Cognitive Aspects of Parallel Programming

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Parallel computing involves solving a computing problem in less time by breaking it down in parts and computing those parts simultaneously. The hardware required for parallel processing hardware continues to progress rapidly, enabling ever-increasing theoretical operation counts per second. However, it is well known that parallel programming systems haven't kept up with the advances in hardware: while hundreds of parallel languages have been proposed, very few have taken hold beyond the language developers themselves. One reason for this is usability - that is the degree of ease with which one can develop and maintain parallel programs that are both correct and reach the desired level of performance. The current body of knowledge of parallel programming usability is very limited, both in the number of contributions and the methodology of the studies. A major limitation of existing studies is that they are not based on a theoretical framework. This means that while they may be able to determine if one language is better than another in a particular experimental context, it is difficult to apply the results more generally. Writing a program involves more than just syntactic and semantic knowledge. It also involves schematic knowledge, which consists of generic solutions, as shown in studies of the cognitive processes of program design. Writing programs is also intimately tied to understanding programs, since schemas can be learned by reading programs, and practical programming often involves modifying existing programs. Program understanding requires the reader to construct a mental model. These theories of program design and understanding have been applied to procedural and object oriented programming but not to parallel programming.The long-term research goal of this proposal is to discover the cognitive models and processes used by parallel programmers, and how these depend on the programming language. This work will extend the current body of knowledge on the psychology of programming, which has only been applied to sequential programming. This work will have important applications to assessing the usability of parallel programming languages, designing supporting tools, and the teaching of parallel programming. The proposed research builds on my graduate textbook, Elements of Parallel Computing, published in 2017 by Chapman and Hall/CRC Press, in their Computational Science series. This book presents the algorithmic and implementation structures that are practically relevant for parallel programming. Such structures have endured through many changes in parallel hardware and software. The proposed work will investigate how these structures form part of the mental models that parallel programmers use when writing and understanding programs.