{"id":2633,"date":"2022-01-18T21:00:33","date_gmt":"2022-01-18T21:00:33","guid":{"rendered":"https:\/\/pensando.io\/?p=2633"},"modified":"2022-03-14T19:10:14","modified_gmt":"2022-03-14T19:10:14","slug":"apples-oranges-and-eggs","status":"publish","type":"post","link":"https:\/\/share.mahalodigital.com\/Pensando\/apples-oranges-and-eggs\/","title":{"rendered":"The Confusing World of Vendor Benchmarking: Part One – Apples, Oranges, and Eggs"},"content":{"rendered":"

This is the first in a series of upcoming articles that will focus on performance testing and comparisons from Steve McQuerry, Director of Technical Product Management at Pensando Systems.<\/span><\/h3>\n

In this series we will contrast the various architectural approaches to DPUs and demonstrate why the Pensando Distributed Services Card is the clear choice when deploying multiple services on a single card.<\/span><\/h3>\n

 <\/p>\n

If you’ve seen the graphs on social media or a presentation from Pensando and just want to get to the details, Principled Technologies<\/a> have prepared a validation report which can be found here: Principled Technologies Report (PDF)<\/a><\/span>.<\/span><\/h3>\n

 <\/p>\n

Recently both Fungible and NVIDIA claimed to have set world record storage IOPS performance with their respective DPU (Data Processing Unit) products.\u00a0 As a result of these claims, their methodologies have generated some interesting discussions around DPU performance measurement and the resulting comparisons, both in the<\/span> press and on social media<\/span><\/a>.\u00a0<\/span><\/p>\n

 <\/p>\n

N.B. When it’s pointed out that your competitive testing <\/span>“is not directly comparable” <\/i><\/b>you may end up with some egg on your face (read on – it’ll make sense soon).<\/span><\/p>\n

 <\/p>\n

When you dig into the details of these tests it\u2019s important to understand if it’s a like-to-like comparison or just another case of apples and oranges.\u00a0 Benchmarks and world records may be interesting and noteworthy,\u00a0 but we should never lose sight of the reason we do performance testing: to gain an understanding of how products should behave in <\/span>real-world environments<\/b>.\u00a0 If the testing principle is flawed then does it really show us anything?\u00a0 For example, how useful is it to have the ability to<\/span> crush 142 eggs on your forehead in one minute<\/span><\/a>?<\/span><\/p>\n

Corner case benchmarks and world record performance display a focus and tenacity that may deserve recognition, but does it matter if it doesn’t actually translate to real-world use cases?\u00a0 One of the main use cases for a DPU is to offload multiple infrastructure services from general purpose CPUs.\u00a0 In this sort of deployment, modern infrastructure services such as networking, storage and security don’t run individually or serially, so real-world performance can’t be measured for each service on a case-by-case basis\u2014it has to be measured with multiple services, all running at the same time.\u00a0 This is how workloads execute in the real world and, most importantly, when we model how the platform behaves with multiple services competing for resources, we gain some insight into where we’re likely to see performance issues.\u00a0<\/span><\/p>\n

<\/h4>\n

Contrasting DPU Architectures<\/strong><\/p>\n

Most DPUs can be put into one of two categories: sea of cores, or a sea of cores combined with hardware packet processing using a legacy NIC. (Many vendors use Arm cores but some have designs based on other processors.)\u00a0 Both of these solutions lack innovation and just recycle existing technologies in a different package. Instead of using legacy technologies to address the challenges facing modern infrastructure, Pensando has taken a more innovative approach: we have built<\/span> a <\/span>domain-specific processor<\/span><\/i> for I\/O processing alongside general purpose Arm cores.\u00a0<\/span><\/p>\n

Now you might ask: what’s the difference?\u00a0 Well, a domain-specific processor is built from the ground up to allow entire I\/O services to execute within the I\/O processor, vs. mostly running I\/O services in general purpose cores with hardware acceleration of certain functions in the datapath.<\/span><\/p>\n

Our platform is designed from the ground up to provide optimal performance and flexibility with full programmability for SDN, security, storage and telemetry services.\u00a0 The addition of P4-programmable hardware enables both Pensando and our customers to develop customized software-defined services and applications that run with the performance of hardware, something we refer to as <\/span>Software-in-Silicon<\/i><\/strong>.<\/span><\/p>\n

\"\"<\/p>\n

Figure 1 – Architectural Comparisons<\/span><\/em><\/h5>\n

 <\/p>\n

Figure 1 represents a simplified architectural overview of the components required to deliver basic SDN services.\u00a0 Each vendor listed, including Pensando, incorporates additional hardware offloads that were featured in these tests.<\/span><\/p>\n

