Study of data locality for real-time biomedical signal processing of streaming data on Cell Broadband Engine

Abstract

High performance computing is becoming critical in the medical area to aid real-time processing of complex analysis of biological signals. In this paper parallel schemes for real-time computations of pair-wise correlation (PWC) of electroencephalogram (EEG) signals, which belongs to streaming-data class of applications, are proposed and implemented and their performances are evaluated. Currently most of the EEG based diagnosis for epilepsy is done off-line. However, there is a growing need to perform these diagnoses in real-time to aid health care providers, including surgeons, in decision-making process that will lead to improved quality of life and prevent undesirable consequences, such as readmission to hospitals resulting in prolonged suffering and higher health care costs. Systematic study of the PWC problem and the IBM Cell Broadband Engine (CBE) architecture led us to a model that is well suited for the Cell architecture and GPUs. Measurements on the CBE indicate that speedup of 33.91 is possible over the serial code running on Intel Xeon processor and the schemes can be used for real-time signal processing.