New-Tech Europe Magazine | Q2 2021

will be assigned accordingly with no wasted hardware. That all sounds great in theory, but in practice there is one big issue: Latency. The Latency Challenge As you disaggregate resources and move them farther apart you incur more delays and reduced bandwidth due to the network traffic between CPUs and SSDs, or between CPUs and accelerators. Unless you have some way to reduce the network traffic and interconnect the resources in an efficient way, this can be severely limiting. That’s where FPGAs play three major roles in solving the latency challenge: FPGAs act as adaptable accelerators that can be customized for each workload for maximum performance. FPGAs can also bring compute closer to the data, thereby reducing latency and minimizing the required bandwidth. The adaptable, intelligent fabric of FPGAs enables efficient pooling of resources without incurring excessive delays. Adaptable Acceleration The first significant advantage for FPGA-based compute accelerators is dramatically improved performance for workloads that are in high demand these days. In video transcoding use cases for live streaming applications, FPGA solutions typically outperforms x86 CPUs by 30x, which helps data center operators meet the huge increase in the number of simultaneous streams. Another example is in the critical field of genomic sequencing. A recent Xilinx genomics customer found that our FPGA-based accelerator delivered the answer 90 times faster than a CPU, helping medical researchers test DNA samples in a fraction of the time it once took.

Figure 1: Xilinx Alveo SN1000 SmartNIC

Moving Compute Closer to Data The second key advantage for FPGAs in a composable data center is the ability to bring adaptable compute close to the data, whether at rest or in motion. Xilinx FPGAs used in SmartSSD computational storage devices accelerate functions like high speed search, parsing, compression and encryption, which are usually performed by a CPU. This helps offload the CPU for more complex tasks, but also reduces the traffic between the CPU and the SSDs, thereby cutting down on bandwidth consumption and reducing latency. Similarly, our FPGAs are now used in SmartNICs like our new Alveo SN1000 to accelerate data in motion with wire-speed packet processing, compression and crypto services as well as the ability to adapt to custom switching requirements for a particular data center or customer. Intelligent Fabric When you combine an FPGA’s adaptable compute acceleration with low-latency connectivity, you can go a step further in the composable data center. You can assign a compute- heavy workload to a cluster of

accelerators that are interconnected by an adaptable intelligent fabric - creating a high-performance computer on demand. Of course, none of this is possible if you can’t program the compute accelerators, SmartSSDs and SmartNICs with the optimal acceleration algorithms, and then provision them in the right numbers for each workload. For that task, we have built a comprehensive software stack that leverages domain-specific industry frameworks like TensorFlow and FFMPEG, which work in conjunction with our Vitis development platform. We also see a role for higher-level provisioning frameworks like RedFish to help with intelligent resource allocation. Conclusion The promise of the composable data center is an exciting change and Xilinx devices and accelerator cards are key building blocks to this new efficient architecture. With rapid reconfigurability, low latency and a flexible architecture that can adapt to changing workloads, Xilinx is well positioned to be a major player in this evolution.

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