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AMD 2nd Gen EPYC (Rome) Application Performance on vSphere Series: Part 2 – VMmark

In recently published benchmarks with VMware VMmark, we’ve seen lots of great results with the AMD EPYC 7002 Series (known as 2nd Gen EPYC, or “Rome”) by several of our partners. These results show how well a mixed workload environment with many virtual machines and infrastructure operations like vMotion can perform with new server platforms.

This is the second part of our series covering application performance on VMware vSphere running with AMD 2nd Gen EPYC processors (also see Part 1 of this series). This post focuses on the VMmark benchmark used as an application workload.

We used the following hardware and software in our testbed:

  • AMD 2nd Gen EPYC processors (“Rome”)
  • Dell EMC XtremIO all-flash array
  • VMware vSphere 6.7 U3
  • VMware VMmark 3.1


VMmark is a benchmark widely used to study virtualization performance. Many VMware partners have used this benchmark—from the initial release of VMmark 1.0 in 2006, up to the current release of VMmark 3.1 in 2019—to publish official results that you can find on the VMmark 3.x results site. This long history and large set of results can give you an understanding of performance on platforms you’re considering using in your datacenters. AMD EPYC 7002–based systems have done well and, in some cases, have established leadership results. At the publishing of this blog, VMmark results on AMD EPYC 7002 have been published by HPE, Dell | EMC, and Lenovo.

If you look though the details of these published results, you can find some interesting information like disclosed configuration options or settings that might provide performance improvements to your environment. For example, in the details of a Dell |EMC result, you can find that the server BIOS setting Numa Nodes Per Socket (NPS) was set to 4. And in the HPE submission, you can find that the server BIOS setting Last Level Cache (LLC) as NUMA Node was set to enabled. This is also referred to as CCX as NUMA because each CCX has its own LLC. (CCX is described in the following section.)

We put together a VMmark test setup using AMD EPYC Rome 2-socket servers to evaluate performance of these systems internally. This gave us the opportunity to see how the NPS and CCX as NUMA BIOS settings affected performance for this workload.

In the initial post of this series, we looked at the effect of the NPS BIOS settings on a database-specific workload and described the specifics of those settings. We also provided a brief overview and links to additional related resources. This post builds on that one, so we highly recommend reading the earlier post if you’re not already familiar with the BIOS NPS settings and the details for the AMD EPYC 7002 series.


Each EPYC processor is made up of up to 8 Core Complex Dies (CCDs) that are connected by AMD’s Infinity fabric (figure 1). Inside each CCD, there are two Core Complexes (CCXs) that each have their own LLC cache shown as 16M L3 cache in figure 2. These diagrams (the same ones from the previous blog post) are helpful in illustrating these aspects of the chips.

Figure 1. Logical diagram of AMD EPYC Rome processor

Figure 2. Logical diagram of CCX

The CCX as NUMA or LLC as NUMA BIOS settings can be configured on most of the AMD EPYC 7002 Series processor–based servers. The specific name of the setting will be slightly different for different server vendors. When enabled, the server will present the four cores that share each L3 cache as a NUMA node. In the case of the EPYC 7742 (Rome) processors used in our testing, there are 8 CCDs that each have 2 CCXs for a total 16 CCXs per processor. With CCX as NUMA enabled, each processor is presented as 16 NUMA nodes, with 32 NUMA nodes total for our 2-socket server. This is quite different from the default setting of 1 NUMA node per socket for a total of 2 NUMA nodes for the host.

This setting has some potential to improve performance by exposing the architecture of the processors as individual NUMA nodes, and one of the VMmark result details indicated that it might have been a factor in improving the performance for the benchmark.

VMmark 3 Testing

For this performance study, we used as the basis for all tests:

  • Two 2-socket systems with AMD EPYC 7742 processors and 1 TB of memory
  • 1 Dell EMC XtremIO all-flash array
  • VMware vSphere 6.7 U3 installed on a local NVMe disk
  • VMmark 3.1—we ran all tests with 14 VMmark tiles: that’s the maximum number of tiles that the default configuration could handle without failing quality of service (QoS). For more information about tiles, see “Unique Tile-Based Implementation” on the VMmark product page.

