Domain-Specific languages (DSLs) such as Triton enable developers to write high-performance GPU kernels in a Python-friendly manner; however, profiling these kernels with existing tools often incurs runtime and storage overhead while failing to deliver actionable insights for both kernel authors and framework developers. We present Proton, a multi-level, adaptive profiler tailored for the Triton programming language and compiler. Proton provides frontend APIs to selectively profile relevant regions, aggregate results, capture custom metrics not available through hardware counters, and query profiles using a SQL-like language. Proton’s backend design unifies vendor profiling APIs with instrumentation-based profiling, ensuring portability and extensibility. Using Proton, users are able to query custom and hardware metrics across the relevant levels of abstraction—full end-to-end model execution, isolated neural network layers, language-specific Triton operators, and compiler intermediate representations. We demonstrate the tool’s effectiveness through case studies on production-grade kernel development, continuous integration, multi-GPU analysis, language model inference, and intra-kernel profiling. Our evaluations on end-to-end workloads, as well as standalone Triton kernels, demonstrate that Proton imposes lower runtime overhead and delivers significant reductions in profile sizes relative to existing framework and vendor profilers while being fully open source.

The lack of manufacturing standards for new technologies (such as additive manufacturing) render qualification of novel components difficult. Paramount in predicting the operational performance of a component is characterizing its material and mechanical properties along with the associated uncertainties. Conventional “make and break” quality control methods are inefficient and expensive for small lot manufacturing, and physics-based modeling of the manufacturing process admits additional uncertainties and difficulties. For these reasons, data-driven predictive models are attractive for the characterization of empirical relationships relating the manufacturing inputs to the component quantities of interest. This talk presents an example of verification & validation and uncertainty quantification (V&V UQ) for steel alloys fabricated on a commercial additive manufacturing machine. The uncertainty surrounding both the experimental data and the model itself are quantified and propagated such that the degree to which the model predictions represent the physical data can be quantified (validation). Calibration of the model parameters is effected through Bayesian inversion. The results of the data-driven model are estimates of material properties for use in establishing parameter-driven, statistically-substantiated material properties for use in component qualification.