In this series we will examine using DPUs to offload multiple services typically found in cloud environments. To understand the advantage of a programmable architecture compared to a fixed one, let\u2019s start with the basic SDN use case.<\/span><\/p>\n

Some vendors, like NVIDIA, use hardware acceleration (flow offload) to reduce the burden on the host CPU for SDN pipelines, by providing an API so the SDN software can program a flow entry into the HW table of the ASIC. Since packets are processed on the card instead of the host CPU, this allows for much higher throughput and reduced latency.\u00a0 Others, like AWS Nitro, rely primarily on a card with a sea of cores to offload the bulk of the packet processing functions.\u00a0\u00a0<\/span><\/p>\n

Both of these architectural approaches reduce the amount of CPU cycles required on the host for infrastructure service processing, which allows those valuable cycles to be used for business functions. (It also has the side effect of isolating the infrastructure services from the tenant workloads, which improves security.)<\/span><\/p>\n

It\u2019s been well-established in studies by hyperscalers such as<\/span> Google<\/span><\/a> and<\/span> Facebook<\/span><\/a> that moving infrastructure services onto specialized processors can have significant performance and efficiency benefits, quantifying the CPU overhead required for data center microservices to be between 22 and 80 percent. \u00a0 By now, any additional benchmarking demonstrating this is only belaboring the point.\u00a0 A more useful comparison would focus on what a specific architecture does well and not how it performs when servicing a variety of workloads and functions. \u00a0 With that in mind, let\u2019s take a closer look at a SDN-based comparison for a hardware offload DPU architecture vs. the Pensando Software-in-Silicon architecture.<\/span><\/p>\n

 <\/p>\n

Pensando Software-in-Silicon vs. NVIDIA Hardware Offload<\/strong><\/p>\n

The SDN benchmark data published by NVIDIA at HotChips 33 <\/span>(slide 11<\/span><\/a>) for BlueField-2 was based on a relatively small packet size (114 bytes).\u00a0 Tests like this can provide interesting results, but is it useful? (See my reference to eggs above.) That depends on how it compares to the type of traffic you are servicing.\u00a0 It may be similar to certain applications, but how does it relate to providing services across a variety of traffic types in a modern cloud environment?<\/span><\/p>\n

The most common hardware offload use case in a cloud environment is that of networking services.\u00a0 Each tenant in a cloud creates their own private network, yet the cloud infrastructure must transparently and securely service each customer.\u00a0 This is accomplished through building a SDN pipeline to carry out the required functions.<\/span><\/p>\n

A more interesting test, then, would be to define a SDN pipeline and implement it on each architecture to compare the performance across various packet sizes using TCP traffic.\u00a0 The first order of business would be to define a simple pipeline.\u00a0 For this let’s consider what is required to build a secure multi-tenant environment.\u00a0 First we need an encapsulation method to provide tenant isolation.\u00a0 In this case we will use VXLAN and as packets are parsed we match the VXLAN network ID (VNID) to identify the traffic in a specific cloud instance or network.\u00a0 Next we need to check the security entries for the customer network and provide connection tracking (L4 stateful firewall services). Then we need to do a lookup on the inner packet destination IP address and rewrite the inner packet destination MAC address accordingly.\u00a0 Finally we will need to re-encapsulate the packet in a VXLAN header and forward out the appropriate interface. <\/span><\/p>\n

\"\"<\/p>\n

Figure 2 – Test SDN Pipeline<\/span><\/em><\/h5>\n

 <\/p>\n

In order to understand how the Pensando architecture compares to that of NVIDIA\u2019s BlueField-2 we built an environment to measure the basic performance of this simple pipeline between two bare metal servers.\u00a0 This testing was independently validated by<\/span> Principled Technologies<\/span><\/a>; a more detailed report including setup and configuration can be found here: Principled Technologies Report (PDF)<\/a><\/span>.<\/span><\/p>\n

For this test we compared the latest commercially available products, namely the Pensando DSC-200 and NVIDIA\u2019s ConnectX-6 Dx, both running at 100G.<\/span><\/p>\n

 <\/p>\n

Why not the BlueField-2?<\/strong><\/p>\n

Despite numerous attempts we have been unable to purchase any BlueField-2 100G DPUs from NVIDIA or their reseller channel.\u00a0 We have, however, been able to get access to multiple ConnectX-6 Dx cards, which are what NVIDIA uses as the packet processing engine in BlueField-2, so we have focused this set of tests on that.\u00a0\u00a0<\/span><\/p>\n

