Altair Case Studies AI-Driven Virus Variant Tracking: A Case Study of Argonne National Laboratory

	AI-Driven Virus Variant Tracking: A Case Study of Argonne National Laboratory Altair

AI-Driven Virus Variant Tracking: A Case Study of Argonne National Laboratory

Altair

Technology Category	Analytics & Modeling - Machine Learning Infrastructure as a Service (IaaS) - Cloud Computing
Applicable Industries	Education Equipment & Machinery
Applicable Functions	Product Research & Development
Use Cases	Predictive Maintenance Virtual Training
Services	Data Science Services Training
Challenge	Argonne National Laboratory, a U.S. Department of Energy multidisciplinary science and engineering research center, was faced with the challenge of tracking the rapidly evolving SARS-CoV-2 virus and its variants during the COVID-19 pandemic. The rapid evolution of the virus, sometimes becoming deadlier and more transmissible, necessitated the quick identification of variants of concern (VOCs). The early discovery of VOCs is crucial in saving lives by providing scientists with the time to develop effective vaccines and treatments. However, the existing methods of tracking these variants were slow and inefficient, posing a significant challenge to the research team. Read More
About Customer	Argonne National Laboratory is a multidisciplinary science and engineering research center under the U.S. Department of Energy. The laboratory is home to talented researchers who collaborate to answer some of humanity's biggest questions. The Aurora exascale computer, which is scheduled to be operational at the Argonne Leadership Computing Facility (ALCF) in 2023, will support cutting-edge machine learning and data science workloads alongside more traditional modeling and simulation. In the lead-up to exascale with Aurora, Argonne’s Polaris system is already facilitating advances in various scientific and research projects. Read More
Solution	To tackle the challenge of tracking virus variants, a team of researchers at Argonne National Laboratory, in collaboration with university and industry partners, utilized artificial intelligence (AI). They leveraged the power of the ALCF’s Polaris supercomputer, Cerebras’ AI-hardware accelerator, and NVIDIA’s GPU-accelerated Selene system. Polaris, equipped with GPUs and workload orchestration by Altair® PBS Professional®, was able to handle large, complex workloads, including a year’s worth of genome data used for the project. The team trained large language models (LLMs) for the task, which has implications beyond COVID-19. They developed the first genome-scale language model (GenSLM), which streamlined the process of analyzing 1.5 million complete, high-quality SARS-CoV-2 genome sequences. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Infrastructure as a Service (IaaS) - Cloud Computing

Applicable Industries

Education

Equipment & Machinery

Applicable Functions

Product Research & Development

Use Cases

Predictive Maintenance

Virtual Training

Services

Data Science Services

Training

Challenge

Argonne National Laboratory, a U.S. Department of Energy multidisciplinary science and engineering research center, was faced with the challenge of tracking the rapidly evolving SARS-CoV-2 virus and its variants during the COVID-19 pandemic. The rapid evolution of the virus, sometimes becoming deadlier and more transmissible, necessitated the quick identification of variants of concern (VOCs). The early discovery of VOCs is crucial in saving lives by providing scientists with the time to develop effective vaccines and treatments. However, the existing methods of tracking these variants were slow and inefficient, posing a significant challenge to the research team.

About Customer

Argonne National Laboratory is a multidisciplinary science and engineering research center under the U.S. Department of Energy. The laboratory is home to talented researchers who collaborate to answer some of humanity's biggest questions. The Aurora exascale computer, which is scheduled to be operational at the Argonne Leadership Computing Facility (ALCF) in 2023, will support cutting-edge machine learning and data science workloads alongside more traditional modeling and simulation. In the lead-up to exascale with Aurora, Argonne’s Polaris system is already facilitating advances in various scientific and research projects.

Solution

To tackle the challenge of tracking virus variants, a team of researchers at Argonne National Laboratory, in collaboration with university and industry partners, utilized artificial intelligence (AI). They leveraged the power of the ALCF’s Polaris supercomputer, Cerebras’ AI-hardware accelerator, and NVIDIA’s GPU-accelerated Selene system. Polaris, equipped with GPUs and workload orchestration by Altair® PBS Professional®, was able to handle large, complex workloads, including a year’s worth of genome data used for the project. The team trained large language models (LLMs) for the task, which has implications beyond COVID-19. They developed the first genome-scale language model (GenSLM), which streamlined the process of analyzing 1.5 million complete, high-quality SARS-CoV-2 genome sequences.

Impact #1	The research conducted by the Argonne team and their collaborators has set the stage for faster, more detailed insights into the virus mutation process. This enables scientists worldwide to respond to emerging variants and develop strategies to reduce severity and slow the spread, ultimately saving lives. The team's work has been recognized at SC22 in Dallas, and their paper will be published in the International Journal of High-Performance Computing Applications (IJHPCA). The team believes that the full potential of their effort on large biological data is yet to be realized, indicating the potential for further advancements in this field.

Impact #1

The research conducted by the Argonne team and their collaborators has set the stage for faster, more detailed insights into the virus mutation process. This enables scientists worldwide to respond to emerging variants and develop strategies to reduce severity and slow the spread, ultimately saving lives. The team's work has been recognized at SC22 in Dallas, and their paper will be published in the International Journal of High-Performance Computing Applications (IJHPCA). The team believes that the full potential of their effort on large biological data is yet to be realized, indicating the potential for further advancements in this field.

Benefit #1	The team was able to analyze 1.5 million complete, high-quality SARS-CoV-2 genome sequences.
Benefit #2	The Polaris supercomputer was able to handle a year's worth of genome data for the project.
Benefit #3	The team's work won the ACM’s prestigious 2022 Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research.

Benefit #1

The team was able to analyze 1.5 million complete, high-quality SARS-CoV-2 genome sequences.

Benefit #2

The Polaris supercomputer was able to handle a year's worth of genome data for the project.

Benefit #3

The team's work won the ACM’s prestigious 2022 Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research.

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Overview

AI-Driven Virus Variant Tracking: A Case Study of Argonne National Laboratory

Operational Impact

Quantitative Benefit