Dedicated high-powered workstation for machine and deep learning in genomics and industry

纽芬兰纪念大学数学和统计系正在大力加强其在数据科学方面的研究工作。最近成立了由12名成员组成的数据科学研究小组(https://www.mun.ca/math/dsci)，)，并提出了一年制数据科学硕士学位的建议，这只是我们最近为让大西洋加拿大的数据科学声名鹊起而做出的最新集体努力中的两项。目前的基础设施支助申请是这方面的一项承诺。申请的基础设施将主要支持NSERC资助的共同申请者的研究计划，但也将支持数据科学研究小组其他10名成员的工作。该基础设施将实现HQP的重要协作和共同监督，并将使我们能够在这个日益重要的领域为HQP提供高度市场化的技能方面的最先进培训。*我们目前部署深度学习或预处理数据以用于机器学习和生物信息学的需求需要一个强大的专用工作站，以允许并行化、测试、调整和验证。普通机器根本不能容忍这样的处理和内存要求。我们已经尝试使用Compute Canada资源，但是由于排队和等待列表，该资源不够用，特别是在原型阶段，我们必须更改和调整参数。我们可以通过纪念大学遗传学系(www.med.mun.ca/chia)访问IBM LSF 10.1.0.6机器和集群Torngat，该集群由数值分析和科学计算中心的成员共享。遗憾的是，由于内存的限制和图形处理器能力的限制，这两台机器都不适合拟议的研究项目。*我们追求的问题涉及巨大的、高度机密的数据集，这些数据集无法用目前MUN现有的基础设施进行有效分析。为了使用真实数据集构建和测试准确的模型(用于疾病检测、天气预报和异常检测)；在数据科学领域保持竞争力；在顶级期刊或会议上发表文章；吸引最好的HQP；并为HQP提供最先进的培训，同时保护数据的完整性，所需设备的计算能力至关重要。

***The Department of Mathematics and Statistics at Memorial University of Newfoundland is in the process of significantly strengthening its research endeavours in Data Science. The recent formation of the 12 member Data Science Research Group (https://www.mun.ca/math/dsci), as well as the proposal of a one year MSc in Data Science are just two of our most recent collective efforts to put data science in Atlantic Canada on the map. The present application for infrastructure support is one undertaking in this regard. The requested infrastructure will primarily support the NSERC-funded programs of research of the co-applicants, but will also support the work of the other 10 members of the Data Science Research Group. The infrastructure will enable significant collaboration, co-supervision of HQP, and will allow us to provide state-of-the-art training in highly marketable skills to HQP in this increasingly vital field.*********Our current needs to deploy deep learning or preprocess data to use in machine learning and bioinformatics require a powerful dedicated workstation to allow parallelization, testing, tune-up, and verification. Ordinary machines are simply not capable of tolerating such processing and memory requirements. We have tried to use Compute Canada resources, however due to queuing and wait listing, this resource is not adequate particularly at the prototyping stage where we have to change and tune parameters. We have access to the IBM LSF 10.1.0.6 machine through Memorial University's department of Genetics (www.med.mun.ca/CHIA) and to the cluster Torngat, which is shared among the members of the Centre for Numerical Analysis and Scientific Computing. Unfortunately, both machines are not suitable for the proposed research projects due to RAM constraints and limitations in terms of their GPU capabilities.******The problems we pursue involve enormous, and highly confidential datasets that cannot be efficiently analysed with the infrastructure currently available at MUN. In order to use real datasets to build and test accurate models (for disease detection, weather prediction, and anomaly detection); remain competitive in the data science field; publish in top-tier journals or conferences; attract the best HQP; and to provide HQP with state-of-the-art training, while protecting the integrity of the data, the computational power of the requested equipment is vital.