NDLI: Performance evaluation of multi core systems for high throughput medical applications involving model predictive control

Content Provider	IEEE Xplore Digital Library
Author	Pore, M. Banerjee, A. Gupta, S.K.S.
Copyright Year	2014
Description	Author affiliation: Impact Lab., Arizona State Univ., Tempe, AZ, USA (Pore, M.; Banerjee, A.; Gupta, S.K.S.)
Abstract	Many medical control devices used in case of critical patients have model predictive controllers (MPC). MPC estimate the drug level in the parts of patients body based on their human physiology model to either alarm the medical authority or change the drug infusion rate. This model prediction has to be completed before the drug infusion rate is changed i.e. every few seconds. Instead of mathematical models like the Pharmacokinetic models more accurate models such as spatio-temporal drug diffusion can be used for improving the prediction and prevention of drug overshoot and undershoot. However, these models require high computation capability of platforms like recent many core GPUs or Intel Xeon Phi (MIC) or IntelCore i7. This work explores thread level and data level parallelism and computation versus communication times of such different model predictive applications used in multiple patient monitoring in hospital data centers exploiting the many core platforms for maximizing the throughput (i.e. patients monitored simultaneously). We also study the energy and performance of these applications to evaluate them for architecture suitability. We show that given a set of MPC applications, mapping on heterogeneous platforms can give performance improvement and energy savings.
Starting Page	1
Ending Page	10
File Size	634832
Page Count	10
File Format	PDF
e-ISBN	9781479959761
DOI	10.1109/HiPC.2014.7116884
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-17
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Computational modeling Mathematical model Predictive models Biomedical monitoring Parallel processing Graphics processing units
Content Type	Text
Resource Type	Article

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