HPC architectures increasingly handle workloads where the working data set cannot be easily partitioned or is too large to fit into node local memory. We have defined a system architecture and a software stack to enable large data sets to be held in fabric-attached memory (FAM) that is accessible to all compute nodes across a Slingshot-connected HPC cluster, thus providing a new approach to handling large data sets.

CXL, an open industry standard interconnect, addresses the growing high-performance computational workloads to support heterogeneous processing and memory systems with applications in Artificial Intelligence, Machine Learning, Analytics, Cloud Infrastructure, Cloudification of the Network and Edge, communication systems, and High-Performance Computing by enabling coherency and memory semantics on top of PCI Express® (PCIe®) based I/O semantics to optimize performance in evolving usage models.

2023 has been an interesting and challenging year for storage security. The cyber threat landscape has witnessed large numbers of attacks impacting data and increased nation state activities directed at critical infrastructure. The regulatory landscape is undergoing change as well and potentially imposing requirements that necessitate adjustments to security capabilities, controls, and practices to reflect new realities. By the end of 2023 there will be significant changes to security standards and specifications relevant to storage.

Current clustered Samba uses its homegrown distributed database "ctdb" as a storage backend for maintaining coherent fileserver state. ctdb predates most cloudy distributed NoSQL databases that came to rise on the wings of the likes of Google Bigtable, Amazon Dynamo, Apache Cassandra and so on in the 2000's. It has worked extremely well for the high performance scaleout NAS usecase, but the emerging shift to the cloud entails serious scalability, elasticity and managability challenges. So are there alternatives to ctdb?

The Key Per IO (KPIO) project was a joint initiative between NVM Express® and the Trusted Computing Group (TCG) Storage Work Group to define a new KPIO Security Subsystem Class (SSC) under TCG Opal SSC for NVMe® class of Storage Devices. Self-Encrypting Drives (SED) perform continuous encryption on user accessible data based on contiguous LBA ranges per namespace. This is done at interface speeds using a small number of keys generated/held in persistent media by the storage device. KPIO allows a large number of encryption keys to be managed and securely downloaded into the NVM subsystem.

As industries grapple with an ever-expanding and complex sea of data, there is a paramount need for rethinking storage and analytics. Traditionally, data is categorized as structured or unstructured, but at AirMettle, we perceive data through a different lens. Most data embodies a semi-structured nature, an overlooked characteristic that holds the key to unlocking unprecedented efficiency in data analytics.

The IEEE Security In Storage Work Group (SISWG) produces standards that many storage developers, storage vendors, and storage system operators care about, including: a) A family of standards on sanitization: the IEEE 2883 family b) A family of standards on encryption methods for storage components: the IEEE 1619 family c) A standard on Discovery, Authentication, and Authentication in Host Attachments of Storage Devices: the IEEE 1667 specification IEEE has a different work group (IEEE P3172) focusing on post-quantum cryptography, but when they are done, a family method that recommends new q

This presentation by provides an overview of the NVM Express® ratified technical proposal TP4146 Flexible Data Placement and shows how a host can manage its user data to capitalize on a lower Write Amplification Factor (WAF) by an SSD to extend the life of the device, improve performance, and lower latency.

It’s been a year since the announcement that Intel would “Wind Down” its Optane 3D XPoint memories. Has anything risen to take its place? Should it? This presentation reviews the alternatives to Optane that are now available or are in development, and evaluates the likelihood that one or more of these could fill the void that is being left behind. We will also briefly review the legacy Optane left behind to see how that legacy is likely to be used to support persistent memories in more diverse applications, including cache memory chiplets.

Object storage systems provide significant value for storing and managing data. The nature of data stored in object systems opens up opportunities to get more value out of these systems than the common expectations of cost reduction, ease of use, resilience, and durability. Maintaining the metadata for large unstructured data sets is difficult and can be time consuming. The system I propose here is an add on engine that adds Artificial Intelligence inferencing functionality to object storage systems.