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A Lock Free Approach To Parallelize The Cellular Potts Model: Application To Ductal Carcinoma In Situ.

Antonio J Tomeu^1,2, Alberto G Salguero²

¹University of Cadiz, Computer Science, Escuela Superior de Ingeniería, Campus of Puerto RealPuerto Real, Spain.

Journal of Integrative Bioinformatics

|April 9, 2020

Summary

This study introduces a new parallel approach for the Cellular Potts Model (CPM) using transactional memory, improving computational performance for multiscale biological simulations like breast cancer modeling.

Keywords:

Cellular Automata Cellular Potts Model DCIS multicore parallel software transactional memory speedup

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Area of Science:

Computational biology
Biophysics
Bioinformatics

Background:

The Cellular Potts Model (CPM) is widely used for simulating multiscale biological systems.
Existing parallelization methods for CPM face performance limitations due to data coherence techniques like locks or extensive data dissemination.
Efficient parallelization is crucial for handling complex biological simulations.

Purpose of the Study:

To propose a novel, efficient parallelization approach for the Cellular Potts Model (CPM).
To enhance the computational performance of CPM simulations by addressing limitations of existing parallelization techniques.
To demonstrate the applicability of the new approach using a biological case study.

Main Methods:

Developed an alternative parallelization strategy for the CPM utilizing transactional memory.
Implemented the parallelized CPM in Java.
Applied the Java implementation to simulate ductal adenocarcinoma in situ (DCIS) of the breast.
Analyzed execution times and speedups on a university cluster.

Main Results:

The transactional memory-based parallelization approach effectively maintains data coherence.
The Java implementation achieved significant speedups in simulating DCIS.
Performance analysis demonstrated the computational advantages of the proposed method.

Conclusions:

Transactional memory offers a viable and efficient alternative for parallelizing the Cellular Potts Model.
The proposed method overcomes performance bottlenecks associated with traditional parallelization techniques.
This approach has the potential to accelerate complex multiscale biological simulations, including cancer modeling.