Computational storage in general can bring unique benefits in increasing the efficiency of CPU utilization in a data processing system. In this presentation we discuss the benefits of leveraging computational storage for offloading compute intensive tasks of object storage applications in a disaggregated storage environment. We demonstrate the ability of the solution to complement the CPU by taking away tasks that benefit from in-situ processing within the storage, thereby improving the overall system performance while lowering the TCO.

Computational Storage is a new field that is addressing performance and scaling issues for compute with traditional server architectures. This is an active area of innovation in the industry where multiple device and solution providers are collaborating in defining this architecture while actively working to create new and exciting solutions. The SNIA Computational Storage TWG is leading the way with new interface definitions with Computational Storage APIs that work across different hardware architectures.

Apache Ozone is a highly scalable distributed object storage system and also provides the file system interface. Distributed storage systems typically use replication to provide high reliability and Ozone supports the replication model for the same. However replication is expensive in terms of storage space and other resources ( ex: network bandwidth etc). Erasure Coding(EC) is a proven technique to save storage space and throughput requirements. Apache Ozone implemented the EC support.

We examine the benefits of using computational storage devices like Xilinx SmartSSD to offload the compression to achieve an ideal compression scheme where higher compression ratios are achieved with lower CPU resources. This offloading of compute intensive task of compression frees up the CPU to cater to real customer applications. The scheme proposed in this paper comprises of Xilinx Storage Services (XSS) with Xilinx Runtime (XRT) software and HLS based GZIP compression kernel that runs on the FPGA.

Computational storage has emerged as a powerful solution for improving the performance and efficiency of compute systems by offloading computational tasks to storage devices. Emulating computational storage environments is crucial for software development, testing and benchmarking purposes before deploying such systems in production. This proposal presents an approach to emulate Computational Storage Drives(CSDs), Computational Storage Processor (CSPs) and Computational Storage Arrays (CSAs) using QEMU, SPDK, and libvfio-user.

High-performance computing data centers supporting large-scale simulation applications can routinely generate a large amount of data. To minimize time-to-result, it is crucial that this data be promptly absorbed, processed, and potentially even multidimensionally indexed so that it can be efficiently retrieved when the scientists need it for insights.

Computational storage is a new paradigm in computing where data processing is moved closer to the storage device to enhance performance and reduce data transfer bottlenecks. In this presentation, we showcase how data integrity checks on CSDs with a software toolkit and a dedicated hardware to process the host payload can significantly improve storage performance and reduce data transfer overhead. This is a cross-industry effort that includes the system, the system software, and the CSD representing a complete end-to-end solution.

Learn what is happening in NVMe to support Computational Storage devices. Computational Storage requires two new command sets: The Computational Programs Command Set and the Subsystem Local Memory Command Set. We will introduce you to how these two command sets work together, the details of each command set, and how they fit within the NVMe I/O Command Set architecture.

Computational Storage standards are under active development at both SNIA and NVMe. The CS TWG in SNIA continues to work on enhancements to the Architecture and Programming Model after the successful release of the 1.0 revision of the standard in August 2022. The CS TWG also continues to refine the CS API, which was released for public review in July 2022, to ensure alignment and compatibility with NVMe. Many of the same companies are engaged with the SNIA CS work and the NVMe CS work and strive to ensure compatibility and cohesion between the SNIA and NVMe CS standards.

There is an exponential growth of stored data and of applications processing data in the cloud and the edge. These applications based on traditional CPU based architectures may run into resource limits. Recent developments in Computational Storage have emerged as a promising solution to alleviate the limitations associated with traditional models. In this model, compute is performed near data thereby overcoming CPU, memory and fabric limitations.