This study accompanies the initial public release of the software for ARORA, or A Realistic Open environment for Rapid Agent training, and marks a high point of several years of work for the mature and completely open ARORA simulator. The purpose of ARORA is to support the training of an autonomous agent for tasks associated with a large-scale and geospecific outdoor urban environment, including the task of navigation as a car. The study elaborates on the simulator's architecture, agent, and environment. For the environment, ARORA provides an improvement on similar simulators through an unconstrained geospecific environment with detailed semantic annotation. The agent is represented as a car available with four different options of physics fidelity. The agent also has sensors available: a pose sensor, a camera sensor, and a set of three proximity sensors. Future use cases from training extend to both civilians and militaries (including human training and wargaming), in terms of training autonomous agents in outdoor urban environments. The study also presents a brief description of NavSim, a Python-based companion tool. The purpose of NavSim is to connect to ARORA (or any other similar simulator) and train an agent using reinforcement-learning algorithms. The study also provides challenges in development and subsequent work-arounds and solutions. The goal of the ARORA & NavSim system is to provide communities with a high-fidelity, publicly available, free, and open-source system for training an autonomous agent as a car.
Scientific Big Data being gathered at exascale needs to be stored, retrieved and manipulated. The storage stack for scientific Big Data includes a file system at the system level for physical organization of the data, and a file format and input/output (I/O) system at the application level for logical organization of the data; both of them of high-performance variety for exascale. The high-performance file system is designed with concurrent access, high-speed transmission and fault tolerance characteristics. High-performance file formats and I/O are designed to allow parallel and distributed applications with easy and fast access to Big Data. These specialized file formats make it easier to store and access Big Data for scientific visualization and predictive analytics. This chapter provides a brief review of the characteristics of high-performance file systems such as Lustre and GPFS, and high-performance file formats such as HDF5, NetCDF, MPI-IO, and HDFS.
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