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Coprocessor architectures in High Performance Computing are prevalent in today’s scientific computing clusters and require specialized knowledge for proper utilization. Various alternative paradigms for parallel and offload computation exist, but little is known about the human factors impacts of using the different paradigms. With computer science student participants from the University of Nevada, Las Vegas with no previous exposure to Graphics Processing Unit programming, our study compared NVIDIA CUDA C/C++ as a control group and the Thrust library. The designers of Thrust claim their higher level of abstraction enhances programmer productivity. The trial was conducted on 91 participants and was administered through our computerized testing platform. Although the study was narrowly focused on the basic steps of an offloaded computation problem and was not intended to be a comprehensive evaluation of the superiority of one approach or the other, we found evidence that although Thrust was designed for ease of use, the abstractions tended to be confusing to students and in several cases diminished productivity. Specifically, abstractions in Thrust for (i) memory allocation through a C++ Standard Template Library-style vector library call, (ii) memory transfers between the host and Graphics Processing Unit coprocessor through an overloaded assignment operator, and (iii) execution of an offloaded routine through a generic transform library call instead of a CUDA kernel routine all performed either equal to or worse than CUDA.

There are many paradigms available to address the unique and complex problems introduced with parallel programming. These complexities have implications for computer science education as ubiquitous multi-core computers drive the need for programmers to understand parallelism. One major obstacle to student learning of parallel programming is that there is very little human factors evidence comparing the different techniques to one another, so there is no clear direction on which techniques should be taught and how. We performed a randomized controlled trial using 88 university-level computer science student participants performing three identical tasks to examine the question of whether or not there are measurable differences in programming performance between two paradigms for concurrent programming: threads compared to process-oriented programming based on Communicating Sequential Processes. We measured both time on task and programming accuracy using an automated token accuracy map (TAM) technique. Our results showed trade-offs between the paradigms using both metrics and the TAMs provided further insight about specific areas of difficulty in comprehension.

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