Fig. 1.A batch of renders from the HSSD dataset, a popular embodied AI dataset with complex apartment-scale scenes (7.4 million triangles per scene on average).When rendering a batch of 1024 views at 128×128 pixels each, our ray tracer generates frames at an aggregate throughput of 25K frames/sec on a H100 GPU.We perform a systematic study of batch rendering performance under a range of embodied AI workloads and find that ray tracing based solutions, rather than rasterization based solutions, are a preferred rendering solution when executing on widely used datacenter-class GPUs.In this paper we study the problem of efficiently rendering images for embodied AI training workloads, where agent training involves rendering millions to billions of independent, low-resolution frames, often with simple lighting and shading, that serve as the agent's observations of the world.To enable high-throughput training from images, we design a flexible, batchmode rendering interface that allows state-of-the-art GPU-accelerated batch world simulators to efficiently communicate with high-performance rendering backends.Using this interface we architect and compare two highperformance renderers: one based on the GPU hardware-accelerated graphics pipeline and a second based on a GPU software implementation of ray tracing.To evaluate these renderers and encourage further research by the graphics community in this area, we build a rendering benchmark for this under-explored regime.We find that the ray tracing renderer outperforms the rasterization-based solution across the benchmark on a datacenter-class GPU, while also performing competitively in geometrically complex environments on a high-end consumer GPU.When tasked to render large batches of independent 128×128 images, the ray tracer can exceed 100,000 frames per second per GPU for simple scenes, and exceed 10,000 frames per second per GPU on geometrically complex scenes from the HSSD dataset.

Training AI agents to perform complex tasks in simulated worlds requires millions to billions of steps of experience. To achieve high performance, today's fastest simulators for training AI agents adopt the idea of batch simulation: using a single simulation engine to simultaneously step many environments in parallel. We introduce a framework for productively authoring novel training environments (including custom logic for environment generation, environment time stepping, and generating agent observations and rewards) that execute as high-performance, GPU-accelerated batched simulators. Our key observation is that the entity-component-system (ECS) design pattern, popular for expressing CPU-side game logic today, is also well-suited for providing the structure needed for high-performance batched simulators. We contribute the first fully-GPU accelerated ECS implementation that natively supports batch environment simulation. We demonstrate how ECS abstractions impose structure on a training environment's logic and state that allows the system to efficiently manage state, amortize work, and identify GPU-friendly coherent parallel computations within and across different environments. We implement several learning environments in this framework, and demonstrate GPU speedups of two to three orders of magnitude over open source CPU baselines and 5-33× over strong baselines running on a 32-thread CPU. An implementation of the OpenAI hide and seek 3D environment written in our framework, which performs rigid body physics and ray tracing in each simulator step, achieves over 1.9 million environment steps per second on a single GPU.