부산대학교 도서관

Electrocardiogram (ECG) signal features (e.g. Heart rate, intrapeak interval times) are data commonly used in physiological assessment. Commercial off-the-shelf (COTS) software solutions for ECG data processing are available, but are often developed for serialized data processing which scale poorly for large datasets. To address this issue, we’ve developed a Matlab code library for parallelized ECG feature generation. This library uses the pMatlab and MatMPI interfaces to distribute computing tasks over supercomputing clusters using the Simple Linux Utility for Resource Management (SLURM). To profile its performance as a function of parallelization scale, the ECG processing code was executed on a non-human primate dataset on the Lincoln Laboratory Supercomputing TXGreen cluster. Feature processing jobs were deployed over a range of processor counts and processor types to assess the overall reduction in job computation time. We show that individual process times decrease according to a 1/n relationship to the number of processors used, while total computation times accounting for deployment and data aggregation impose diminishing returns of time against processor count. A maximum mean reduction in overall file processing time of 99% is shown.