Aurora supercomputer that helps scientists visualize the spread of cancer

Preparing for Exascale: Aurora supercomputer that helps scientists visualize the spread of cancer

  • As part of the Aurora Early Science Program, Amanda Randles of Duke University is leading a project that uses exascal computing power to advance cancer research.

    Photo credit: (Image by Joseph Insley, Argonne National Laboratory.)

    As part of the Aurora Early Science Program, Amanda Randles of Duke University is leading a project that uses exascal computing power to advance cancer research.

  • Argonne's Aurora exascale system was developed in collaboration with Intel and Hewlitt Packard Enterprise (HPE) and can handle extensive workloads for artificial intelligence and data analysis in addition to conventional modeling and simulation campaigns.

    Photo credit: (Image from Argonne National Laboratory.)

    Argonne’s Aurora exascale system was developed in collaboration with Intel and Hewlitt Packard Enterprise (HPE) and can handle extensive workloads for artificial intelligence and data analysis in addition to conventional modeling and simulation campaigns.

Scientists are preparing a cancer modeling study to run on Argonne’s new Aurora supercomputer before it goes online in 2022.

The U.S. Department of Energy (DOE) Argonne National Laboratory will be home to one of the country’s first exascale supercomputers when Aurora arrives 2022. To prepare codes for the architecture and scale of the system, 15th Research teams take part through the Argonne Leadership Computing Facility (ALCF), on DOE Office of Science user setup. With access to preproduction time on the supercomputer, these researchers will be among the first in the world to use an exascale machine for science.

about 60,000 Miles of blood vessels carry nutrients around our body. However, sometimes they can carry something much more dangerous – cancer.

Through a process known as metastasis, cancer cells can spread from one part of the body to another by traveling through our blood vessels. Understanding how this happens can guide the development of new drugs and treatments, but the process is still a mystery.

Amanda Randles, an assistant professor at Duke University, uses modeling and simulation to find answers. You and your team have evolved HARVEY, a model that simulates blood flow throughout the body, and now they want to use it to predict the movement of cancer cells on a microscopic level.

As we tackle our new research on the metastasis process and perform the complicated simulations required, we will need even more computing power to process the massive amounts of data in real time. The Aurora system will help us meet this need. “- Amanda Randles, Assistant Professor at Duke University

Simulating such a complex process requires an enormous amount of computing power, even more than high-performance computing (HPC) Systems can deliver today. With the introduction of Aurora, one of the first exascale systems in the country, the Randles team can gain important insights.

Randles is one of the select few researchers selected to participate in ALCFAurora Early Science Program (ESP). Your project will be one of the first to run on Aurora, which is delivered in Argonne in 2022.

As we tackle our new research on the metastasis process and perform the complicated simulations required, we will need even more computing power to process the huge amounts of data in real time. The Aurora system will help us meet that need, ”said Randles.

Once the Aurora system is up and running, it can perform astounding billions of billions of calculations per second. The Randles team will use this ability to create high resolution, more accurate representations of the geometry of the circulatory system. It will also allow them to simulate the flow of fluid through the system, as well as collections of red blood cells and tumor cells moving in the fluid flow.

Scientists can analyze data on the fly

To ensure they can get started as soon as Aurora is online, the Randles team is working with Argonne scientists to prepare their application for efficient operation on the new system. One of the challenges they have to face is the discrepancy between computing speed and the speed with which data can be saved.

As with others HPC Systems, Aurora will allow computation at lightning-fast speeds, but the speed at which data can be stored for longer periods (by being written to disk) cannot match that speed, a known gap that has only grown over the years is.

Without an address, the researchers would have to slow down their calculations by orders of magnitude in order to write all of their simulation data to the hard drive. However, this approach would take a long time and require a lot of storage space. For the Randles team and many others, this is impractical given the volume of data and the workload.

As an alternative, the HPC A great effort is being made by the community to enable data analysis in the field (ie, in computer memory) while simulations are running. By visualizing and analyzing data in this way, the computation can still be done quickly and researchers can decide which data to store or discard over the long term.

For example, if you identify a specific area of ​​interest throughout the system, you can store data in that small area with a higher frequency or level of detail. That way, you can still get more science out of the data while reducing the amount of data you actually have to write to disk, ”said Joseph Insley. ALCF Team leader for visualization and data analysis.

Through a DOE Called project SENSEI, Insley and ALCF Computer scientist Silvio Rizzi works with other researchers around the world DOE Laboratory complex and industry to create a unified interface through which scientists can access a library of frameworks that support the analysis and visualization of in-situ data. With access to early hardware and the Aurora Software Development Kit, the ESP The team tested and refined these functions before the exascale machine arrived.

We used that ALCFTheta supercomputers and early Aurora hardware to integrate this library into the HARVEY Code. By enabling visualization and analysis of the data while it is still in memory, the science team can gain more insight from the data than they would otherwise, ”said Insley.

Ultimately, the team’s research on Aurora aims to advance the development of new cancer drugs and treatments that have the potential to save lives in the years to come.

By understanding the biological mechanisms behind metastatic cancer cells, we also hope for our work with HARVEY will eventually help doctors and their patients fight cancer, ”concluded Randles.

Randles’ ESP Project,Extreme in-situ visualization and analysis of fluid-structure interaction simulations ”, supported by DOEAdvanced Scientific Computing Research Program.

The Argonne Leadership Computing Facility provides supercomputing capabilities to the scientific and engineering community to advance fundamental discovery and understanding across a wide range of disciplines. Supported by the US Department of Energy (DOE‘s) Office of Science, Advanced Scientific Computer Research (ASCR) Program that ALCF is one of two DOE Leadership Computing Institutions in the Nation Devoted to Open Science.

Argonne National Laboratory seeks solutions to pressing national problems in science and technology. Argonne is the country’s first national laboratory and conducts cutting-edge and applied scientific research in almost all scientific disciplines. Argonne Researchers work closely with researchers from hundreds of corporations, universities, and federal, state and local authorities to help them solve their specific problems, advance America’s scientific leadership, and prepare the nation for a brighter future. With employees of more than 60 Nations, Argonne is administered by UChicago Argonne, GMBH for the US Department of Energy’s Office of Science.

The Department of Energy at the Department of Energy is the leading proponent of basic science in the United States, working to address some of the most pressing challenges of our time. Further information can be found at https: // ener gy .gov / science.

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