NVMe technology has brought disruptive innovation to storage and has a far-reaching impact on the datacenter infrastructure. The features of the protocol make NVMe the number one choice when designing a new product or application involving storage.
Enterprise applications such as database acceleration require low-latency as well as high-bandwidth 4K or 8K data write transfer rates which are two requirements that fit perfectly into the NVMe protocol strengths. These characteristics place NVMe in the lead to implement redo log, for example, a use case where many transaction records get stored and for future replay if the database fails. For this use case, the 250S+ brings up to 4TB of NVMe storage straight to the edge of the FPGA reconfigurable fabric where the transaction records get gathered to the SSDs at high-speed ready for replay14.
NVMe also alleviates the challenges of virtualized infrastructures and simplifies the implementation of VMs (Virtual Machines), stateless VMs and SRIOV where IO is the most common bottleneck. In the stateless VM use case, the IT manager needs lock down operating system images that corporate users do not modify. Users only modify their data and the OS image remains unchanged in the NVMe storage; privacy and security between users is critical. For such IT infrastructure, NVMe storage is shared between multiple users. The 250S+ is all-in-one platform to implement this application. Each 1TB physical drive gets divided by the FPGA IP so each user gets segregated and secure access to its OS image and data. The hypervisor manages the direct access to the fraction of drive without the need for an emulation driver which provides better performance for this IO bounded application.
The “Big Data” market also brings opportunities for intelligent NVMe products which combine storage and processing since it is moving away from a batching approach to a real-time processing methodology. Map reduce problems are moving towards real-time analytics instead of batching and, therefore, they need a new tier of storage which is much faster than the GFS backend. The storage tiering now seen in IT infrastructures separates cold storage rarely accessed and low speed, to very fast SSDs, NVMe or NVM memories. In this use case, all the data gets recorded in the GDFS but then it is moved to a compute node with faster memory. The 250-SoC implementing NVMe-over-Fabric answers both these requirements as it gives access to high-speed storage and high-performance compute capabilities.
The deep learning industry has similar needs to the analytics world. The new generation accelerators for deep learning, i.e., GPGPUs, TPUs and FPGAs; these devices need large memory bandwidth to match the chips’ compute abilities. The training operations consume a lot of this high-throughput data, often multiple terabytes15. Recent research efforts show that the FPGA fabric can accelerate training operations of certain network types. Therefore, combining both the storage and the compute engine onto one hardware platform reduces the latency allowing for more retraining cycles as the training dataset increases16.
In the HPC space, local storage of the 250S+ and the remote version with the 250-SoC have several applications like checkpoint/restart, burst buffer, distributed filesystems or caching the job data from a scheduler. By running the algorithm close to the storage on the FPGA fabric, the footprint of the FPGA application remains low, while utilizing the storage fully and keeping the CPU free for other processing jobs. Instead of simply storing the data or using host CPU to compress or encrypt the in-memory databases, in which gigabytes of data are held in volatile memory but need to be backed up into flash on a regular basis. An FPGA-based system can process these snapshots of data for permanent storing into large NVMe-based storage arrays. For this type of operation, the MPSoC is particularly well-suited to perform more complex operations on the user data.
Finally, in the IoT space, there is a need for data filtering and preprocessing on IoT gateways where aggregation takes place as well as encryption for data after it has been received, the FPGA processes streams of data in real-time with bit-operations like encryption or compression and stores the data away on-board using the 250S+ or passes it to the storage backplane at the input bandwidth with the cabled 250S+ or the 250-SoC. FPGAs are also the platform of choice from blockchain calculations. Blockchain technology brings a differentiation to IoT gateways to provide an adaptive and secure method to maintain user privacy preferences of IoT devices17.