Learn what is happening in NVMe to support Computational Storage devices. The development is ongoing and not finalized, but this presentation will describe the directions that the proposal is taking. Kim and Stephen will describe the high level architecture that is being defined in NVMe for Computational Storage. The architecture provides for programs based on a standardized eBPF. We will describe how this new command set fits within the NVMe I/O Command Set architecture. The commands that are necessary for Computational Storage will be described.

Standardized computational storage services are frequently touted as the Next Big Thing in building faster, cheaper file systems and data services for large-scale data centers. However, many developers, storage architects and data center managers are still unclear on how best to deploy computational storage services and whether computational storage offers real promise in delivering faster, cheaper – more efficient – storage systems. In this talk we describe Los Alamos National Laboratory’s ongoing efforts to deploy computational storage into the HPC data center.

There is a new architectural approach to accelerating storage-intensive databases and applications which take advantage of new techniques that dramatically accelerate database performance, improve response times and reduce infrastructure cost at massive scale. This new architecture efficiently stores fundamental data structures increasing space savings—up to 80%—over host- and software-based techniques that cannot address inherent inefficiencies in today’s fastest SSD technology.

The SNIA Computational Storage TWG is driving forward with both a CS Architecture specification and a CS API specification. How will these specification affect the growing industry Computational Storage efforts? Learn what is happening in industry organizations to make Computational storage something that you can buy from a number of vendors to move your computation to where your data resides. Hear what is being developed in different organizations to make your data processing faster and allow for scale-out storage solutions to multiply your compute power.

With the ever-increasing dataset sizes, several file formats like Parquet, ORC, and Avro have been developed to store data efficiently and to save network and interconnect bandwidth at the price of additional CPU utilization. However, with the advent of networks supporting 25-100 Gb/s and storage devices delivering 1, 000, 000 reqs/sec the CPU has become the bottleneck, trying to keep up feeding data in and out of these fast devices.

Scientific data is commonly represented by static datasets. There is a myriad of sources for such data: snapshots of the Earth, as captured by satellites, sonars, and laser scanners; the output of simulation models; the aggregation of existing datasets, and more.

The computational storage ecosystem is growing fast, from IP providers to system integrators. How to benefit from this promising technology : design your own device and bring your added value at the device level, or save time and select an available product? This talk will present guidelines to use computational storage technology and a review of the computational storage building blocks, including computing, non-volatile memories, interfaces, embedded software, host software and tools.

Computational storage is paramount for truly composable infrastructure. While there isn't yet broad adoption, computational storage is ready to move beyond test benches into widespread deployment. This presentation explore today’s cloud data center requirements using real-world use cases to show how to move compute to the data, instead of the data to the compute. Computational systems seek to address the limitations of hyper-converged infrastructure systems, in which users can only scale compute and storage by purchasing additional nodes.

With the growth of Containerized applications and Kubernetes as an orchestration layer, the ability to leverage these technologies within the storage device directly adds additional support to the implementation and parallel processing of data. By using an os-based Computational Storage Drive (CSD), a deployment of SPARK will be presented and the steps required to achieve this task. The ability to use a distributed processing operation and orchestrate it with the Host and the CSDs at the same time to maximize the benefits of the application deployment.

In this presentation the Co-Chairs of the Computational Storage Technical Working Group (CS TWG) will provide a status update from the work having been done over the last year, including the release of the new Public Review materials around Architecture and APIs. We will update the status of the definition work and address the growing market and adoption of the technology with contributions from the 47+ member organizations participating in the efforts. We will show use cases, customer case studies, and efforts to continue to drove output from the Technical efforts.