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C codegen considered unnecessary: go directly to binary, do not pass C. Compilation of Julia code for deployment in model-based engineering

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arxiv 2502.01128 v2 pith:M35OX56R submitted 2025-02-03 eess.SY cs.SY

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keywords juliacodehigh-levellanguageprogramprogrammingapproachbinaries
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Since time immemorial an old adage has always seemed to ring true: you cannot use a high-level productive programming language like Python or R for real-time control and embedded-systems programming, you must rewrite your program in C. We present a counterexample to this mantra by demonstrating how recent compiler developments in the Julia programming language allow users of Julia and the equation-based modeling language ModelingToolkit to compile and deploy binaries for real-time model-based estimation and control. Contrary to the approach taken by a majority of modeling and simulation tools, we do not generate C code, and instead demonstrate how we may use the native Julia code-generation pipeline through LLVM to compile architecture-specific binaries from high-level code. This approach avoids many of the restrictions typically placed on high-level languages to enable C-code generation. As case studies, we include a nonlinear state estimator derived from an equation-based model which is compiled into a program that performs state estimation for deployment onto a Raspberry Pi, as well as a PID controller library implemented in Julia and compiled into a shared library callable from a C program.

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  1. Accelerating Bidiagonalization of Banded Matrices through Memory-Aware Bulge-Chasing on GPUs

    cs.DC 2025-10 conditional novelty 6.0 of 10

    A memory-aware GPU bulge-chasing algorithm reduces banded matrices to bidiagonal form, achieving >100x speedups over CPU libraries at 32k sizes.

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