In this year’s first Square Talk, Debasmita discusses how sound mixed-precision tuning can achieve performance comparable to dynamic analysis while preserving correctness guarantees.
This is joint work with Anastasia Isychev (TU Wien) and was published at OOPSLA ’25.
SPEAKER: Debasmita Lohar, Assist. Prof. at ITU.
ABSTRACT: Mixed-precision tuning is an important optimization method for numerical programs, where some variables and operations are assigned lower precisions to improve overall performance. This method is increasingly used in embedded systems, scientific computing, and machine learning, where efficiency and resource constraints matter. The key challenge is the trade-off: reduced precision speeds up execution but introduces rounding errors that may compromise accuracy. Our work presents the first comprehensive evaluation of state-of-the-art mixed-precision tuning techniques. We compare sound static analyses, which provide formal guarantees but are often viewed as conservative, with dynamic methods, which explore a broader optimization space but can violate accuracy, sometimes by several orders of magnitude. Using an extensive study on the FPBench benchmark suite, we quantify the performance and soundness trade-offs and show that sound tools, enhanced with techniques such as regime inference, can match or even outperform dynamic approaches while preserving correctness guarantees.
Yet, extending sound guarantees to larger, real-world programs remains challenging. Progress will likely require tailoring static analyses to specific application domains, and or combining them with targeted heuristic guidance to navigate the search space better. Thus, precision tuning can become a practical component of numerical software development, not a manual, expert-only process.
PAPER: https://dl.acm.org/doi/epdf/10.1145/3763137.