MRI: Acquisition of a GPU-Accelerated Cluster, High Performance Rio Grande Valley Cluster (HiRGV)

MRI：获取 GPU 加速集群，高性能 Rio Grande Valley 集群 (HiRGV)

• 批准号: 2018900
• 负责人: Nikolaos Dimakis
• 金额: $ 69.91万
• 依托单位: The University of Texas Rio Grande Valley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2018900&HistoricalAwards=false
• 关键词: MRI; Acquisition; GPU; Accelerated; Cluster

The acquisition of the High-Performance Rio Grande Valley (HiRGV) computing cluster at the University of Texas Rio Grande Valley (UTRGV) will support novel and ongoing multidisciplinary research and provide faculty and student training using heterogeneous computing technologies. HiRGV will have a mix of processors, such as multi-core and Graphics Processing Units (GPUs). While GPUs were initially developed to satisfy the extreme computing needs of dynamic visualization in video games, they are now the driving force in many applications, such as artificial intelligence, that need massive parallelization to achieve high performance. The projects supported by HiRGV will advance scientific knowledge in multiple areas that are all highly relevant to national interests: Design and development of Very Large Scale Integrated chips will become faster; Weaker signals will be found in gravitational wave searches, where the U.S. is the leading nation at present; Fuel cell efficiency will be improved using novel materials; Cancer therapy will be advanced; A novel cryptography tool and service will be provided to the community; Deep reinforcement learning algorithms will be developed to resolve challenging decision making problems; Beekeepers will be allowed to monitor beehive health by AI systems; Molecular mechanism will be discovered for type 2 diabetes. All of the above work will be carried out at the second largest Hispanic-Serving Institution in the country. Undergraduate and graduate students from a predominantly under-represented group will receive hands-on training in high level computing and acquire skills that are in high demand for the 21st century workforce.The specific goals of the projects supported by HiRGV are as follows. 1) Parallel computation for large-scale sparse matrix solution; Minimized fill-ins and non-zero entries to the top-left directions will improve execution time for SOLVE phase of sparse matrix solution; 2) Fast fully-coherent all-sky (FCAS) search for gravitational wave (GW) signals; Transitioning from the current episodic to an always-on FCAS search for GWs from binary inspirals will result in a major improvement in detection sensitivity and parameter estimation accuracy; 3) Quantum Theory of Atoms and Molecules (QTAIM) on catalytic layers correlated with CO adsorption phenomenological models will solve the shortcoming of current approaches in small molecule adsorption and fuel cell technology; 4) Studying the Molecular Mechanism of GRP119 Receptor Ligand Recognition and Activation Through Molecular Dynamics Simulations; Elucidated ligand recognition and activation of the GPR119 and other lipid binding receptors of the same family will be used for treating type 2 diabetes and other diseases; 5) Molecular target identification and drug discovery for cancer using high performance GPU cluster; Discovered biomarkers and target proteins can be applied to a drug design given a target cancer service; and 6) Applying computer vision technologies (CVT) to honey bee health and surveillance; CVT recording will provide how to analyze honeybee movements, presence of pests, or the amount and type of pollen brought into a hive; 7) Developing a novel cryptography tool that offers various desirable security features with low computational cost using GPUs; 8) Developing deep reinforcement learning algorithms that provide reactive human-like decision-making, high-level deliberative and explanatory capabilities, and efficient transfer of knowledge between tasks. In addition to the above projects, the HiRGV will support six projects in terms of code development, testing, and performance assessment, enabling them to run on Texas Advanced Computing Center (TACC) resources.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.

