Image compression using artificial intelligence (AI) is gaining importance in scientific research, where instruments and simulations can produce hundreds of images per second. Effective compression with high ratios is essential for facilitating discoveries. A key challenge is the automatic detection of outliers—cases where compression fails or significant phenomena are present. To address this, we developed a consensus-driven methodology using unsupervised machine learning techniques for identifying outlier compressed images. We evaluated our approach on unlabeled datasets, including microscopy and X-ray images, successfully identifying multiple outliers using metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), structural texture similarity index measure (STSIM) and deep image and structural texture similarity index (DISTS).

The scientific computing paradigm has transitioned from compute-intensive to I/O-intensive and memory-intensive in the past decade, especially when data-driven science has become common practice. Numerous empirical I/O-aware scheduling optimizations have been developed by incorporating I/O capacity and bandwidth as constraints into scheduling. Unfortunately, there is a lack of data flow (I/O) characterization tools and an understanding of trade-offs between concurrency, locality, and I/O bandwidth. To bridge the gap, this work 1) presents a set of descriptors to characterize, organize, and visualize I/O profiles, including flow size, I/O bandwidth, and operation count, which group data flows by I/O types, tasks, and files; 2) proposes an I/O Roofline model-based trade-off analysis to find the optimal trade-off between flow operational intensity, concurrency, and flow performance. The I/O descriptors generate useful insights into complicated I/O behaviors, suggesting distinct concurrency, storage, and scheduling to be used by types, tasks, and files. The proposed trade-off analysis guides scheduling decisions that generate resource assignment with the best flow parallelism. We evaluate our I/O-aware scheduling methodology on a highly I/O-intensive workflow–1000 Genomes. The experimental results demonstrate speedups of up to 2.4× compared to the state-of-the-art methods.