We tested the default cases first: CCX as NUMA disabled and Numa Per Socket (NPS) set to 1. We then tested the configurations of NPS 2 and 4, both with and without CCX as NUMA enabled.  Figure 3 below shows the results with the VMmark score and the average host CPU utilization.  VMmark scores are reported relative to the score achieved by the default settings of NPS 1 and CCX as NUMA disabled.

From a high level, the VMmark score and the overall performance of the benchmark does not change a large amount across all of the test cases. These fall within the 1-2% run-to-run variation we see with this benchmark and can be considered equivalent scores. We saw the lowest results with NPS 2 and NPS 4 settings. These results showed 3% and 5% reductions respectively, indicating that those settings don’t give good performance in our environment.

Figure 3. VMmark 3 performance on AMD 2nd Gen EPYC with NPS and CCX as NUMA Settings

We observed one clear trend in the results: a lower CPU utilization in the 6% to 8% range with CCX as NUMA enabled.  This shows that there are some small efficiency gains with CCX as NUMA in our test environment. We didn’t see a significant improvement in overall performance due to these efficiency gains; however, this might allow an additional tile to run on the cluster (we didn’t test this).  While some small gains in efficiency are possible with these settings, we don’t recommend moving away from the default settings for general-purpose, mixed-workload environments.  Instead, you should evaluate these advanced settings for specific applications before using them.

AMD EPYC Rome Application Performance on vSphere Series: Part 1 – SQL Server 2019

Exciting new server platforms based on the second generation of AMD EPYC processors (Rome) have become recently available from many of our hardware partners. The new Rome processors offer up to 64 cores per socket—that’s a big increase over the previous generation of AMD processors. This means that a two-socket server using these processors has 128 cores and 256 logical threads with simultaneous multi-threading (SMT) enabled, making two-socket servers look more like four-socket servers in terms of core counts.

This is the first blog in a series that will take at look at the performance of some different workloads on the AMD EPYC Rome processor on VMware vSphere. Today we’re giving you the results of our tests on Microsoft SQL Server 2019.

The AMD EPYC Rome processor is built with Core Complex Dies (CCDs) connected via Infinity Fabric. In total, there are up to eight CCDs in the EPYC 7002 processor (Rome), as shown in figure 1.

Figure 1. Logical diagram of AMD EPYC Rome processor

Two Core Complexes (CCXs) comprise each CCD.  A CCX is up to four cores sharing an L3 cache, as shown in this additional logical diagram from AMD for a CCD, where the orange line separates the two CCXs.

Figure 2. Logical diagram of CCD

The AMD EPYC 7002 series processors in some ways simplify the architecture for many applications, including virtualized and private cloud deployment. There are more details on the EPYC Rome processor as well as a comparison to the previous generation AMD EPYC processors in a great article written by Anandtech.

AMD EPYC 7002 series (Rome) server processors are fully supported for vSphere 6.5 U3, vSphere 6.7 U3, and vSphere 7.0.  For all tests in this blog, vSphere 6.7 U3 was used.

The server used for testing here was a two-socket system with AMD EPYC 7742 processors and 1 TB of memory. Storage was an all flash ExtremeIO Fibre Channel array with a 4TB LUN assigned to the test system. vSphere 6.7 U3 was installed on a local NVMe disk and used as the basis for all tests.

Testing with SQL Server 2019

Microsoft SQL Server 2019 is the current version of this popular relational database.  It’s widely used by VMware customers and is one of the most commonly used applications on the vSphere platform. It’s a good application to test the performance of both large- and medium-sized virtual machines.

For the test, we used the SQL Server workload of the DVD Store 3 benchmark. It’s an open-source online transaction processing (OLTP) workload that simulates an online store.  It uses many common database features such as indexes, foreign keys, stored procedures, and transactions.  The workload is measured in terms of orders per minute (OPM), where each order is made up of logging in, browsing the store, reading and rating reviews, adding items to the shopping cart, and purchasing them.

For all tests, the number of worker threads that simulated users were increased in successive test runs until the maximum OPM was achieved and then began to decline, or stay the same, as additional threads are added.  At this point, CPU utilization was between 90 and 100 percent.

