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Industries like automotive, robotics, and finance are increasingly implementing computational workloads like simulations, machine learning (ML) model training, and big data analytics to improve their products. For example, automakers rely on simulations to test autonomous driving features, robotics companies train ML algorithms to enhance robot perception capabilities, and financial firms run in-depth analyses to better manage risk, process transactions, and detect fraud.
Some of these workloads, including simulations, are especially complicated to run due to their diversity of components and intensive computational requirements. A driving simulation, for instance, involves generating 3D virtual environments, vehicle sensor data, vehicle dynamics controlling car behavior, and more. A robotics simulation might test hundreds of autonomous delivery robots interacting with each other and other systems in a massive warehouse environment.