholders trigger the generation of appropriate directives for each system using a user-defined rule with a code transformation framework Xevolver. Because only special placeholders are inserted in the code, the proposed approach can keep the code-maintainability and readability. From the performance evaluations of directivebased implementations on various HPC systems, it is shown that the best implementation is different among the HPC systems. Then, through the demonstration of transformation into multiple kinds of implementations, the proposed approach can successfully generate directives from a smaller number of special placeholders. Therefore, it is clarified that the proposed directive generation approach is effective to keep the maintainability of a code to be executed on various HPC systems.

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“…The efforts to work on comprehensive and generic approaches are many nowadays. Examples include the construction of standard programming interface for parallel systems, abstraction of complexity in multiple GPU processing, the troubles on scheduling workflows and proposed checkpoints, identification of programming errors in HPC applications, and code maintainability at massively distributed systems …”