MRI: Acquisition of Cutting-Edge GPU and MPI Nodes for the Interdisciplinary Pitt Center for Research Computing

计算科学与工程跨越许多学科的研究和教育，需要最先进的网络基础设施资源来解决大问题，并在数据支持的科学与工程中实现创新战略。该项目将大大扩展匹兹堡大学跨学科的研究计算中心（CRC），这是匹兹堡大学科学计算和研究的核心设施。CRC支持整个大学59个部门的800多名用户的工作。新的扩展硬件将推进本科和研究生课程以及类似广泛的部门和课程的教育经验。这些扩大的资源将使皮特扩大各级研究和教育之间的协同作用，超越大学的教师，工作人员和学生在霍华德大学，其他历史上的黑人学院和大学（HBCU），以及许多本科教师和学生都在区域和全国范围内。新的资源还将扩大科学计算到匹兹堡公立学校区的学生，包括附近的匹兹堡科学技术学院和匹兹堡公立Allderdice，这两所城市学校拥有不同的学生群体。资助的资源将包括16个最先进的图形处理单元（GPU）计算节点，包括NVIDIA Ampere A100 GPU加速器。每个GPU节点将比上一代GPU快2倍，在科学计算软件上比当前CPU节点快14- 50倍，并将提高机器学习的生产力。另外36个最先进的MPI节点包含AMD“米兰”内核和高系统内存，将实现复杂的计算模拟。新资源的可用性将极大地扩展GPU和消息传递接口（MPI）计算的访问和机会，为从机器学习和大数据到量子化学，蛋白质分子动力学，能量转换，纳米粒子催化设计，天气/风预报，天文数据分析，原子隧道电子显微镜测量和计算机视觉。除了简单的加速之外，这些资源还将使变革性研究能够实现更准确的天气网格，用于分子和材料能量和属性的量子化学计算的新机器学习替代品，蛋白质折叠和结合的稀有事件采样，功能磁共振成像神经科学和下一代数字天文学。这些资源将立即使30多个NSF资助的研究小组受益，利用超过1800万美元的研究和培训赠款。这些资源将支持所有领域的研究，包括化学，计算生物学，化学工程，材料科学，心理学，天体物理学，天气预报，计算机科学，以及专注于能源，可持续性和其他关键科学和工程领域的研究中心。与新资源相关的研讨会和课程将侧重于调整现有软件，并为MPI和GPU计算开发新软件，包括通过这些扩展资源的数字处理转换实现的各种机器学习方法。这些资源将与霍华德大学、其他HBCU以及地区和国家本科学校的合作者共享，以扩大对计算科学的参与。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Computational science and engineering spans research and education across many disciplines, and state-of-the-art cyberinfrastructure resources are needed to tackle large problems and enable innovative strategies in data-enabled science and engineering. This project will greatly expand the interdisciplinary University of Pittsburgh Center for Research Computing (CRC), the core facility for scientific computing and research at Pitt. CRC supports the work of over 800 users in 59 departments across the entire university. The new expanded hardware will advance both undergraduate and graduate courses and educational experience across a similarly broad range of departments and courses. These expanded resources will enable Pitt to expand a synergy between research and education at all levels, reaching beyond the university to faculty, staff, and students at Howard University, other historically black colleges and universities (HBCUs), and many undergraduate faculty and students both regionally and nationwide. The new resources will also expand scientific computing to students in the Pittsburgh Public School district, including nearby Pittsburgh Science and Technology Academy and Pittsburgh Public Allderdice, both urban schools with diverse student populations.The funded resources will consist of 16 state-of-the-art graphics processing unit (GPU) computing nodes including NVIDIA Ampere A100 GPU accelerators. Each GPU node will be ~2x faster than previous generation GPUs and 14-50x faster on scientific computing software than current CPU nodes, and will enable increased machine learning productivity. An additional 36 state-of-the-art MPI nodes containing AMD “Milan” cores and high system memory will enable complex computational simulations. The availability of the new resource will dramatically expand the access and opportunity to GPU and message passing interface (MPI) computing, offering significant speed improvements for an immense range of scientific computing, from machine learning and big data, to quantum chemistry, protein molecular dynamics, energy conversion, nanoparticle catalysis design, weather/wind forecasting, astronomical data analysis, atomistic tunneling electron microscopy measurements, and computer vision. Beyond simple acceleration, the resources will enable transformative research with vastly more accurate weather grids, new machine learning surrogates for quantum chemical calculations of molecular and materials energies and properties, rare-event sampling in protein folding and binding, fMRI neuroscience, and next generation digital astronomy. The resources will immediately benefit over 30 NSF-funded research groups, leveraging over $18 million in research and training grants. The resources will support research in all areas including Chemistry, Computational Biology, Chemical Engineering, Materials Science, Psychology, Astrophysics, Weather Forecasting, Computer Science, and research centers focusing on energy, sustainability, and other key areas of science and engineering. Workshops and courses associated with the new resources will focus on adapting existing software and developing new software for MPI and GPU-computing including a wide range of machine learning methods enabled by the transformation in numeric processing with these expanded resources. These resources will be shared with collaborators at Howard University, other HBCUs, and with regional and national undergraduate schools to broaden participation in computational science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.