SPNC: An Open-Source MLIR-Based Compiler for Fast Sum-Product Network Inference on CPUs and GPUs

Sommer, Lukas; Axenie, Cristian; Koch, Andreas

doi:10.1109/cgo53902.2022.9741277

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…While our overall contribution is to connect a simulating infrastructure for the study of the human heart to compiler technology, our work shows a proof-of-concept MLIR code generation that does not require an additional dialect proposal. Though this could be perceived as lacking since many previous work on MLIR [9,28,29] have proposed new dialects, we have found no justification for the addition of a new language. Indeed, MLIR includes all necessary dialects and operations required to optimize the execution of ionic models.…”

Section: Representation Of Ionic Modelscontrasting

confidence: 59%

“…The approach has shown significant speedup in comparison to state-of-the-art solutions for climate and weather simulation, proving that extra levels of abstractions can help to devise new optimizations. 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].…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Representation Of Ionic Modelscontrasting

confidence: 59%