Transient hardware faults have become prevalent due to the shrinking size of transistors, leading to silent data corruptions (SDCs). Therefore, HPC applications need to be evaluated (e.g., via fault injections) and protected to meet the reliability target. In the evaluation, the target programs exercise with a set of given inputs which are usually from program benchmark suite. However, these inputs rarely manifest the SDC vulnerabilities, leading to over-optimistic assessment and unexpectedly higher failure rates in production. We propose Peppa-X, which efficiently identifies the test inputs that estimate the bound of program SDC resiliency. Our key insight is that the SDC sensitivity distribution in a program often remains stationary across input space. Thereby, we can guide the search of SDC-bound inputs by a sampled distribution. Our evaluation shows that Peppa-X can identify the SDC-bound input of a program that existing methods cannot find even with 5x more search time.

Error-bounded lossy compression is becoming more and more important to today's extreme-scale HPC applications because of the ever-increasing volume of data generated because it has been widely used in in-situ visualization, data stream intensity reduction, storage reduction, I/O performance improvement, checkpoint/restart acceleration, memory footprint reduction, etc. Although many works have optimized ratio, quality, and performance for different error-bounded lossy compressors, there is none of the existing works attempting to systematically understand the impact of lossy compression errors on HPC application due to error propagation.In this paper, we propose and develop a lossy compression fault injection tool, called LCFI. To the best of our knowledge, this is the first fault injection tool that helps both lossy compressor developers and users to systematically and comprehensively understand the impact of lossy compression errors on HPC programs. The contributions of this work are threefold: (1) We propose an efficient approach to inject lossy compression errors according to a statistical analysis of compression errors for different state-of-the-art compressors. (2) We build a fault injector which is highly applicable, customizable, easy-to-use in generating top-down comprehensive results, and demonstrate the use of LCFI. (3) We evaluate LCFI on four representative HPC benchmarks with different abstracted fault models and make several observations about error propagation and their impacts on program outputs.

Libraries are digitizing their collections of maps from all eras, generating increasingly large online collections of historical cartographic resources. Aligning such maps to a modern geographic coordinate system greatly increases their utility. This work presents a method for such automatic georeferencing, matching raster image content to GIS vector coordinate data. Given an approximate initial alignment that has already been projected from a spherical geographic coordinate system to a Cartesian map coordinate system, a probabilistic shape-matching scheme determines an optimized match between the GIS contours and ink in the binarized map image. Using an evaluation set of 20 historical maps from states and regions of the U.S., the method reduces average alignment RMSE by 12%.