此次收购位于德克萨斯大学大河谷分校（UTRGV）的高性能大河谷（HiRGV）计算集群，将支持新的和正在进行的多学科研究，并使用异构计算技术为教职员工和学生提供培训。HiRGV将拥有多种处理器，如多核和图形处理单元（gpu）。虽然gpu最初是为了满足视频游戏中动态可视化的极端计算需求而开发的，但它们现在是许多应用程序（如人工智能）的推动力，这些应用程序需要大规模并行化才能实现高性能。HiRGV支持的项目将推动与国家利益高度相关的多个领域的科学知识：超大规模集成芯片的设计和开发将加快；在引力波搜索中会发现较弱的信号，目前美国在这方面处于领先地位；使用新型材料将提高燃料电池的效率；癌症治疗将会更先进；为社区提供新颖的密码工具和服务；将开发深度强化学习算法来解决具有挑战性的决策问题；养蜂人将被允许通过人工智能系统监控蜂巢的健康状况；将发现2型糖尿病的分子机制。所有上述工作都将在该国第二大拉美裔服务机构进行。来自弱势群体的本科生和研究生将接受高水平计算方面的实践培训，并获得21世纪劳动力高需求的技能。HiRGV支持的项目具体目标如下：1)大规模稀疏矩阵解的并行计算；最小化左上方向的填充项和非零项将提高稀疏矩阵求解的SOLVE阶段的执行时间；2)快速全相干全天（FCAS）搜索引力波（GW）信号；从目前的偶发搜索过渡到始终在线的FCAS搜索，将大大提高探测灵敏度和参数估计精度；3)与CO吸附现象学模型相关的催化层原子分子量子理论（QTAIM）将解决当前小分子吸附和燃料电池技术方法的不足；4)通过分子动力学模拟研究GRP119受体配体识别和激活的分子机制；阐明GPR119和其他脂质结合受体的配体识别和激活将用于治疗2型糖尿病和其他疾病；5)基于高性能GPU集群的癌症分子靶点识别与药物发现；发现的生物标志物和靶蛋白可以应用于给定目标癌症服务的药物设计；6)应用计算机视觉技术（CVT）进行蜜蜂健康监测；CVT记录将提供如何分析蜜蜂的运动，害虫的存在，或花粉的数量和类型带入蜂箱；7)开发一种新颖的加密工具，使用gpu提供各种理想的安全功能和低计算成本；8)开发深度强化学习算法，提供类似人类的反应性决策，高水平的审议和解释能力，以及任务之间有效的知识转移。除了上述项目外，HiRGV将在代码开发、测试和性能评估方面支持六个项目，使它们能够在德克萨斯高级计算中心（TACC）资源上运行。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Classification of time series as images using deep convolutional neural networks: application to glitches in gravitational wave data

使用深度卷积神经网络将时间序列分类为图像：应用于引力波数据中的故障

• 发表时间: 2023
• 期刊: 5th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2023
• 作者: Jin, Shuzu;Mohanty, Soumya;Xie, Qunying;Wang, Hanzhi;Zhang, Xue-Hao
• 通讯作者: Zhang, Xue-Hao

Optical properties and simulated x-ray near edge spectra for Y2O2S and Er doped Y2O2S

• DOI: 10.1016/j.mtcomm.2022.104328
• 发表时间: 2022-09
• 期刊: Materials Today Communications
• 影响因子: 3.8
• 作者: N. Dimakis;E. Rodriguez;Kofi Nketia Ackaah-Gyasi;M. Pokhrel

South Texas coastal area storm surge model development and improvement

德克萨斯州南部沿海地区风暴潮模型的开发和改进

• DOI: 10.3934/geosci.2020016
• 发表时间: 2020
• 期刊: AIMS Geosciences
• 影响因子: 1.3
• 作者: E. Davila, Sara;Davila Hernandez, Cesar;Flores, Martin;Ho, Jungseok
• 通讯作者: Ho, Jungseok

Extending the Frequency Reach of Pulsar Timing Array-based Gravitational Wave Search without High-cadence Observations

• DOI: 10.3847/2041-8213/abd9bd
• 发表时间: 2020-12
• 期刊: The Astrophysical Journal Letters
• 作者: Yan Wang;S. Mohanty;Zhoujian Cao

Molybdenum disulfide monolayer electronic structure information as explored using density functional theory and quantum theory of atoms in molecules

• DOI: 10.1016/j.apsusc.2021.149545
• 发表时间: 2021-07
• 期刊: Applied Surface Science
• 影响因子: 6.7
• 作者: N. Dimakis;Om Vadodaria;Korinna Ruiz;Sanju Gupta