Computer systems have become increasingly complicated through increased system specialization and heterogeneity designed to meet an increasingly diverse set of system requirements across scale, performance, energy efficiency, reliability, and quality of results. With automated system optimization opportunities being driven by predictive models of system behavior, traditional strategies for manually developing predictive behavioral models have become increasingly more complicated and less precise with growing system complexity.

In this talk, I'll present DiffTune, a technique for learning neurosymbolic performance models of modern computer processors. Processor performance models are critical for many computer systems engineering tasks, however, due to the limits on our ability to introspect modern processors, these models must be inferred from behavioral measurements. Our system leverages deep learning to perform differentiable surrogate optimization of a CPU simulator to yield models that predict the performance of programs executed on modern Intel CPUs better than state-of-the-art, handcrafted techniques from LLVM.

Our approach demonstrates that behavioral models can be effectively learned from data as well as can be constructed to provide an interpretation of their predictions through behavioral traces grounded in the execution of a simulator.