When we think of computational storage, we often think of offloading applications like databases. However, the SSD is an ideal location to offload the storage controller itself. For example, data reduction is must have requirement for today’s storage controllers. Offloading compression to the SSD results in data reduction at line speed. This talk will focus on this and other functions that can be offloaded to the SSD enabling lower latency, higher IOPs and making CPU MIPs available for things it does best like replication.

Join the presenters from Day 2 in a lively panel discussion and ask your questions.

Join Summit Chair David McIntyre as he wraps up the Summit and previews what is next on the horizon for persistent memory and computational storage education.

Compute Express Link will serve as a new computing backbone that will carry the weight of petabytes of memory shared by heterogenous processors. At the heart of the new CXL fabrics will be software that transforms the hardware into a pool of composable resources for fine-grained provisioning of capacity, performance, availability, and mobility. In this session, Bernie Wu will provide a sneak preview of how CXL will change the datacenter. He’ll accomplish this by demonstrating 3 technologies that provide these capabilities today, and which will move onto CXL when it’s available.

AI/ML is not new, but innovations in ML models development have made it possible to process data at unprecedented speeds. Data scientists have used standard POSIX file systems for years, but as the scale and nComputational storage can bring unique benefits in increasing the efficiency of CPU utilization in a data processing system. Here we discuss the benefits of leveraging computational storage in a disaggregated storage environment.

In most architectures, data is copied from far memory or storage to a local memory for processing. However, when the size of data is in order of hundreds of gigabytes, it is efficient to move static compute functions near data, instead of copying the bulky data near compute.

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.

To exploit the near-storage computational capability for fast I/O and data processing, consequently reducing I/O bottlenecks. By drawing inspiration from seminal CISC processor ISAs, we introduce a new abstraction, CISCOps for storage devices, that combines multiple I/O and data processing operations into one fused operation and offloaded for near-storage processing. By offloading, CISCOps significantly reduces dominant I/O overheads such as system calls, data movement, communication, and other software overheads.

In this session, 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.

NVMe and SNIA are both working on standards related to Computational Storage. The question that is continually asked is are these efforts are compatible or at odds with each other. The truth is that many of the same people are working on both of these standards efforts and are very interested in ensuring that they work together as opposed to conflicting with each other.