Excelero Achieves Red Hat OpenShift Operator Certification for its NVMesh Software-Defined Storage

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SAN JOSE, March 11, 2021 — Excelero, maker of software-defined storage for IO-intensive workloads, such as GPU computing for AI/ML/DL, HPC and fast data analytics, has achieved Red Hat OpenShift Operator Certification for its NVMesh elastic NVMe software.

With this certification, storage architects can integrate high-performance, low-latency NVMe storage into the native Red Hat Enterprise Linux (RHEL) and Red Hat OpenShift environments with Excelero NVMesh, and support both public cloud and on-premises deployments with their choice of networking protocols for even the most demanding applications.

Trusted by over 2,000 organizations, Red Hat OpenShift is the industry’s leading enterprise Kubernetes platform that helps organizations innovate across architectures, applications and infrastructures. As organizations deploy I/O-intensive containerized workloads including AI, deep learning (DL), high performance computing (HPC) and data analytics, IT leaders are rethinking the standard requirement to deploy high-performance storage appliances with Container Storage Interfaces (CSI) plugins. The dependency on appliances can restrict hybrid and multi-cloud deployments of these workloads and slow down digital transformation.

With Excelero NVMesh’s native support for RHEL CoreOS through its NVMesh Operator acting as a custom controller to install and control NVMesh inside OpenShift clusters, IT teams can build agile storage infrastructures without the need for an external storage cluster or appliance. They can support public cloud, hybrid cloud, multi-cloud and on-premise models with the same technology stack with the customer’s choice of RDMA and TCP/IP based storage networking. Backed by Excelero NVMesh, data centers teams using OpenShift can create agile and elastic infrastructures that achieve superior storage performance, scalability and ROI, with the flexibility to adjust further as their needs grow.

“Red Hat OpenShift allows organizations to support multi-cloud use cases and gives them a performance and flexibility advantage,” said Edo Ganot, CBO of Excelero. “Excelero has been working with key customers on extending NVMesh support to public clouds. We’re delighted to become a part of the Red Hat OpenShift Operator ecosystem and help the community of OpenShift data centers to build agile, high-performance, lower cost storage architectures to help accelerate their digital transformation journey.”

“Red Hat OpenShift makes it easier for organizations to build and deploy container-based applications across any cloud environment, and OpenShift Operators are leading the way,” said Lars Herrmann, vice president, partner ecosystems, product and technologies, at Red Hat. “We are pleased to welcome Excelero as a Red Hat certified Operator to help customers better manage and automate their OpenShift deployments and look forward to our continued collaboration.”

Excelero NVMesh’s CSI and Operators are available in the Red Hat Ecosystem Catalog.

About Excelero
Excelero is the market leader in distributed block storage software. The company delivers Elastic NVMe software that powers AI training and analytics workloads at any size and performance scale. With its partners, Excelero enables customers to massively improve ROI across their entire infrastructure, using standard servers in the data center and standard instances in the public clouds, maximizing GPU and NVMe utilization, minimizing overheads and reducing software license costs.

Excelero’s NVMesh® is distributed block storage that connects CPUs and GPUs to NVMe flash to create a significant improvement in price/performance, from entry level to any scale. NVMesh was designed as a storage layer that eradicates data bottlenecks so teams can access data at any speed in any location in public or private clouds. NVMesh delivers up to 20x faster data processing for multi-server, multi-GPU compute nodes when working with massive datasets for machine learning, deep learning and complex analytical workloads.