Name
Title | ||
|---|---|---|
High Performance Computer Acoustic Data Accelerator: A New System for Exploring Marine Mammal Acoustics for Big Data Applications |
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Delma,Detection theory,Computer science,Communication channel,Bioacoustics,Real-time computing,Software,Big data,Operating system,Distributed computing,Scalability |
DocType | Volume | Citations |
Journal | abs/1509.03591 | 5 |
PageRank | References | Authors |
0.92 | 6 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peter Dugan | 1 | 17 | 5.16 |
| john a zollweg | 2 | 6 | 1.32 |
| marian popescu | 3 | 5 | 0.92 |
| Denise Risch | 4 | 10 | 2.42 |
| Hervé Glotin | 5 | 32 | 6.17 |
| Yann LeCun | 6 | 26090 | 3771.21 |
| clark | 7 | 5 | 0.92 |
| Christopher W. Clark | 8 | 22 | 7.10 |