Dataset Information

Lessons Learned in a Decade of Research Software Engineering GPU Applications

ABSTRACT: After years of using Graphics Processing Units (GPUs) to accelerate scientific applications in fields as varied as tomography, computer vision, climate modeling, digital forensics, geospatial databases, particle physics, radio astronomy, and localization microscopy, we noticed a number of technical, socio-technical, and non-technical challenges that Research Software Engineers (RSEs) may run into. While some of these challenges, such as managing different programming languages within a project, or having to deal with different memory spaces, are common to all software projects involving GPUs, others are more typical of scientific software projects. Among these challenges we include changing resolutions or scales, maintaining an application over time and making it sustainable, and evaluating both the obtained results and the achieved performance.

SUBMITTER: Krzhizhanovskaya V

PROVIDER: S-EPMC7304729 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:CrowdsourcingCrowdsourcing is the practice of obtaining needed ideas, services, or content by requesting contributions from a large group of people. Amazon Mechanical Turk is a web marketplace for crowdsourcing microtasks, such as answering surveys and image tagging. We explored the limits of crowdsourcing by using Mechanical Turk for a more complicated task: analysis and creation of wind simulations.Harnessing crowdworkers for engineeringOur investigation examined the feasibility of using crowdsourcing for complex, highly technical tasks. This was done to determine if the benefits of crowdsourcing could be harnessed to accurately and effectively contribute to solving complex real world engineering problems. Of course, untrained crowds cannot be used as a mere substitute for trained expertise. Rather, we sought to understand how crowd workers can be used as a large pool of labor for a preliminary analysis of complex data.Virtual wind tunnelWe compared the skill of the anonymous crowd workers from Amazon Mechanical Turk with that of civil engineering graduate students, making a first pass at analyzing wind simulation data. For the first phase, we posted analysis questions to Amazon crowd workers and to two groups of civil engineering graduate students. A second phase of our experiment instructed crowd workers and students to create simulations on our Virtual Wind Tunnel website to solve a more complex task.ConclusionsWith a sufficiently comprehensive tutorial and compensation similar to typical crowd-sourcing wages, we were able to enlist crowd workers to effectively complete longer, more complex tasks with competence comparable to that of graduate students with more comprehensive, expert-level knowledge. Furthermore, more complex tasks require increased communication with the workers. As tasks become more complex, the employment relationship begins to become more akin to outsourcing than crowdsourcing. Through this investigation, we were able to stretch and explore the limits of crowdsourcing as a tool for solving complex problems.

Dataset Information

Lessons Learned in a Decade of Research Software Engineering GPU Applications

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets