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Communication collectives are at the heart of distributed-memory parallel algorithms and the Message Passing Interface. In parallel computing courses, students can learn about collectives not only to utilize them as building blocks to implement other algorithms, but also as exemplars for designing and analyzing efficient algorithms. We develop a visualization tool to help students understand different algorithms for collective operations as well as evaluate and analyze the algorithms' efficiencies. Our implementation is written in C++ with OpenMP and uses the Thread Safe Graphics Library. We simulate distributed-memory message passing to implement the algorithms, and the threads concurrently illustrate their local memories and message passing using a shared canvas. Our tool includes visualizations of different algorithms for Scatter, Gather, ReduceScatter, AllGather, Broadcast, Reduce, AllReduce, and AlltoAll.
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Beyond collective intelligence: Collective adaptation
We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for ‘intelligent’ collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.
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- Award ID(s):
- 1918490
- PAR ID:
- 10433778
- Date Published:
- Journal Name:
- Journal of The Royal Society Interface
- Volume:
- 20
- Issue:
- 200
- ISSN:
- 1742-5662
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1098/rsif.2022.0736
