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

收购德克萨斯大学格兰德河谷（UTRGV）的高性能格兰德河谷（HiRGV）计算集群将支持新颖和正在进行的多学科研究，并使用异构计算技术为教师和学生提供培训。HiRGV将有一个混合的处理器，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，但它们现在是许多需要大规模并行化以实现高性能的应用程序（如人工智能）的驱动力。HiRGV支持的项目将在与国家利益高度相关的多个领域推进科学知识：超大规模集成芯片的设计和开发将变得更快;在引力波搜索中将发现更弱的信号，美国目前是领先的国家;使用新材料将提高燃料电池效率;癌症治疗将得到推进;将为社区提供一种新的密码学工具和服务;将开发深度强化学习算法来解决具有挑战性的决策问题;将允许养蜂人通过人工智能系统监测蜂巢健康;将发现2型糖尿病的分子机制。 所有上述工作将在该国第二大西班牙裔服务机构进行。来自代表性不足的群体的本科生和研究生将接受高水平计算的实践培训，并获得21世纪世纪劳动力高度需求的技能。HiRGV支持的项目的具体目标如下。1)大规模稀疏矩阵求解的并行计算：最小化左上方向的填充和非零项将改善稀疏矩阵求解的SOLVE阶段的执行时间; 2）快速全相干全天空（FCAS）搜索引力波（GW）信号;从现在的偶发事件到永远的-在FCAS上，从二元螺旋线搜索GW将导致检测灵敏度和参数估计精度的重大改善; 3）催化层原子分子量子理论（QTAIM）与CO吸附唯象模型的结合，将解决目前小分子吸附和燃料电池技术研究方法的不足; 4）通过分子动力学模拟研究GRP 119受体配体识别和激活的分子机制，阐明了GRP 119和同一家族的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）使用高性能GPU集群进行癌症的分子靶标识别和药物发现;发现的生物标志物和靶蛋白可以应用于给定靶癌症服务的药物设计;以及6）将计算机视觉技术（CVT）应用于蜜蜂健康和监测; CVT记录将提供如何分析蜜蜂运动、害虫的存在或带入蜂巢的花粉的数量和类型; 7）开发一种新的密码工具，该工具使用GPU以低计算成本提供各种期望的安全特征; 8）开发深度强化学习算法，提供反应式的人类决策，高层次的审议和解释能力，以及任务之间的有效知识转移。除上述项目外，HiRGV还将在代码开发、测试和性能评估方面支持6个项目，使它们能够在德克萨斯州高级计算中心（TACC）的资源上运行。该奖项反映了NSF的法定使命，并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，被认为值得支持。

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.