We created a Windows Server 2019 VM and installed SQL Server 2019 on it.  For the later tests this VM was cloned multiple times to be able to quickly scale-out the test setup.

Scale Up Performance of a Monster VM

With such a large number of cores available, it was natural to test how much performance was possible when scaling up to the maximum size of vCPUs per VM (a Monster VM). We configured the scaled up VM with 512 GB of RAM and a DVD Store test database of about 400GB.

We compared the maximum throughput for 64 and 128 vCPU VMs and found good scalability. The 128 vCPU VM achieved 1.86 times the throughput of the 64 vCPU VM.  This small fall off in scalability is due to the additional NUMA node, which results in some increased latency. Additionally, the sheer number of cores involved in such large systems caused slightly higher overhead to manage for the vSphere scheduler.

Figure 3. Scale-up performance from 64 vCPUs to 128 vCPUs for a single VM.


Scale-Out Performance of Multiple VMs

To test the scale-out performance of a vSphere environment, we cloned the SQL Server 2019 VM until we had eight.  We configured each VM to have 16 vCPUs with 128 GB of RAM.  This allowed us to have a maximum number of active vCPUs in the test to be equal to the 128 cores in the server.  Additionally, we configured the size of the DVD Store test database to be about 100GB.  We did this to scale the workload to the size of the VM.

The results below show that the total throughput continues to increase as the number of VMs is increased to eight.  In total, the eight VMs were able to produce slightly over 6x what a single VM could achieve.

Figure 4. As we scaled out the 16-vCPU VM from 1 to 2, 4, and 8 VMs, we observed the eight VMs were able to produce slightly over 6x what a single VM could achieve.

Optimizing Performance Opportunities with AMD EPYC Rome

As mentioned at the beginning of this post, AMD EPYC Rome processors used in this test are made up of eight CCD modules, each with 8 cores.  Within each CCD there are two CCXs that share an L3 processor cache. Each CCD has an associated memory controller.  With default settings, all eight CCDs and their memory controllers act as one NUMA node with memory access interleaved across all memory controllers.

There is an option in the BIOS settings to partition the processor into multiple NUMA domains per socket.  This partitioning is based on grouping the CCDs and their associated memory controllers.  The option is referred to as NUMA per socket or NPS, and the default is 1.  This means that there is one NUMA node per socket.  The other options are to configure it to 2 or 4.  In the case where NPS is set to 4, there are 4 NUMA nodes per socket, with each NUMA node having 2 CCDs and their associated memory.

If the VM sizes allow for them to align with the NPS setting in terms of cores and memory, then there is the opportunity for performance gains with some workloads.  In the specific case of our scale-out performance testing that we looked at above, there were 8 SQL Server VMs with 16 vCPUs and 128 GB of RAM each.  This lines up with an NPS 4 setting – 1 VM per NUMA node with 16 vCPUs matching 16 cores per NUMA node.  Additionally, there is 128 GB of RAM for each VM as well as 128 GB of RAM in each NPS 4–based NUMA node for our system with 1TB of RAM.

When tested, this configuration of VMs with such nice alignment resulted in a 7.8% gain in throughput for the NPS 4 setting over the default of NPS 1.  NPS 2 showed only a negligible gain of 1%.

Figure 5. Because of good alignment, the NPS 4 setting gained 7.8% in throughput over the NPS 1 setting, compared to the NPS 2 setting, which showed only a 1% performance improvement.

It is important to note that not all workloads and VMs will gain 8% or even any performance just by using the NPS 4 setting.  The performance gain in this case is due to the clean alignment of the VMs with NPS 4. Compared to NPS 1, where multiple VMs were probably not confined across their own set of caches and were stepping on other VM’s cache usage.  In this specific scenario with NPS 4, each VM basically has its own NUMA node with its own set of L3 processor caches and lower memory latency due to the interleaving across only the local memory for the CCDs being used.  In circumstances where VM size is uniform and nicely aligns with one of these NPS settings, it is possible to obtain some modest performance gains.  Please use these settings with caution and test their effect before using them in production.



New White Paper: Optimize Virtualized Deep Learning Performance with New Intel Architectures

By Dave Jaffe, VMware Performance Engineering and Padma Apparao, Intel – VMware Center of

A new white paper is available showing the advantages of running deep learning image classification on the 2nd Generation Intel Xeon Scalable processor compared to previous Intel processors, and to show the performance benefits of running on the VMware vSphere hypervisor compared to bare metal.

