Compare HPE Pointnext vs. SwiftStack

The convergence of high-performance computing (HPC) and machine learning is placing unprecedented requirements on storage solutions, as the input/output demands of these two distinct workloads diverge significantly. This shift is occurring at this very moment, with a recent analysis from the independent firm Intersect360 revealing that a striking 63% of current HPC users are actively implementing machine learning applications. Furthermore, Hyperion Research projects that, if trends continue, public sector organizations and enterprises will see HPC storage expenditures increase at a rate 57% faster than HPC compute investments over the next three years. Reflecting on this, Seymour Cray famously stated, "Anyone can build a fast CPU; the trick is to build a fast system." In the realm of HPC and AI, while creating fast file storage may seem straightforward, the true challenge lies in developing a storage system that is not only quick but also economically viable and capable of scaling effectively. We accomplish this by integrating top-tier parallel file systems into HPE's parallel storage solutions, ensuring that cost efficiency is a fundamental aspect of our approach. This strategy not only meets the current demands of users but also positions us well for future growth.

SwiftStack is a versatile data storage and management solution designed for applications and workflows that rely heavily on data, enabling effortless access to information across both private and public infrastructures. Its on-premises offering, SwiftStack Storage, is a scalable and geographically dispersed object and file storage solution that can begin with tens of terabytes and scale to hundreds of petabytes. By integrating your current enterprise data into the SwiftStack platform, you can enhance accessibility for your contemporary cloud-native applications without the need for another extensive storage migration, utilizing your existing tier 1 storage effectively. SwiftStack 1space further optimizes data management by distributing information across various clouds, both public and private, based on operator-defined policies, thereby bringing applications and users closer to their needed data. This system creates a unified addressable namespace, ensuring that data movement within the platform remains seamless and transparent to both applications and users alike, enhancing the overall efficiency of data access and management. Moreover, this approach simplifies the complexities associated with data handling in multi-cloud environments, allowing organizations to focus on their core operations.