高性能计算机声学数据加速器：探索海洋哺乳动物声学大数据应用的新系统
High Performance Computer Acoustic Data Accelerator: A New System for Exploring Marine Mammal Acoustics for Big Data Applications
摘要：
本文提出了一种新的软件模型设计的分布式声波信号检测运行时使用机器学习算法称为DeLMA。提出了一种新的算法--声学数据挖掘加速器（ADA）。ADA是一个强大而可扩展的解决方案，用于使用分布式计算技术有效地处理大型声音档案。DeLMA和ADA算法一起提供了一个强大的工具，目前正在使用的生物声学研究计划（BRP）在康奈尔大学鸟类学实验室。本文提供了一个高层次的系统技术概述，并讨论了设计的各个方面。给出了基本的运行时性能和项目总结。DeLMA-ADA基准性能比较桌面串行配置与64核分布式HPC系统显示，运行时执行速度提高了44倍。在HPC上使用48个核心进行的性能测试显示，效率是4核心桌面解决方案的9倍至12倍。19个东海岸部署的项目总结结果显示，DeLMA-ADA解决方案迄今已处理超过300万通道小时的声音。
英文摘要：
This paper presents a new software model designed for distributed sonic signal detection runtime using machine learning algorithms called DeLMA. A new algorithm--Acoustic Data-mining Accelerator (ADA)--is also presented. ADA is a robust yet scalable solution for efficiently processing big sound archives using distributing computing technologies. Together, DeLMA and the ADA algorithm provide a powerful tool currently being used by the Bioacoustics Research Program (BRP) at the Cornell Lab of Ornithology, Cornell University. This paper provides a high level technical overview of the system, and discusses various aspects of the design. Basic runtime performance and project summary are presented. The DeLMA-ADA baseline performance comparing desktop serial configuration to a 64 core distributed HPC system shows as much as a 44 times faster increase in runtime execution. Performance tests using 48 cores on the HPC shows a 9x to 12x efficiency over a 4 core desktop solution. Project summary results for 19 east coast deployments show that the DeLMA-ADA solution has processed over three million channel hours of sound to date.
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