The study of numerical models for the human body has become a major focus of the research community in biology and medicine.
For instance, numerical ionic models of a complex organ, such as the heart, must be able to represent individual cells and their interconnections through ionic channels, forming a system with billions of cells, and requiring efficient code to handle such a large system.
The modeling of the electrical system of the heart combines a compute-intensive kernel that calculates the intensity of current flowing through cell membranes, and feeds a linear solver for computing the electrical potential of each cell.

Considering this context, we propose limpetMLIR, a code generator and compiler transformer to accelerate the kernel phase of ionic models and bridge the gap between compiler technology and electrophysiology simulation.
LimpetMLIR makes use of the MLIR infrastructure, its dialects, and transformations to drive forward the study of ionic models, and accelerate the execution of multi-cell systems.
Experiments conducted in 43 ionic models show that our limpetMLIR based code generation greatly outperforms current state-of-the-art simulation systems by an average of
2.9x, reaching peak speedups of more than 15x in some cases.
To our knowledge, this is the first work that deeply connects an optimizing compiler infrastructure to electrophysiology models of the human body, showing the potential benefits of using compiler technology in the simulation of human cell interactions.