FSx for Lustre is a distributed parallel filesystem that’s used as part of HPC (high-performance computing) workloads across a variety of complex fields (including life sciences, machine learning, meteorology, finance, and media). It is AWS’s managed version of the Lustre filesystem, and while it’s a popular high-performance storage option, it can quickly become unaffordable as HPC projects scale. This article directly compares the cost and performance for FSx for Lustre and its alternatives, as well as discussing their scalability and compatibility with your existing HPC workloads. cunoFS is a high-performance alternative to Amazon’s FSx for Lustre filesystem This article compares FSx for Lustre, AWS Mountpoint, and cunoFS. cunoFS allows HPC users to use Amazon’s S3 object storage as a high-performance POSIX-compatible filesystem that outperforms FSx for Lustre on both cost and performance, as demonstrated in the benchmarks on this page.  cunoFS allows you to run your existing applications and scripts (Python, C++, bash, etc.) as normal, which massively improves your development efficiency.  Features and compatibility: comparing FSx for Lustre, AWS Mountpoint, and cunoFS FSx for Lustre vs. Mountpoint for Amazon S3 Mountpoint for Amazon S3 allows you to mount an Amazon S3 bucket as a local filesystem, but it has

Migrating on-premises scientific workloads to the cloud has many benefits in terms of cost and performance that are attractive to both academic and business organizations. Microsoft Azure is a popular cloud platform within the scientific research community as it offers a variety of features and tools to build and scale the infrastructure for your workflows, as well as being cost-effective and scalable.  This article explains what you need to consider when migrating your workloads to Azure, and compares the best workload migration solutions. We explain how to set up your cluster, migrate your scheduling system, and choose appropriate file storage (spoiler: using cunoFS to access Azure Blob Storage wins). What you need to consider before migrating your workloads to Azure When you’re planning your migration you should ensure that Azure supports all the features you require for your workloads, both now and in the future. You don’t want to invest in migrating to a platform that later becomes unsuitable — both re-implementing your workflows and migrating your data will be expensive. To encourage customer loyalty, cloud storage providers make it cheap to transfer data to their platforms but expensive to move it out. Azure is no exception, charging a big

High-performance computing, machine learning (ML) and AI, and scientific workloads must be built to be scalable, a requirement that is often difficult for traditional filesystem storage solutions to meet. Object storage provides a fast, networked, and highly scalable storage option for these use cases, but there are some inherent performance and compatibility problems that need to be solved when using object storage with your existing scripts and applications. In this article, we break down the types of performance requirements that might exist for object storage, and show how cunoFS achieves higher throughput performance with existing object storage solutions. cunoFS turns any S3-API compatible object storage solution into a high-performance, POSIX-compatible filesystem that can be used with your existing code, often with better performance than native APIs and SDKs. cunoFS works on all major operating systems and is compatible with all popular object storage systems, both cloud and on-premises, including AWS S3, Azure Blob Storage, Google Cloud Storage, NetApp StorageGRID, MinIO, and Dell ECS. High-performance storage requirements: what does “high-performance” mean to you? ”Performance” can mean different things in different use cases — each project measures success differently, and there can be some confusion as to whether a storage solution is