Modeling of high performance programs to support heterogeneous computing

Abstract

In order to harness the power of multicore CPUs and GPUs, HPC (High Performance Computing) programmers and even end-users need new tools and techniques to express their core problem, divide that core problem into sub problems, allocate computational resources for the sub problems, execute the resources, and collect results. HPC users focus more on the problem domain while HPC programmers are concerned with the code or HPC domain. However, in current practice, the distinction of programmers and users is not clearly delineated because most of the end-users (e.g., scientists who have a computational need) must create and write their own HPC code. Moreover, HPC users also have to maintain the HPC source code to keep abreast with the latest advances, techniques and platforms introduced by the HPC programming community. The specific aim of this dissertation is to introduce new software engineering ideas (e.g., Model-Driven Engineering (MDE) and Domain-Specific Languages (DSLs)) and supporting tools to assist in the evolution of parallel programs used by HPC programmers, as well as HPC users. In this dissertation, we show that tool support can be provided for HPC programs at different levels of abstraction targeted for a specific set of users. These levels of abstraction are: 1) Code-level, 2) Algorithm-level, 3) Program-level, and 4) Sub-domain-level. We designed, implemented, and evaluated DSLs at each abstraction level to support heterogeneous computing. Code-level abstraction is very general and it can be applied to any C/C++ program, while algorithm-level abstraction is only applicable for programs implementing MapReduce algorithms. Compared to code-level and algorithm-level abstraction, program-level and sub-domain-level abstractions are very specific and are only applicable to specific domains and users (e.g., Signature Discovery Intiative (SDI) project participants and Nbody solution users). We observed that if the domain is specific, less information is required from the user because the DSLs are domain-aware. If the domain is very general (e.g., in the code-level and algorithm-level abstractions), there are more application usage areas for the DSL, but adoption of the DSL at more general levels requires additional information from the end-users.