TornadoViz: Visualizing Heterogeneous Execution Patterns in Modern Managed Runtime Systems

With the increasing prevalence of machine learning and large language model (LLM) inference, heterogeneous computing has become essential. Modern JVMs are embracing this transition through projects such as TornadoVM and Babylon, which enable hardware acceleration on diverse hardware resources, including GPUs and FPGAs. However, while performance results are promising, developers currently face a significant tooling gap: traditional profilers excel at CPU-bound execution but become a “black box” when execution transitions to accelerators, providing no visibility into device memory management, execution patterns or cross-device data movement. This gap leaves developers without a unified view of how their Java applications behave across the heterogeneous computing stack. In this paper, we present TornadoViz, a visual analytics tool that leverages TornadoVM’s specialized bytecode system to provide interactive analysis of heterogeneous execution and object lifecycles in managed runtime systems. Unlike existing tools, TornadoViz bridges the managed-native divide by interpreting the bytecode stream that orchestrates heterogeneous execution, hence connecting high-level application logic with low-level hardware utilization patterns. Our tool enables developers to visualize task dependencies, track memory operations across devices, analyze bytecode distribution patterns, and identify performance bottlenecks through interactive dashboards.