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Azure Managed Lustre—Parallel file system for HPC and AI workloads | Azure Weblog and Updates

At present, Microsoft is saying the general public preview of Azure Managed Lustre, a brand new addition to the storage choices in our Azure HPC + AI answer. Lustre is an open-source parallel file system famend for high-performance computing (HPC) and is adept at large-scale cluster computing. Azure Managed Lustre supplies the high-performance storage of Lustre with the management and consistency of Microsoft’s cloud. Because of this, clients can concentrate on their enterprise objectives, whether or not that’s constructing a fraud detection system primarily based on SAS analytics, decoding the human genome to create the following breakthrough in medication, main the frontiers of vitality exploration with seismic information processing and sustainable vitality answer, or predicting the local weather and climate patterns impacting human life on planet earth.

Lustre as an Azure Managed Service

With Lustre as a managed service, customers can now concentrate on constructing and operating their HPC and AI functions with out managing a complicated parallel file system. HPC and AI workloads can seamlessly migrate to the cloud, retaining their compatibility with Lustre and defending current automation and platform investments developed when beforehand run on premises. Azure Managed Lustre permits a quick on-demand deployment of clusters serving international areas, assuaging advance planning whereas assembly the compliance and information residency necessities. With a palette of efficiency choices, Azure Managed Lustre delivers an elastic answer the place customers can deploy unbiased and unique clusters with predictable efficiency eliminating the noisy-neighbor drawback generally skilled within the on-premises shared infrastructures.

The general public preview consists of two sturdy SSD-based SKUs which ship a selection in efficiency choices for mission essential workloads: 125 MB/s and 250 MB/s for each provisioned TiB of capability. In the course of the preview, you’ll be able to create cluster sizes as much as 128 TiB by default, with an choice to scale as much as 768 TiB upon request. Azure Managed Lustre is constructed on the extremely sturdy Azure managed disks with locally-redundant storage consisting of three replicas so even when one or two replicas expertise points you continue to have tolerance towards failures. Azure Managed Lustre delivers POSIX-compliant Lustre model 2.15 (LTS), which provides a number of efficiency enhancements. We targeted on capabilities that allow customers to devour Azure Managed Lustre simply with out worrying about how you can construct and deploy the Lustre purchasers on their compute VMs and containers. With the Azure Managed Lustre preview, clients can select from:

  • Use Azure HPC photographs prebuilt with Lustre consumer packages for Ubuntu 18.04 and 20.04 (or)
  • Obtain Lustre consumer packages from for Linux distros – Ubuntu 18.04, 20.04 & 22.04; RHEL 7.8 & RHEL 8.0

Information tiering utilizing hierarchical storage administration permits customers to import and export information between Azure Managed Lustre and Azure Blob. This functionality permits customers to outline information archival, retention and safety primarily based on pre-defined insurance policies. Customers of enormous information units can import the info which is sizzling and related for his or her energetic information processing in Azure Managed Lustre clusters and archive/retain the remaining in Azure Blob. This permits them to maintain their run-time prices low. Moreover, information tiering to Azure Blob permits the customers to leverage its international presence and availability to instantiate Azure Managed Lustre clusters in a number of areas on demand. Integration with Azure Blob moreover facilitates multiprotocol information entry through NFS, HDFS and REST. Trendy containerized functions in AI/machine studying (ML) and analytics can now run on Azure Kubernetes Service leveraging the CSI driver for Azure Managed Lustre. For instance, analytics functions constructed on high of SAS Viya can seamlessly leverage the excessive per-core storage efficiency that they require by integrating with the Azure Managed Lustre CSI driver.

Objective-built for HPC and AI workloads

We’re grateful to our personal preview clients who helped us construct and ideal the product whereas addressing their key wants. We’re dedicated to constructing and supporting Azure Managed Lustre because it helps you along with your journey of operating your high-performance functions in Azure.

Bizdata logo

We work with clients who’ve many various kinds of HPC workloads, and we ceaselessly hit storage efficiency constraints as a result of their initiatives can contain something from 1000’s to tens of 1000’s of recordsdata, all of which have to be learn and written in parallel. Once we encounter storage bottlenecks or throttling errors on our HPC platforms, we all know it’s time to flip to Azure Managed Lustre. With Azure Managed Lustre we are able to add extraordinarily performant storage that may readily sustain with the HPC compute, and robotically sync enter and outcomes information with persistent blob storage. As a result of it may be began and stopped on demand, inside a couple of minutes, Azure Managed Lustre is kind of price environment friendly and pays for itself by giving us the power to scale up or scale out our HPC compute, producing outcomes a lot quicker.”—Felipe Ayora, Director—Analysis and Superior Computing, BizData.

Sharpreflections logo

The brand new Azure Managed Lustre file system is an actual recreation changer. It accelerates HPC deployment of our software program and eliminates the necessity to run persistent storage servers. Setup is easy, and parallel i/o efficiency is quick sufficient to help very high-throughput workloads.”—Invoice Shea, CEO Sharp Reflections.

Subsequent Steps

To trial Azure Managed Lustre totally free, full the registration kind. Study extra about how you can use Azure Managed Lustre and its numerous supported options from our documentation.

Study extra about HPC and AI options



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