Deep learning has revolutionized many scene perception tasks over the past decade. Some of these improvements can be attributed to the development of large labeled datasets. The creation of such datasets can be an expensive, time-consuming, and imperfect process. To address these issues, we introduce GeoSynth, a diverse photorealistic synthetic dataset for indoor scene understanding tasks. Each GeoSynth exemplar contains rich labels including segmentation, geometry, camera parameters, surface material, lighting, and more. We demonstrate that supplementing real training data with GeoSynth can significantly improve network performance on perception tasks, like semantic segmentation. A subset of our dataset will be made publicly available at https://github.com/geomagical/GeoSynth.

PerfTrack is a data store and interface for managing performance data from large-scale parallel applications. Data collected in different locations and formats can be compared and viewed in a single performance analysis session. The underlying data store used in PerfTrack is implemented with a database management system (DBMS). PerfTrack includes interfaces to the data store and scripts for automatically collecting data describing each experiment, such as build and platform details. We have implemented a prototype of PerfTrack that can use Oracle or PostgreSQL for the data store. We demonstrate the prototype's functionality with three case studies: one is a comparative study of an ASC purple benchmark on high-end Linux and AIX platforms; the second is a parameter study conducted at Lawrence Livermore National Laboratory (LLNL) on two high end platforms, a 128 node cluster of IBM Power 4 processors and BlueGene/L; the third demonstrates incorporating performance data from the Paradyn Parallel Performance Tool into an existing PerfTrack data store.