The 2nd Generation Intel Xeon Scalable processor’s Deep Learning Boost technology includes new Vector Neural Network Instructions (VNNI), which are especially performant with input data expressed as an 8-bit integer (int8) rather than a 32-bit floating point number (fp32). Together with the large VNNI registers, these instructions provide a marked performance improvement in image classification over the previous generation of Intel Xeon Scalable processors.

The latest version of vSphere, 7.0, supports the VNNI instructions. The work reported in this paper demonstrates a very small virtualization overhead for single image inferencing but major performance advantages for properly configured virtualized servers compared to the same servers running as bare metal.

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Weathervane 2.0: An Application-Level Performance Benchmark for Kubernetes

Weathervane 2.0 lets you evaluate and compare the performance characteristics of on-premises and cloud-based Kubernetes clusters.

Kubernetes simplifies the deployment and management of applications on a wide range of infrastructure solutions. One cost of this simplification is a new set of decisions that you must make when acquiring or configuring Kubernetes clusters, including selecting the underlying infrastructure, configuring network and storage layers, sizing compute nodes, etc. Each choice can impact the performance of applications deployed on the cluster. Weathervane 2.0 helps you understand this impact, by:

  • Comparing the performance of Kubernetes clusters
  • Evaluating the impact of configuration decisions on cluster performance
  • Validating a new cluster before putting it into production

When using Weathervane, you only need to build a set of container images, edit a configuration file, and start the benchmark. Weathervane manages the deployment, execution, and tear-down of all benchmark components on your Kubernetes cluster.

Weathervane 2.0 is available at https://github.com/vmware/weathervane.

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Using VMmark 3 as a Performance Analysis Tool

VMmark was originally developed to fill the need for a server consolidation benchmark for a rapidly changing datacenter that was becoming increasingly dominated by virtualization.  The design of VMmark, which is a collection of workloads, gives us the ability to quickly change workload parameters to modify the behavior of the entire benchmark. This allows us to use VMmark to exercise technologies that were not available at the time the benchmark was designed. The VMmark 3 run rules provide for academic or research results publication using a modified version of the benchmark.

VMmark 3 was designed in 2015 when the memory size of a typical high-end 2 socket server was 768 GB.  Each VMmark 3 tile was configured to use 156 GB of memory, allowing multiple tiles to be run on each server.  A new technology, Intel Optane DC Persistent Memory, now allows up to 3 TB of memory in a 2 socket server, with plans to increase that even further.  Testing the performance of this technology with an unmodified version of VMmark 3 wouldn’t be easy as we’d saturate CPU resources long before we could fully exercise this large amount of memory.  Thankfully the flexible nature of VMmark allows us to modify it to consume significantly more memory with minimal changes in CPU usage.

The two primary VMmark workloads are Weathervane and DVD Store.  Each can be modified to consume more memory.  Weathervane, as configured for VMmark 3, uses 14 VMs.  Thus while it would be possible to modify this application, doing so would be a time-consuming process.  We therefore decided to look at DVD Store, which uses only four VMs.  Most of the work is done in the DVD Store database VM which was our target for modification.

Determining the best configuration for DVD Store to utilize a larger amount of memory required multiple iterations of testing.   We modified one test parameter of the DVD Store workload, and then examined the results to determine the effect on the VMmark tile. We were looking for larger memory usage with a minimal increase in CPU usage so that we could exercise the larger memory configuration without requiring additional CPUs. The following table lists the default configuration and the variables we changed:

Parameter Default Configurations Tried
VM Memory Size 32 GB 128, 250 and 385 GB
Think Time 1 second 0.5, 0.9, 1.25, and 1.5 seconds
Number of Threads 24 36 and 48
Number of Searches 3 5, 7, and 9
Batch Search Size 3 5, 7, and 9
Database Size 100 GB 300 and 500 GB

The final configuration that we determined to have the most increased memory usage while keeping the CPU usage moderate was 250 GB DS3DB VM memory size, 1.5 seconds think time, and 300 GB database size.  All other parameters were kept at the default.

