High Memory High-Performance Computer Cluster for Biomedical Research

项目摘要 贝勒医学院（Baylor College of Medicine）的生物医学研究人员越来越依赖于高绩效 基于计算机集群（HPC）的序列和其他高维数据的基本和综合分析， 进行他们的研究。生物统计学和信息学共享资源（BISR）， 学院的先进技术核心，目前管理着一个贝奥武夫风格的集群作为一个服务， 计算机研究者此群集的使用率很高，但正在老化，并且不具有高内存类型 节点需要有效及时处理单细胞和单核测序实验， 每个处理器通常需要100-200 GB的内存。在某些情况下，分析根本无法运行。 尽管英特尔还有其他HPC功能，例如在人类基因组测序中心或 在各个实验室以及该地区的其他HPC资源中，没有一个能够提供令人满意的解决方案， 我们的用户内部基于BCM的系统不是为需要高内存的作业而设计的。没有一个是开放的 一般用户，没有一个是作为共享资源运行的，以确保一致的正常运行时间、高速 网络连接、可挂载存储和符合法规的数据保护。外部资源 只是不提供给所有者机构以外的一般用户，或者它们是明确设计用于某些 作业类型，并对使用进行限制，以防止其用于我们的用户所需的运行类型。的 新的BISR HPC将填补一个独特的利基，提供高内存HPC功能，作为一个正式的管理 共享资源，以吸引生物医学研究人员。此外，我们不只是提供原始CPU时间， 不需要任何帮助的计算专家用户。我们为调查人员提供协助， 通过提供中央软件管理和故障排除，进行干湿实验室研究。全部潜能 最近收购了支持S10的超高通量NovaSeq 6000测序仪和最近获得CPRIT资助的 如果没有这种计算支持，单细胞测序Core可能无法实现。我们提出建设 一种新的支持GPU的高内存系统，专为支持调查人员不断增长的需求而设计 他们正在进行大型单细胞和/或单核测序实验。典型的实验 涉及100到10，000个细胞/每个生物单位和10到1000个生物单位的序列。这些 实验代表了数十万个基因组、转录组和/或表观基因组序列， 必须经过处理、调整和整合。拟议的系统将包括一个前端节点，22个计算 每个节点具有36个处理器和1 TB内存，1个GPU服务器具有8个GPU和1 PB直接连接 存储.主要用户和他们的项目将占使用量的82%左右。单电池需求 测序正在增长，我们预计将有许多额外的用户。此HPC的可用性 将对整个学院的其他高维数据为基础的研究产生积极的影响。

PROJECT SUMMARY Biomedical investigators at Baylor College of Medicine (BCM) are increasingly dependent on high performance computer cluster (HPC) based basic and integrative analysis of sequence and other high-dimensional data to conduct their research. The Biostatistics and Informatics Shared Resource (BISR), a Shared Resource in the College’s Advanced Technology Cores, currently manages a Beowulf style cluster as a service for computational investigators. This cluster is highly used but is aging and does not have the type of high-memory nodes needed for efficient timely processing of single-cell and single nucleus sequencing experiments, which typically require 100-200 GB of memory per processor. In some cases, analyses simply cannot be run. Although there are other HPC capabilities at BCM, for example in the Human Genome Sequencing Center or within individual labs as well as other HPC resources in the region, none of these offer satisfactory solutions to our users. Internal BCM-based systems are not designed for high-memory requiring jobs. None are open to general users, and none are operated as a shared resource that ensures consistent up-times, high-speed network connections, mountable storage and regulatorily compliant data protections. External resources are simply not available to general users outside of the owner institution, or they are expressly designed for certain types of jobs and place limits on usage that preclude their use for the types of runs needed by our users. The new BISR HPC will fill a unique niche in providing high-memory HPC capabilities, as a formally managed shared resource, to BCM biomedical investigators. In addition, we are not simply providing raw CPU hours to computationally expert users who do not need any help. We provide assistance to investigators that straddle wet and dry lab research by offering central software management and troubleshooting. The full potential of a recently acquired S10-supported ultra-high throughput NovaSeq6000 sequencer and a recently CPRIT-funded single-cell sequencing Core may fail to be realized without this computational support. We propose to build a new high-memory GPU-enabled system specifically designed to support the burgeoning need of investigators who are conducting large single-cell and/or single nucleus sequencing experiments. Typical experiments involve sequences from 100’s to 10,000’s of cells/per biologic unit and 10’s to 1000’s of biologic units. These experiments represent hundreds of thousands of genomic, transcriptomic and/or epigenomic sequences that must be processed, aligned and integrated. The proposed system will include a front-end node, 22 compute nodes each with 36 processors and 1 TB of memory, 1 GPU server with 8 GPU’s and 1PB direct attached storage. Major Users and their projects will account for about 82% of usage. Demand for single-cell sequencing is growing and we anticipate that there will be numerous additional users. Availability of this HPC will have a positive impact on other high-dimension data-based research throughout the College.