Last month, the SNIA Networking Storage Forum made sense of the “wild west” of programming frameworks, covering xPUs, GPUs and computational storage devices at our live webcast, “You’ve Been Framed! An Overview of xPU, GPU & Computational Storage Programming Frameworks.” It was an excellent overview of what’s happening in this space. There was a lot to digest, so our stellar panel of experts has taken the time to answer the questions from our live audience in this blog. Q. Why is it important to have open-source programming frameworks? A. Open-source frameworks enable community support and partnerships beyond what proprietary frameworks support. In many cases they allow ISVs and end users to write one integration that works with multiple vendors. Q. Will different accelerators require different frameworks or can one framework eventually cover them all? A. Different frameworks support accelerator attributes and specific applications. Trying to build a single framework that does the job of all the existing frameworks and covers all possible use case would be extremely complex and time consuming. In the end it would not produce the best results. Having separate frameworks that can co-exist is a more efficient and effective approach. That said, providing a well-defined hardware abstraction layer does complement different programming frameworks and can  allow one framework to support different types of accelerators. Q. Is there a benefit to standardization at the edge? A. The edge is a universal term that has many different definitions, but in this example can be referred to as a network of end points where data is generated, collected and processed. Standardization helps with developing a common foundation that can be referenced across application domains, and this can make it easier to deploy different types of accelerators at the edge. Q. Does adding a new programming framework in computational storage help to alleviate current infrastructure bottlenecks? A. The SNIA Computational Storage API and TP4091 programming framework enables a standard programming approach over proprietary methods that may be vendor limited. The computational storage value proposition significantly reduces resource constraints while the programming framework supports improved resource access at the application layer. By making it easier to deploy computational storage, these frameworks may relieve some types of infrastructure bottlenecks. Q. Do these programming frameworks typically operate at a low level or high level? A. They operate at both. The goal of programming frameworks is to operate at the application resource management level with high level command calls that can initiate underlying hardware resources. They typically engage the underlying hardware resources using lower-level APIs or drivers. Q. How does one determine which framework is best for a particular task? A. Framework selection should be addressed by which accelerator type is best suited to run the workload. Additionally, when multiple frameworks could apply, the decision on which to use would depend on the implementation details of the workload components. Multiple frameworks have been created and evolve because of this fact. There is not always a single answer to the question. The key idea motivating this webinar was to increase awareness about the frameworks available so that people can answer this question for themselves. Q. Does using an open-source framework generally give you better or worse performance than using other programming options? A. There is usually no significant performance difference between open source and proprietary frameworks, however the former is more relatively adaptable and scalable by the interested open-source community. A proprietary framework might offer better performance or access to a few more features, but usually works only with accelerators from one vendor. Q. I would like to hear more on accelerators to replace vSwitches. How are these different from NPUs?    A. Many of these accelerators include the ability to accelerate a virtual network switch (vSwitch) using purpose-built silicon as one of several tasks they can accelerate, and these accelerators are usually deployed inside a server to accelerate the networking instead of running the vSwitch on the server’s general-purpose CPU. A Network Processing Unit (NPU) is also an accelerator chip with purpose-built silicon but it typically accelerates only networking tasks and is usually deployed inside a switch, router, load balancer or other networking appliance instead of inside a server. Q. I would have liked to have seen a slide defining GPU and DPU for those new to the technology. A. SNIA has been working hard to help educate on this topic. A good starting point is our “What is an xPU” definition. There are additional resources on that page including the first webcast we did on this topic “SmartNICs to xPUs: Why is the Use of Accelerators Accelerating.”  We encourage you to check them out. Q. How do computational storage devices (CSD) deal with “data visibility” issues when the drives are abstracted behind a RAID stripe (e.g. RAID0, 5, 6). Is it expected that a CSD will never live behind such an abstraction? A. The CSD can operate as a standard drive under RAID as well as a drive with a complementary CSP (computational storage processor, re: CS Architecture Spec 1.0). If it is deployed under a RAID controller, then the RAID hardware or software would need to understand the computational capabilities of the CSD in order to take full advantage of them. Q. Are any of the major OEM storage vendors (NetApp / Dell EMC / HPE / IBM, etc.) currently offering Computational Storage capable arrays? A. A number of OEMs are offering arrays with compute resources that reside with data. The computational storage initiative that is promoted by SNIA provides a common reference architecture and programming model that may be referenced by developers and end customers.