The following table lists the CPU and memory utilization of the default configuration and the “increased memory” configuration.

Configuration CPU Utilization Memory Utilization
Default 26.3 126 GB
Increased Memory 24.1 350 GB

We were able to almost triple the memory consumption of a single VMmark tile without increasing the CPU usage. Using this “increased memory” configuration for VMmark we can now see the effect of the additional memory provided by Intel Optane DC Persistent Memory in Memory Mode.

More detailed information about this configuration and the methodology used to refine it can be found in the Intel Optane DC Persistent Memory whitepaper.  Detailed instructions to configure VMmark 3 to increase the memory footprint can be obtained by emailing the VMmark team at vmmark-info@vmware.com.  We encourage you to experiment with VMmark under academic rules for your own studies and to let us know if you have any questions.


vSphere 6.7 Update 3 Supports AMD EPYC™ Generation 2 Processors, VMmark Showcases Its Leadership Performance

Two leadership VMmark benchmark results have been published with AMD EPYC™ Generation 2 processors running VMware vSphere 6.7 Update 3 on a two-node two-socket cluster and a four-node cluster. VMware worked closely with AMD to enable support for AMD EPYC™ Generation 2 in the VMware vSphere 6.7 U3 release.

The VMmark benchmark is a free tool used by hardware vendors and others to measure the performance, scalability, and power consumption of virtualization platforms and has become the standard by which the performance of virtualization platforms is evaluated.

The new AMD EPYC™ Generation 2 performance results can be found here and here.

View all VMmark results
Learn more about VMmark
These benchmark result claims are valid as of the date of writing.

Introducing VMmark ML

VMmark has been the go-to virtualization benchmark for over 12 years. It’s been used by partners, customers, and internally in a wide variety of technical applications. VMmark1, released in 2007, was the de-facto virtualization consolidation benchmark in a time when the overhead and feasibility of virtualization was still largely in question. In 2010, as server consolidation became less of an “if” and more of a “when,” VMmark2 introduced more of the rich vSphere feature set by incorporating infrastructure workloads (VMotion, Storage VMotion, and Clone & Deploy) alongside complex application workloads like DVD Store. Fast forward to 2017, and we released VMmark3, which builds on the previous versions by integrating an easy automation deployment service alongside complex multi-tier modern application workloads like Weathervane. To date, across all generations, we’ve had nearly 300 VMmark result publications (297 at the time of this writing) and countless internal performance studies.

Unsurprisingly, tech industry environments have continued to evolve, and so must the benchmarks we use to measure them. It’s in this vein that the VMware VMmark performance team has begun experimenting with other use cases that don’t quite fit the “traditional” VMmark benchmark. One example of a non-traditional use is Machine Learning and its execution within Kubernetes clusters. At the time of this writing, nearly 9% of the VMworld 2019 US sessions are about ML and Kubernetes. As such, we thought this might be a good time to provide an early teaser to VMmark ML and even point you at a couple of other performance-centric Machine Learning opportunities at VMworld 2019 US.

Although it’s very early in the VMmark ML development cycle, we understand that there’s a need for push-button-easy, vSphere-based Machine Learning performance analysis. As an added bonus, our prototype runs within Kubernetes, which we believe to be well-suited for this type of performance analysis.

Our internal-only VMmark ML prototype is currently streamlined to efficiently perform a limited number of operations very well as we work with partners, customers, and internal teams on how VMmark ML should be exercised. It is able to:

  1. Rapidly deploy Kubernetes within a vSphere environment.
  2. Deploy a variety of containerized ML workloads within our newly created VMmark ML Kubernetes cluster.
  3. Execute these ML workloads either in isolation or concurrently to determine the performance impact of architectural, hardware, and software design decisions.

