Server / HPC Cluster

我们开发各种生物信息学领域的算法和工具，如网络生物学，遗传流行病学，系统医学等。这些帮助我们从分子数据和传感器数据中获得新的见解，并产生新的假设，可用于探索各种疾病的新诊断和治疗方法。由于涉及的数据量，我们需要数百TB的存储空间。我们的许多工作也对计算硬件提出了很高的要求，这是传统的网格计算架构无法满足的。例如，生物网络通常包括数千个节点和数十万条边，并且需要特别大的主存储器来充分处理。为了有效地调查这些和其他数据，同时需要大量的处理器内核。这种组合在传统的计算中心中没有表现出来，因为它们只提供许多处理器核心或大量的主存。对分子和传感器数据的更精确的基于人工智能的分析只能在高度并行的共享存储器架构中执行。由于不同项目的硬件需求不同，我们需要一个能够灵活满足这些需求的集群，并且根据项目的不同，可以提供足够的内存、处理器内核、主存和GPU（如果需要）。

We develop algorithms and tools in various bioinformatics areas, such as network biology, genetic epidemiology, systems medicine, and others. These help us to gain new insights from molecular data and sensor data and generate new hypotheses that can be used to explore new diagnosis and treatment approaches for various diseases. Due to the amount of data involved, we need several hundred terabytes of of storage space. Much of our work also places very high demands on the computing hardware, which we cannot meet with conventional grid-computing architectures. For example, biological networks often comprise thousands of nodes and hundreds of thousands of edges and require a particularly large main memory to be processed adequately. In order to investigate these and other data effectively, a high number of processor cores is required at the same time. This combination is not represented in conventional computing centers, since they offer only either many processor cores or a large amount of main memory. More precise artificial intelligence- based analyses of molecular and sensor data can only be performed in highly parallel shared-memory architectures. Due to the heterogeneous hardware requirements in different projects, we need a cluster that can satisfy these needs flexibly and, depending on the project, can provide enough memory, processor cores, main memory, and GPUs, if necessary.