Sean P. Ponce

Open source software and the algorithm visualization community

Abstract Algorithm visualizations are widely viewed as having the potential for major impact on computer science education, but their quality is highly variable. We report on the software development practices used by creators of algorithm visualizations, based on data that can be inferred from a catalog of over 600 algorithm visualizations. Since nearly all are free for use and many provide source code, they might be construed as being open source software. Yet many AV developers do not appear to have used open source best practices. We discuss how such development practices might be employed by the algorithm visualization community, and how they might lead to improved algorithm visualizations in the future. We conclude with a discussion of OpenDSA, an open-source project that builds on earlier progress in the field of algorithm visualization and hopes to use open-source procedures to gain users and contributors.

Parallel Mining of Neuronal Spike Streams on Graphics Processing Units

Multi-electrode arrays (MEAs) provide dynamic and spatial perspectives into brain function by capturing the temporal behavior of spikes recorded from cultures and living tissue. Understanding the firing patterns of neurons implicit in these spike trains is crucial to gaining insight into cellular activity. We present a solution involving a massively parallel graphics processing unit (GPU) to mine spike train datasets. We focus on mining frequent episodes of firing patterns that capture coordinated events even in the presence of intervening background events. We present two algorithmic strategies—hybrid mining and two-pass elimination—to map the finite state machine-based counting algorithms onto GPUs. These strategies explore different computation-to-core mapping schemes and illustrate innovative parallel algorithm design patterns for temporal data mining. We also provide a multi-GPU mining framework, which exhibits additional performance enhancement. Together, these contributions move us towards a real-time solution to neuronal data mining.