HPC for Quantitative and Computational Neuroscience

描述(由申请人提供)：该提案要求为普林斯顿神经科学研究所(PNI)提供一个新的高性能计算(HPC)系统。这既是为了满足不断增长的用户基础，也是为了满足前沿神经科学研究在数据分析和存储需求方面的快速增长。PNI的使命是支持对认知、注意力、记忆、学习、决策和认知控制等心理功能潜在的神经机制的多学科研究。PNI非常强调发展形式上严格的理论，以及分析神经科学数据的数量复杂的方法。他们的工作使用了最先进的方法来记录所有分析水平的神经活动，从非脑区神经元的单个和多个单元记录 人类物种对人类全脑功能磁共振成像和脑电的研究。这些方法产生的数据集的大小在过去十年里呈爆炸性增长，分析它们所需的方法在计算上变得越来越苛刻。与此同时，PNI的用户群已经增长，并将在未来几年继续大幅增长，从2005年成立时的15人增加到目前的21人，随着它占据 目前在建的新大楼(将于2013年投入使用)。这些因素共同给现有的PNI计算设施带来了严重的压力，这些设施的局限性现在制约着其调查人员的研究努力。为了满足这些需求，本提案要求支持一个新的HPC系统，该系统由一个52节点群集(具有832个核心)和一个540 TB存储系统组成。这一新系统将安置在普林斯顿大学新建的高性能计算研究中心(HPCRC)，与普林斯顿计算科学与工程研究所(PICSciE)共同管理，并通过高性能GPFS存储网格与HPCRC的其他强大计算系统连接。这将使PNI调查人员能够利用HPCRC提供的可观的额外CPU能力，透明地访问他们的数据，以及PICScience E工作人员在并行计算方面的专业知识。PNI支持的工作旨在加深我们对在广泛的临床条件下受到干扰的精神功能的神经机制的理解，这些疾病包括抑郁症、焦虑症、精神分裂症、神经损伤、衰老和药物成瘾。在这项工作中，PNI调查人员得到了许多NIH研究所的资助，包括NIMH、NINDS、NIDA、NIA和NEI。这项工作的进展有望导致诊断、治疗和最终治愈精神疾病的更复杂和更有效的方法。目前的提案将支持PNI调查人员的研究努力，为他们提供必要的技术基础设施，以进行更复杂的神经科学研究，对他们产生的数据进行更详细的分析，并构建用于解释这些发现的更现实的神经功能模型。

DESCRIPTION (provided by applicant): This proposal requests a new high-performance computing (HPC) system for the Princeton Neuroscience Institute (PNI). This is needed to meet both the growing user base and the rapid growth in data analysis and storage demands of leading edge neuroscientific research. The PNI's mission is to support multidisciplinary research on the neural mechanisms underlying mental functions such as perception, attention, memory, learning, decision making, and cognitive control. PNI places a strong emphasis on the development of formally rigorous theory, and quantitatively sophisticated approaches to the analysis of neuroscientific data. Their work makes use of state-of-the-art methods for recording neural activity at all levels of analysis, from single- and multi-unit recordings of neurons in non human species to whole brain fMRI and EEG studies in humans. The size of the data sets generated by these methods has grown explosively over the last decade, and the methods needed to analyze them have become increasingly computationally demanding. At the same time, the PNI user base has grown and will continue to grow substantially over the next several years, from 15 at its inception in 2005, to 21 at present, and to an expected 26 as it occupies its new building presently under construction (and due for occupancy in 2013). These factors have conspired to place severe strains on existing PNI computing facilities, the limits of which are now constraining the research efforts of its investigators. To meet these needs, this proposal requests support for a new HPC system comprised of a 52 node cluster (with 832 cores) and a 540 TB storage system. This new system will be housed in Princeton University's newly constructed High Performance Computing Research Center (HPCRC), co-administered with the Princeton Institute for Computational Science and Engineering (PICSciE), and linked to other powerful computing systems at the HPCRC by way of a high performance GPFS storage grid. This will allow PNI investigators to leverage the availability of considerable additional CPU power at the HPCRC with transparent access to their data, as well as the expertise of PICSciE staff in parallel computing. Work supported by the PNI aims to deepen our understanding of the neural mechanisms underlying mental functions that are disturbed in a wide range of clinical conditions, including depression, anxiety disorders, schizophrenia, neurological impairments, aging, and drug addiction. PNI investigators are supported, in this work, by grants from a number of NIH institutes, including NIMH, NINDS, NIDA, NIA and NEI. Progress in this work promises to lead to more sophisticated and more effective approaches to the diagnosis, treatment and ultimately cure of mental disease. The current proposal will support the research efforts of PNI investigators by providing them with the technological infrastructure necessary to pursue more sophisticated neuroscientific studies, to conduct more detailed analyses of the data they generate, and to construct more realistic models of neural function used to interpret these findings.