enerGyPU and enerGyPhi Monitor for Power Consumption and Performance Evaluation on Nvidia Tesla GPU and Intel Xeon Phi

García, Arturo; Hernandez, Betanzos; Montenegro, Carlos; Navaux, Philippe O. A.; Barrios, J H Carlos

doi:10.1109/ccgrid.2016.100

Cited by 3 publications

(1 citation statement)

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“…The second module is the data manager, compose by three classes designed for splitting, batching and multi-task any dataset over GPU workstations and multi-nodes computational platforms. The third module extends the enerGyPU monitor for workload characterization, constitute by a data capture in runtime to collect the convergence tracking logs and the computing factor metrics; and a dashboard for the experimental analysis results [7]. The fourth module is the runtime that enables the platform selection from GPU workstations to multi-nodes whit different execution modes, such as synchronous and asynchronous coordination gradient computations with gRPC or MPI communication protocols.…”

Section: Background and State Of The Artmentioning

confidence: 99%

DiagnoseNET: Automatic Framework to Scale Neural Networks on Heterogeneous Systems Applied to Medical Diagnosis

Garcia

Precioso

Staccini

et al. 2020

Lecture Notes in Electrical Engineering

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Determine an optimal generalization model with deep neural networks for a medical task is an expensive process that generally requires large amounts of data and computing power. Furthermore, scale deep learning workflows over a wide range of emerging heterogeneous system architecture increases the programming expressiveness complexity for model training and the computing orchestration. We introduce Diag-noseNET, a programming framework designed for scaling deep learning models over heterogeneous systems applied to medical diagnosis. It is designed as a modular framework to enable the deep learning workflow management and allows the expressiveness of neural networks written in TensorFlow, while its runtime abstracts the data locality, micro batching and the distributed orchestration to scale the neural network model from a GPU workstation to multi-nodes. The main approach is composed through a set of gradient computation modes to adapt the neural network according to the memory capacity, the workers' number, the coordination method and the communication protocol (GRPC or MPI) for achieving a balance between accuracy and energy consumption. The experiments carried out allow to evaluate the computational performance in terms of accuracy, convergence time and worker scalability to determine an optimal neural architecture over a mini-cluster of Jetson TX2 nodes. These experiments were performed using two medical cases of study, the former dataset is composed by clinical descriptors collected during the first week of hospitalization of patients in the Provence-Alpes-Côte d'Azur region; the second dataset uses a short ECG records between 30 and 60 seconds, obtained as part of the PhysioNet 2017 Challenge.

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Section: Background and State Of The Artmentioning

confidence: 99%