Evaluating Text Analytic Frameworks for Mental Health Surveillance

Mayer, Benjamin; Arnold, J.G.; Begoli, Edmon; Rush, Everett; Drewry, Michael; Brown, Kris; Ponce, Eduardo; Srinivas, Sudarshan

doi:10.1109/icdew.2018.00014

Cited by 1 publication

(1 citation statement)

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“…Another motivation for the PaPaS framework is its applicability for evaluating machine learning and natural language processing algorithms [21]. Due to the numerous set of tunable hyperparameters [42] and the breadth of machine learning toolkits available, both parameter and performance studies are labor-intensive.…”

Section: Papas Motivationmentioning

confidence: 99%

PaPaS

Ponce

Stephenson

Lenhart

et al. 2018

Proceedings of the Practice and Experience on Advanced Research Computing

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The current landscape of scientific research is widely based on modeling and simulation, typically with complexity in the simulation's flow of execution and parameterization properties. Execution flows are not necessarily straightforward since they may need multiple processing tasks and iterations. Furthermore, parameter and performance studies are common approaches used to characterize a simulation, often requiring traversal of a large parameter space. High-performance computers offer practical resources at the expense of users handling the setup, submission, and management of jobs. This work presents the design of PaPaS, a portable, lightweight, and generic workflow framework for conducting parallel parameter and performance studies. Workflows are defined using parameter files based on keyword-value pairs syntax, thus removing from the user the overhead of creating complex scripts to manage the workflow. A parameter set consists of any combination of environment variables, files, partial file contents, and command line arguments. PaPaS is being developed in Python 3 with support for distributed parallelization using SSH, batch systems, and C++ MPI. The PaPaS framework will run as user processes, and can be used in single/multi-node and multi-tenant computing systems. An example simulation using the BehaviorSpace tool from NetLogo and a matrix multiply using OpenMP are presented as parameter and performance studies, respectively. The results demonstrate that the PaPaS framework offers a simple method for defining and managing parameter studies, while increasing resource utilization.

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Section: Papas Motivationmentioning

confidence: 99%