论文标题
Daos作为HPC存储，从数值天气预测中的视图
DAOS as HPC Storage, a view from Numerical Weather Prediction
论文作者
论文摘要
对象存储解决方案有可能解决某些I/O工作负载的POSIX文件系统的长期性能问题，而新的存储技术为数据密集型用例提供了有希望的性能特征。在这项工作中，我们对Intel的分布式异步对象存储（DAOS）进行了初步评估，这是一种新兴的高性能对象存储，并结合非胆汁差存储，并评估其在HPC存储中的潜在用途。我们证明DAO可以提供所需的性能，在大多数情况下，带宽缩放与其他DAOS服务器节点线性缩放，尽管配置和应用程序设计方面的选择可能会影响可实现的带宽。我们描述了一个新的I/O基准和相关的指标，该指标可以从应用程序衍生的工作负载中解决对象存储性能。
Object storage solutions potentially address long-standing performance issues with POSIX file systems for certain I/O workloads, and new storage technologies offer promising performance characteristics for data-intensive use cases. In this work, we present a preliminary assessment of Intel's Distributed Asynchronous Object Store (DAOS), an emerging high-performance object store, in conjunction with non-volatile storage and evaluate its potential use for HPC storage. We demonstrate DAOS can provide the required performance, with bandwidth scaling linearly with additional DAOS server nodes in most cases, although choices in configuration and application design can impact achievable bandwidth. We describe a new I/O benchmark and associated metrics that address object storage performance from application-derived workloads.
