CODIR: Towards an MLIR Codelet Model Dialect

Kabrick, Ryan; Perdomo, Diego A. Roa; Raskar, Siddhisanket; Diaz, Jose M. Monsalve; Fox, Dawson; Gao, Guang R.

doi:10.1109/ipdrm51949.2020.00009

Cited by 4 publications

(1 citation statement)

References 18 publications

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“…Sommer et al [29] propose a dialect, and a lowering process to optimize sum-product network inference in both CPUs and GPUs, while DistIR [28] is an IR for distributed computation that employs MLIR to optimize neural networks. Recently, many works have proposed to extend MLIR with new dialects to analyze, optimize and accelerate heterogeneous applications in a variety of domains [12,18,24]. Our work, however, requires no extra abstraction layer and no additional dialect, and can be seen as complementary to previous solutions.…”

Section: Related Workmentioning

confidence: 98%

Lifting Code Generation of Cardiac Physiology Simulation to Novel Compiler Technology

Thangamani

Jost

Loechner

et al. 2023

Proceedings of the 21st ACM/IEEE International Symposium on Code Generation and Optimization

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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-ofthe-art simulation systems by an average of 2.9×, reaching peak speedups of more than 15× 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.

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