EEG high frequency oscillations, known as ripples, in subdural Electroencephalography (EEG) have been associated to the seizure onset zone (SOZ). Ripples, which can be visible in the frequency range from 80 to 250 Hz, are considered reliable biomarkers (like interictal EEG spikes) to identify the epileptic focus in the brain. Consequently, an automated detection method is proposed with the aim of identifying those electrodes that have a higher count of these events. The computational approach considered in this paper relies on processing the EEG records using the Intel Single-Chip Cloud Computer (SCC) platform. This new method preserves data coherency through the message-passing interface and utilizes dynamic voltage and frequency scaling (DVFS) capability of SCC, yielding both energy saving and performance benefit. The proposed SCC-based method for detecting high frequency oscillations (HFO) is validated by EEG experts at Miami Children's Hospital, and the location of the electrodes with higher counts will be compared with the 3-D source localization using interictal spikes to demonstrate the relation if any that exists between them.