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AI-Driven Virus Variant Tracking: A Case Study of Argonne National Laboratory
技术
- 分析与建模 - 机器学习
- 基础设施即服务 (IaaS) - 云计算
适用行业
- 教育
- 设备与机械
适用功能
- 产品研发
用例
- 预测性维护
- 虚拟培训
服务
- 数据科学服务
- 培训
挑战
美国能源部多学科科学与工程研究中心阿贡国家实验室面临着在 COVID-19 大流行期间追踪快速演变的 SARS-CoV-2 病毒及其变种的挑战。病毒的快速进化有时变得更加致命且更具传播性，因此需要快速识别相关变体 (VOC)。挥发性有机化合物的早期发现对于拯救生命至关重要，因为它可以为科学家提供开发有效疫苗和治疗方法的时间。然而，现有的追踪这些变异的方法速度缓慢且效率低下，给研究团队带来了重大挑战。
关于客户
阿贡国家实验室是美国能源部下属的多学科科学与工程研究中心。该实验室是才华横溢的研究人员的所在地，他们合作回答人类一些最大的问题。 Aurora 百亿亿次计算机计划于 2023 年在阿贡领导计算设施 (ALCF) 运行，将支持尖端的机器学习和数据科学工作负载以及更传统的建模和模拟。在 Aurora 达到百亿亿次级之前，阿贡的北极星系统已经在促进各种科学研究项目的进步。
解决方案
为了应对追踪病毒变体的挑战，阿贡国家实验室的一组研究人员与大学和行业合作伙伴合作，利用了人工智能 (AI)。他们利用了 ALCF 的 Polaris 超级计算机、Cerebras 的 AI 硬件加速器和 NVIDIA 的 GPU 加速的 Selene 系统的强大功能。 Polaris 配备了 GPU 和 Altair® PBS Professional® 的工作负载编排，能够处理大型、复杂的工作负载，包括该项目使用的一年的基因组数据。该团队为该任务训练了大型语言模型 (LLM)，其影响超出了 COVID-19 范围。他们开发了第一个基因组规模语言模型 (GenSLM)，简化了分析 150 万个完整、高质量 SARS-CoV-2 基因组序列的过程。
运营影响
数量效益
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