A GPU Server for Integration of Machine Learning in Mathematics and Statistics Research and Training

我们正在申请GPU服务器的资金，以提供关键的计算能力，以支持卡尔加里大学数学和统计系当前和未来的数据科学研究和培训。机器学习（ML）在学术研究和工业应用中的突破，从根本上改变了学术研究的最新发展水平，以及普通公民的生活。ML的集成已经成为许多领域的趋势。此外，还有越来越多的学员（在学术本科生和研究生以及专业继续学习者中）热衷于在我们的研究实验室中理解ML技术。研究人员满足这一需求的重要杠杆包括现代ML库，如TensorFlow和PyTorch，它们允许简单而迅速地利用现有的ML工具。为了迎接这一令人兴奋的、持续的面向数据的范式转变，数学与统计系成立了一个数据科学小组，用于ML研究和培训。它专注于ML开发，以适应生物统计学，数学金融学，高维统计建模，神经网络表征和地球科学的现有优势。申请人是一个由代表这个数据科学小组的十一（11）名研究人员组成的NSERC资助的多元化团队，他们正在将ML整合到他们既定的研究计划中，支持来自不同背景的学员，为他们提供经验和与他们的职业目标相一致的知识库。实施这一数据科学计划的影响的一个障碍是缺乏适合现代ML技术的适当计算基础设施。因此，我们正在请求获得支持，以获得具有当前主流规格的GPU服务器，这将允许快速部署和执行ML模型。该设备将向数学/统计部门的所有研究人员和学员开放，为他们提供构建和实施ML模型的实践经验。此外，对于非ML计算任务，GPU服务器还将通过用户友好的界面提供大规模并行计算能力，使研究人员不必编写代码来手动协调多线程任务（例如，使用OpenMP或MPI）。卡尔加里大学的IT团队将提供长期的存储、网络、维护和用户培训。总之，通过支持ML和非ML并行计算，该基础设施将有利于部门的研究和HQP培训，为我们的研究生和本科生课程的学员提供更广泛和更好的就业机会。在使用这些许褚时，我们会实施可行和可量度的程序，以促进部门内的电子数据联通程度。

We are requesting funds for a GPU server to provide critical computing power to support current and future research and training in Data Science for the Department of Mathematics and Statistics at the University of Calgary. The breakthrough of Machine Learning (ML) into both academic research, and industrial applications, has fundamentally changed the state-of-the-art of academic research, as well as ordinary citizens' lives. The integration of ML has become a trend in many fields. Moreover, there is also a growing population of trainees (among academic undergraduate and graduate students and professional continuing learners) who are enthusiastic to comprehend ML techniques in our research laboratories. Significant leverages for researchers to meet this need include modern ML libraries such as TensorFlow and PyTorch, which allow for the simple and prompt utilization of established ML tools. To embrace this exciting and ongoing data-oriented paradigm shift, the Department of Mathematics and Statistics has formed a Data Science group for ML research and training. It focuses on ML development tailoring to existing strengths in Biostatistics, Mathematical finance, High-dimensional statistical modeling, Neural network characterization, and Earth science. The applicants, an NSERC-funded diverse team of eleven (11) researchers representing this Data Science group, are integrating ML into their established research programs, supporting trainees from various backgrounds to give them experience and a knowledgebase aligning with their career goals. A roadblock to enacting the impact of this Data Science initiative is the lack of appropriate computational infrastructure tailoring to modern ML techniques. As such, we are requesting the support to acquire a GPU server with current mainstream specifications, which will allow for prompt deployment and execution of ML models. This equipment will be open to all researchers and trainees in the Math/Stats Department, providing them hands-on experiences in building and implementing ML models. Additionally, for non-ML computational tasks, the GPU server will also provide massive parallel computing power via a user-friendly interface, relieving researchers from writing code to manually coordinate multi-threads tasks (e.g., using OpenMP or MPI). The proposal is fully supported by the University of Calgary IT team, who will provide long-term storage, networking, maintenance, and user training. In summary, by supporting ML-focused and non-ML parallel computations, this infrastructure will benefit the research and HQP training in the Department, providing broader and better job opportunities for trainees in our graduate and undergraduate programs. When utilizing the equipment, actionable and measurable procedures will be implemented to facilitate a high degree of EDI in the department.