Predicted Embedding Power Regression for Large-Scale Out-of-Distribution Detection

Out-of-distribution (OOD) inputs can compromise the performance and safety of real world machine learning systems. While many methods exist for OOD detection and work well on small scale datasets with lower resolution and few classes, few methods have been developed for large-scale OOD detection. Existing large-scale methods generally depend on maximum classification probability, such as the state-of-the-art grouped softmax method. In this work, we develop a novel approach that calculates the probability of the predicted class label based on label distributions learned during the training process. Our method performs better than current state-of-the-art methods with only a negligible increase in compute cost. We evaluate our method against contemporary methods across $14$ datasets and achieve a statistically significant improvement with respect to AUROC (84.2 vs 82.4) and AUPR (96.2 vs 93.7)

Performance Improvements for a Large-scale Geological Simulation

AbstractGeological models have been successfully used to identify and study geothermal energy resources. Many computer simulations based on these models are data-intensive applications. Large-scale geological simulations require high performance computing (HPC) techniques to run within reasonable time constraints and performance levels. One research area that can benefit greatly from HPC techniques is the modeling of heat flow beneath the Earth's surface. This paper describes the application of HPC techniques to increase the scale of research with a well-established geological model. Recently, a serial C++ application based on this geological model was ported to a parallel HPC applications using MPI. An area of focus was to increase the performance of the MPI version to enable state or regional scale simulations using large numbers of processors. First, synchronous communications among MPI processes was replaced by overlapping communication and computation (asynchronous communication). Asynchronous communication improved performance over synchronous communications by averages of 28% using 56 cores in one environment and 46% using 56 cores in another. Second, an approach for load balancing involving repartitioning the data at the start of the program resulted in runtime performance improvements of 32% using 48 cores in the first environment and 14% using 24 cores in the second when compared to the asynchronous version. An additional feature, modeling of erosion, was also added to the MPI code base. The performance improvement techniques under erosion were less effective