This is the point where I expect you to point out that this is just another case of apples and oranges, but bear with me:\u00a0 <\/span>The ConnectX-6 Dx ASIC is exactly the same as that used in BlueField-2<\/b>.\u00a0 In addition, we are leveraging the cores in the host to replace those in the BlueField-2 and, we\u2019ve stacked the deck a little in favor of NVIDIA by enabling four times the number of cores found in BlueField-2.\u00a0 Before we go any further: if anyone can provide myself or Principled Technologies with a BlueField-2 100G, we can do an exact apples to apples comparison.<\/span><\/p>\n

To summarize:<\/b> The tests we are running compare the performance of the Pensando DSC-200 and NVIDIA BlueField-2 packet engines.\u00a0 Since we do not have a BlueField-2 DPU we are leveraging the processing function of the host system\u2019s 32 cores (four times the cores available with the BlueField-2 100G) which provides a more favorable outcome for packets that are not offloaded by the hardware.<\/span><\/p>\n

That\u2019s Enough Fine Print, Let\u2019s Get Back To The Testing<\/strong><\/p>\n

For the Pensando DSC, the SDN pipeline was loaded into the firmware of the card and executed on the P4 processing engines.\u00a0 For the NVIDIA ConnectX-6 Dx the SDN pipeline was built using OVS on the target server.\u00a0 Figure 3 shows the basic setup where Server 1 is the sender and Server 2 is the target server. <\/span><\/p>\n

\"\"<\/span><\/h4>\n
Figure 3 – Testbed Setup (DUT – Device Under Test)<\/span><\/em><\/h5>\n

 <\/p>\n

We used a kernel-based tool (iperf3) to generate TCP traffic in order to approximate the type of traffic that may be generated by applications running on two hosts in a provider environment.\u00a0 Because of this, these results do not measure or represent the limits of either architecture, but instead show how each architecture performs when subjected to various packet sizes running traffic that approximates that of a kernel-based application.<\/span><\/p>\n

What we discovered was that for lower packet sizes the CX-6 Dx architecture performed well for traffic that was accelerated by hardware offload. \u00a0 However, as the packet size increased above 256 bytes, the DSC-200 pulled away, performing up to 1.5X better than the NVIDIA ASIC.\u00a0\u00a0<\/span><\/p>\n

Another noteworthy measurement was for traffic that was not offloaded. For traffic that could not be hardware accelerated, the DSC-200 performed up to <\/span>thirteen times<\/b> better than the CX-6 Dx setup.\u00a0 This is due to the nature of the architecture: the DSC handled all of the stateful firewall services including connection tracking in the P4 engines in hardware. Even though the CX-6 Dx supports hardware offload for connection tracking, we were unable to make this work, despite using the latest drivers and software available for the setup at the time of testing.\u00a0 This was likely due to the dependency between the SDN software and the hardware.\u00a0 Even though the hardware capabilities exist for the CX-6 Dx, the software must be able to program the flow. <\/span>When either the software or the hardware is incapable of performing such functions, the packet processing falls back on the CPU cores, which do not match the performance of the ASIC and therefore negatively affect performance.<\/b><\/p>\n

\"\"<\/p>\n

Figure 4 – Test Results<\/span><\/em><\/h5>\n

 <\/p>\n

Why Does This Matter?<\/b><\/p>\n

This brings us back to our original point about architectural differentiation and the value of Software-in-Silicon.\u00a0 The graph above visually highlights the differing performance seen between architectures when workloads are accelerated\u00a0 versus being processed via a sea of cores.\u00a0 The Pensando Software-in-Silicon approach means that we can add support for new protocols and services while maintaining a superior level of performance.\u00a0 Other platforms and vendors will leave you with a stark choice: either live with reduced performance or deploy new hardware (which typically takes 18-24 months to develop, let alone qualify and deploy).\u00a0 This can be seen in the evolution of BlueField products:\u00a0 BlueField (CX-5), BlueField-2 (CX-6), & BlueField-3 (CX-7).\u00a0<\/span><\/p>\n

This is the key differentiator between the architectures.<\/b>\u00a0 The CX-6 Dx (and by extension BlueField 2) architecture relies on hardware offload, it is built on a static ASIC, an API, compatible drivers, and software that can push flows to the ASIC. This approach introduces a number of compromises, most notably:<\/span><\/p>\n

    \n
  1. The offload functionality must be built into the chip;<\/span><\/li>\n
  2. Software that runs on the host CPU or Arm cores must be able to use that functionality.<\/span><\/li>\n<\/ol>\n

    If either condition is not met, then packet processing occurs on the cores, which greatly reduces performance.\u00a0<\/b><\/p>\n

    For Pensando, that functionality is programmed in the P4 packet engines.\u00a0 There is minimal dependency on software running on the Arm cores and new functionality can easily be programmed into the silicon.\u00a0\u00a0<\/span><\/p>\n