VMmark ML development is still very fluid right now, but we decided to test some of these concepts/assumptions in a “real-world” situation. I’m fortunate to work alongside long-time DVD Store author and Big Data guru Dave Jaffe on VMmark ML.  As he and Sr. Technical Marketing Architect Justin Murray were preparing for their VMworld US talk, “High-Performance Virtualized Spark Clusters on Kubernetes for Deep Learning [BCA1563BU]“, we thought this would be a good opportunity to experiment with VMmark ML. Dave was able to use the VMmark ML prototype to deploy a 4-node Kubernetes cluster onto a single vSphere host with a 2nd-Generation Intel® Xeon® Scalable processor (“Cascade Lake”) CPU. VMmark ML then pulled a previously stored Docker container with several MLperf workloads contained within it. Finally, as a concurrent execution exercise, these workloads were run simultaneously, pushing the CPU utilization of the server above 80%. Additionally, Dave is speaking about vSphere Deep Learning performance in his talk “Optimize Virtualized Deep Learning Performance with New Intel Architectures [MLA1594BU],“ where he and Intel Principal Engineer Padma Apparao explore the benefits of Vector Neural Network Instructions (VNNI). I definitely recommend either of these talks if you want a deep dive into the details of VNNI or Spark analysis.

Another great opportunity to learn about VMware Performance team efforts within the Machine Learning space is to attend the Hands-on-Lab Expert Lead Workshop, “Launch Your Machine Learning Workloads in Minutes on VMware vSphere [ELW-2048-01-EMT_U],” or take the accompanying lab. This is being led by another VMmark ML team member Uday Kurkure along with Staff Global Solutions Consultant Kenyon Hensler. (Sign up for the Expert Lead using the VMworld 2019 mobile application or on my.vmworld.com.)

Our goal after VMworld 2019 US is to continue discussions with partners, customers, and internal teams about how a benchmark like VMmark ML would be most useful. We also hope to complete our integration of Spark within Kubernetes on vSphere and reproduce some of the performance analysis done to date. Stay tuned to the performance blog for additional posts and details as they become available.

New Scheduler Option for vSphere 6.7 U2

Along with the recent release of VMware vSphere 6.7 U2, we published a new whitepaper that shows the performance of a new scheduler option that was included in the 6.7 U2 update.  We referred to this new scheduler option internally as the “sibling” scheduler, but the official name is the side-channel aware scheduler version 2, or SCAv2.  The whitepaper includes full details about SCAv1 and SCAv2, the L1TF security vulnerability that made them necessary, and the performance implications with several different workload types.  This blog is a brief overview of the key points, but we recommend that you check out the full document.

In August of 2018, a security vulnerability known as L1TF, affecting systems using Intel processors, was revealed, and patches and remediations were also made available. Intel provided micro-code updates for its processors, operating system patches were made available, and VMware provided an update for vSphere. The full details of the vCenter and ESXi patches are in a VMware security advisory that links to individual KB articles.

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First VMmark 3.1 Publications, Featuring New Cascade Lake Processors

VMmark is a free tool used by hardware vendors and others to measure the performance, scalability, and power consumption of virtualization platforms.  If you’re unfamiliar with VMmark 3.x, each tile is a grouping of 19 virtual machines (VMs) simultaneously running diverse workloads commonly found in today’s data centers, including a scalable Web simulation, an E-commerce simulation (with backend database VMs), and standby/idle VMs.

As Joshua mentioned in a recent blog post, we released VMmark 3.1 in February, adding support for persistent memory, improving workload scalability, and better reflecting secure customer environments by increasing side-channel vulnerability mitigation requirements.

I’m happy to announce that today we published the first VMmark 3.1 results.  These results were obtained on systems meeting our industry-leading side-channel-aware mitigation requirements, thus continuing the benchmark’s ability to provide an indication of real-world performance.

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IoT Analytics Benchmark adds neural network–based deep learning with Keras and BigDL

The IoT Analytics Benchmark released last year dealt with an important Internet of Things use case—monitoring factory sensor data for impending failure conditions. This year, we are tackling an equally important use case—image classification. Whether used in facial recognition, license plate readers, inspection systems, or autonomous vehicles, neural network–based deep learning is making image detection and classification a viable technology.

As in the classic machine learning used in the original IoT Analytics Benchmark code (which used the Spark Machine Learning Library), the new deep learning code first trains a model using pre-labeled images and then deploys that model to infer the classification of new images. For IoT this inference step is the most important. Thus, the new programs, designated as IoT Analytics Benchmark DL, use previously trained models (included in the kit) to demonstrate inferencing that can be performed at the edge (on small gateway systems) or in scaled-out Spark clusters.

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