- Award ID(s):
- 1726188
- NSF-PAR ID:
- 10211255
- Editor(s):
- Obeid, Iyad; Selesnick, Ivan; Picone, Joseph
- Date Published:
- Journal Name:
- IEEE Signal Processing in Medicine and Biology Symposium (SPMB)
- Volume:
- 1
- Issue:
- 1
- Page Range / eLocation ID:
- 01 to 03
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Obeid, I. ; Selesnik, I. ; Picone, J. (Ed.)The Neuronix high-performance computing cluster allows us to conduct extensive machine learning experiments on big data [1]. This heterogeneous cluster uses innovative scheduling technology, Slurm [2], that manages a network of CPUs and graphics processing units (GPUs). The GPU farm consists of a variety of processors ranging from low-end consumer grade devices such as the Nvidia GTX 970 to higher-end devices such as the GeForce RTX 2080. These GPUs are essential to our research since they allow extremely compute-intensive deep learning tasks to be executed on massive data resources such as the TUH EEG Corpus [2]. We use TensorFlow [3] as the core machine learning library for our deep learning systems, and routinely employ multiple GPUs to accelerate the training process. Reproducible results are essential to machine learning research. 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