Longer keys are necessary to process the sophisticated traffic increases in the workload needed by the SSL decryption engine.
The impact to latency and related service levels are also significant.
When traffic is encrypted with such technologies, it becomes impossible to gain needed visibility into the traffic.
A study by NSS Labs reported that decrypting SSL traffic on a firewall appliance (for the purpose of gaining visibility into the traffic) implies a loss of 74 percent for throughput and 87.8 percent for transactions per second.
GPUs, as coprocessors to x86 CPUs, enable much needed parallel processing for machine learning algorithms.
GPUs were originally designed for gaming and graphics applications.
Similar to CUDA-based multi-threaded programming on GPUs, the NFP silicon in Smart NICs supports multi-threaded programming using C or higher-level, vendor-agnostic programming methods such as P4 and e BPF.This way of processing is very unlike general-purpose CPUs like X86 and ARM, which are optimized for single-threaded processing needed in common software applications like web servers and database processing.Modern GPUs also provide high speed and efficient memory access to the massive amounts of training data that need to be processed by machine learning algorithms.Machine learning training, inference algorithms and related technologies are foundations of AI, and these have existed for decades.The inflection points that have created a groundswell of opportunities for companies like Nvidia are: As more devices – both in numbers and types across different industries – connect to the Internet (in other words the Io T phenomena), the amount of useful data generated and the ability to use such data for machine learning to help improve user experiences in those industries will experience a viral effect.