    Summary<\/b><\/p>\n

    Performance testing and benchmarking provides a valuable data point when evaluating technology, but it is important to look at the methodology and translate that into real word applications.\u00a0 What really matters when comparing DPUs like BlueField and the Pensando DSC\u00a0 is their ability to perform across varying traffic sizes while providing complex SDN pipelines, security, and storage services <\/span>simultaneously<\/span><\/i>.\u00a0\u00a0<\/span><\/p>\n

     <\/p>\n

    The differentiator for Pensando is Software-in-Silicon, which provides a customizable hardware acceleration engine.\u00a0 This enables Pensando customers to run packet processing software developed specifically for their business use cases as if it was built in silicon.\u00a0 This innovation eliminates the delay in feature development created by the legacy model where packet processing functions are built into the ASIC and enabled through third party software like OVS. \u00a0 It also greatly improves performance by eliminating the need to utilize cores to provide packet processing for services that cannot be accelerated or offloaded by the ASIC.<\/span><\/p>\n

     <\/p>\n

    As we continue to build solutions at Pensando we will also continue to look for meaningful ways to demonstrate the architectural superiority of our platform through performance testing. In the next blog we will look at combining multiple services (SDN + Storage) in a cloud environment, as deployed by many cloud providers.<\/span><\/p>\n

     <\/p>\n

    One Final Note<\/strong><\/p>\n

    Performance testing in general tends to be a very emotive subject, in particular when its used as a means to compare products.\u00a0 If you’re interested in having a look at the test platforms and potentially even seeing the tests or running them yourself, let us know via the Contact form below and we’ll be happy to get something scheduled.<\/p>\n","protected":false},"excerpt":{"rendered":"

    This is the first in a series of upcoming articles that will focus on performance<\/p>\n","protected":false},"author":21,"featured_media":2665,"comment_status":"open","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[6,8,9],"tags":[],"class_list":["post-2633","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-enterprise","category-networking","category-p4"],"acf":[],"yoast_head":"\nThe Confusing World of Vendor Benchmarking: Part One - Apples, Oranges, and Eggs - Pensando<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Confusing World of Vendor Benchmarking: Part One - Apples, Oranges, and Eggs - Pensando\" \/>\n<meta property=\"og:description\" content=\"This is the first in a series of upcoming articles that will focus on performance\" \/>\n<meta property=\"og:url\" content=\"https:\/\/share.mahalodigital.com\/Pensando\/apples-oranges-and-eggs\/\" \/>\n<meta property=\"og:site_name\" content=\"Pensando\" \/>\n<meta property=\"article:published_time\" content=\"2022-01-18T21:00:33+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2022-03-14T19:10:14+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/share.mahalodigital.com\/Pensando\/wp-content\/uploads\/2022\/01\/GettyImages-603710963.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"819\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Heather Morris\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Heather Morris\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"12 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/share.mahalodigital.com\\\/Pensando\\\/apples-oranges-and-eggs\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/share.mahalodigital.com\\\/Pensando\\\/apples-oranges-and-eggs\\\/\"},\"author\":{\"name\":\"Heather Morris\",\"@id\":\"https:\\\/\\\/share.mahalodigital.com\\\/Pensando\\\/#\\\/schema\\\/person\\\/4694712aaa17f4ab09864e0d7aefd0fc\"},\"headline\":\"The Confusing World of Vendor Benchmarking: Part One – Apples, Oranges, and Eggs\",\"datePublished\":\"2022-01-18T21:00:33+00:00\",\"dateModified\":\"2022-03-14T19:10:14+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/share.mahalodigital.com\\\/Pensando\\\/apples-oranges-and-eggs\\\/\"},\"wordCount\":2404,\"commentCount\":0,\"image\":{\"@id\":\"https:\\\/\\\/share.mahalodigital.com\\\/Pensando\\\/apples-oranges-and-eggs\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/share.mahalodigital.com\\\/Pensando\\\/wp-content\\\/uploads\\\/2022\\\/01\\\/GettyImages-603710963.jpg\",\"articleSection\":[\"Enterprise\",\"Networking\",\"P4\"],\"inLanguage\":\"en\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/share.mahalodigital.com\\\/Pensando\\\/apples-oranges-and-eggs\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/share.mahalodigital.com\\\/Pensando\\\/apples-oranges-and-eggs\\\/\",\"url\":\"https:\\\/\\\/share.mahalodigital.com\\\/Pensando\\\/apples-oranges-and-eggs\\\/\",\"name\":\"The Confusing World of Vendor Benchmarking: Part One - 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