A Visual Profiling System for Direct Volume Rendering

Abstract

Direct Volume Rendering (DVR) is a crucial technique that enables interactive exploration of results from scientific computing or computer graphics. Its applications range from virtual prototyping for product design to computer-aided diagnosis in medicine. Although there are many existing DVR optimizations, they do not provide a thorough analysis of memory-specific hardware behavior. This paper introduces a profiling toolkit that enables the extraction of performance metrics, such as cache hit rates and branching, from a compiled GPU-based DVR application. The metrics are visualized in the image domain to facilitate spatial visual analysis. This paper presents a pipeline that automatically extracts memory traces using binary instrumentation, simulates the GPU memory subsystem, and models DVR-specific functionality within it. The profiler is demonstrated using the Octree-Linear Bounding Volume Hierarchy (OLBVH), and the visualized profiling metrics are explained based on the OLBVH implementation. Our discussion demonstrates that optimizing ray traversal for adaptive sampling, cache usage, branching, and global memory access has the potential to improve performance.