Colonoscopy is an endoscopic technique that allows a physician to inspect the mucosa of the human colon. It has contributed to a marked decline in the number of colorectal cancer related deaths. However, recent data suggest that there is a significant (4-12%) miss-rate for the detection of even large polyps and cancers. To address this, we have investigated automated post-procedure and real-time quality measurements by analyzing colonoscopy videos. One of the fundamental steps is separating informative frames from non-informative frames, a process called Informative Frame Filtering (IFF). Non-informative frames comprise out-of-focus frames and frames lacking typical features of the colon. We introduce a new IFF algorithm in this paper, which is much more accurate than our previous one. Also, we exploit the many-core GPU (Graphics Processing Unit) to create an IFF software module for High Performance Computing (HPC). Code optimizations embedded in the many-core GPU resulted in a 40-fold acceleration compared to CPU-only implementation for our IFF software module.