University of Stuttgart Advances MegaMol Cross-Platform Visualization Framework

By Rob Johnson

April 16, 2018

The MegaMol team at the Visualization Research Center of the University of Stuttgart (VISUS) works each day to empower discoveries in fields like biochemistry, thermodynamics, medicine, and physics. Initially conceived by researcher Sebastian Grottel, the particle visualization framework MegaMol has been in development for over a decade with support from the Computer Graphics and Visualization Group of the TU Dresden and other colleagues. The framework enables imaging and exploration of the tiny-scale complexities of molecular dynamics in unprecedented three-dimensional detail. MegaMol supports rapid prototyping using powerful desktop systems utilizing both Microsoft Windows and Linux.

As science progresses, major research projects today must accommodate increasingly-challenging imaging workloads. With this evolution comes the need to render detailed visuals, often beyond those which a desktop system’s processing power can handle. Big data management and robust HPC systems present an additional opportunity for scientific breakthroughs. By furthering MegaMol’s existing GPU-centric framework with OSPRay’s ability to tap the speed and built-in capabilities of the latest Intel Xeon CPUs, increasingly-complex visualization middleware will drive science forward.

Jim Jeffers, senior director and senior principal engineer of SDVis engineering at Intel put this in context, “GPUs are very good rendering four to eight-gigabyte rasterized workloads and provide value for smaller data sets needing moderate fidelity on powerful workstations. However, many of today’s scientific endeavors using HPC, like molecular dynamics, fall into the Big Data category, requiring the handling of terabytes of information that require high fidelity rendering.” OSPRay addresses this need for grander scale.

More than meets the eye

In everyday life, many of us experience rendering technologies at work when watching films. Science fiction movies combine actual people with photorealistic computer rendered fantasy creatures using ray traced rendering that inherently models lighting. Biochemists require visualization to meet many different requirements when rendering objects on a molecular scale. Ray traced global lighting effects like ambient occlusion and shadowing can prove helpful when trying to understand the spatial structures in molecular dynamics simulations. However, in biochemistry “realistic” object rendering is not the priority for visualization efforts, but the ability to interact with the data is crucial. Especially in large-scale simulations, the sheer amount of detail which hundreds of millions of particles offer can overwhelm the human observer. Instead, scientists depend on visualization to make their datasets more comprehensible by offering greater clarity, simplicity, and abstraction. Doing that requires novel visualization metaphors and massive scalability from an HPC system and the software running on it.

Stuttgart’s recent efforts seek to leverage both MegaMol and OSPRay visualization frameworks to combine their prowess. MegaMol’s legacy design leverages a GPU (Graphical Processing Unit) for its interactive, cross-platform visualization framework. While innovative in many ways, MegaMol faces some limitations. The primary challenge resides in its reliance on GPUs. Years ago, when CPUs offered lesser compute performance, GPUs sped workloads by offloading challenging visualization tasks from constrained CPUs. However, today’s advanced CPUs are not bound by their predecessors’ limitations since they have built-in capability for ray-tracing algorithm parallelism. For this reason, OSPRay offers a complementary visualization framework for MegaMol.

Since OSPRay maximizes vectorization and ray-tracing capability ‘in-situ’ on the CPU, it can leverage the superior memory capacity, and advanced vector extensions (including Intel AVX-512) to accelerate workloads on Intel Xeon Scalable and Intel Xeon Phi processors. As a result, contemporary ray-tracing takes better advantage of many-node clusters making SDVis exascale-capable. Jeffers describes an additional benefit of Intel AVX integration on the CPU. “Visualization is not just about CPU clock rate. Intel AVX-512 is much more efficient because it enables parallel ‘multi-ray’ capability. Instead of evaluating rays one at a time, the vector processing can package multiple rays together for simultaneous processing. Through this ‘single instruction, multiple data’ capability, many efficiencies can be gained.”

MegaMol team colleague Tobias Rau has undertaken the challenging task of integrating their framework and OSPRay to reap the benefits of each’s inherent strength. OSPRay offers significant performance and scale improvements, while MegaMol remains highly effective at data integration, visual abstraction, interactive exploration, and analysis. Using MegaMol for UX, with OSPRay underlying it, the team maximizes both frameworks.

Beyond the latest hardware and rendering engines, other advancements in visualization provided by OSPRay’s use of the ever-improving Embree ray tracing kernel, available under the Apache 2.0 license, will help drive more detailed rendering.

Grand visions

In the future, the team behind MegaMol plans to forge ahead with additional optimization improvements, and other efforts to enhance the ray tracing framework. With support from Intel’s Parallel Computing Center, VISUS plans further integration of SDVis into MegaMol. The effort will enable additional scalability with more massive data sets. MegaMol’s 1.3 release to be posted on GitHub includes OSPRay support for volume and particle rendering. Additionally, OSPRay geometry for Solvent Excluded Surfaces will empower biochemists seeking a deeper understanding of proteins and other macromolecules.

With these and more improvements on the way, VISUS plans to enable visualization of experiments like a simulation of human muscle fibers. Interfacing MegaMol and simulations, plus the ability to internet-stream resulting observations, will facilitate research not possible just a few years ago. By advancing and integrating visualization frameworks, efforts among the MegaMol team and many others around the world will undoubtedly make a substantial impact in life sciences.

About the Author

Rob Johnson spent much of his professional career consulting for a Fortune 25 technology company. Currently, Rob owns Fine Tuning, LLC, a strategic marketing and communications consulting company based in Portland, Oregon. As a technology, audio, and gadget enthusiast his entire life, Rob also writes for TONEAudio Magazine, reviewing high-end home audio equipment.

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