Abstract
.Significance: Despite the ample progress made toward faster and more accurate Monte Carlo (MC) simulation tools over the past decade, the limited usability and accessibility of these advanced modeling tools remain key barriers to widespread use among the broad user community.Aim: An open-source, high-performance, web-based MC simulator that builds upon modern cloud computing architectures is highly desirable to deliver state-of-the-art MC simulations and hardware acceleration to general users without the need for special hardware installation and optimization.Approach: We have developed a configuration-free, in-browser 3D MC simulation platform—Monte Carlo eXtreme (MCX) Cloud—built upon an array of robust and modern technologies, including a Docker Swarm-based cloud-computing backend and a web-based graphical user interface (GUI) that supports in-browser 3D visualization, asynchronous data communication, and automatic data validation via JavaScript Object Notation (JSON) schemas.Results: The front-end of the MCX Cloud platform offers an intuitive simulation design, fast 3D data rendering, and convenient simulation sharing. The Docker Swarm container orchestration backend is highly scalable and can support high-demand GPU MC simulations using MCX over a dynamically expandable virtual cluster.Conclusion: MCX Cloud makes fast, scalable, and feature-rich MC simulations readily available to all biophotonics researchers without overhead. It is fully open-source and can be freely accessed at http://mcx.space/cloud.
Highlights
Since the initial release of the first open-source Monte Carlo (MC) light transport simulator— MCML1—nearly 30 years ago, MC-based photon simulations have been playing important roles amongst the biophotonics research community to facilitate the design and optimization of novel imaging instrumentation and image reconstruction, as well as providing gold-standard solutions for validating novel algorithms and data analysis pipelines
We report a modern, scalable, high-performance, and fully open-source inbrowser MC simulation platform—Monte Carlo eXtreme (MCX) Cloud—to bring state-of-the-art graphics processing units (GPUs) hardware and our extensively optimized and feature-rich MCX simulator software to the rapidly growing biophotonics research community
In the front-end, we have developed a modern web graphical user interface (GUI) based upon a list of open-source web technologies, such as HTML5 markup language,[23] cascading style sheets (CSS), JavaScript, and JQuery[24] for GUI development, and WebGL25 for in-browser 3D data rendering
Summary
Since the initial release of the first open-source Monte Carlo (MC) light transport simulator— MCML1—nearly 30 years ago, MC-based photon simulations have been playing important roles amongst the biophotonics research community to facilitate the design and optimization of novel imaging instrumentation and image reconstruction, as well as providing gold-standard solutions for validating novel algorithms and data analysis pipelines. The adoption of massively parallel computing and graphics processing units (GPUs) have greatly improved the computational efficiency of conventional MC simulations, shortening the simulation run-time by tens to hundreds fold on a modern GPU.[2,3,4,5,6,7] In parallel, a list of new MC algorithms were proposed to handle more complex and accurate tissue anatomical boundaries.[8,9,10,11] Among these algorithms, mesh-based Monte Carlo (MMC) offers the capability to accurately model a curved tissue boundary with tetrahedral meshes while performing ray-tracing computation significantly more efficiently than surface-based MC techniques.[8] More recently, hybrid approaches that combine shape representations offer further computational efficiency and accuracy.[12,13,14,15] These hybrid approaches include (1) dual-grid MMC (DMMC)[12] that combines a coarse tetrahedral mesh with a dense voxelated output volume, (2) split-voxel MC (SVMC)[14] that combines curved surface meshes within a compact voxel data structure, and (3) implicit MMC (iMMC)[15] that combines a skeletal tetrahedral mesh with implicitly defined shapes such as tubes, spheres and thin membranes. These enhancements in modeling geometry have resulted in significantly improved accuracy, which can be directly translated to further speed enhancement while achieving the same output accuracy as conventional approaches
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