Tag Archives: Xeon

Harnessing the Power of VMware vSAN and Intel Gen 4 Xeon Scalable Processors for Optimized AI Workloads

As the workloads of AI systems increase in complexity, more powerful and sophisticated infrastructure is required to support them. Modern applications of artificial intelligence necessitate improved speeds, dependability, scalability, and safety. Therefore, both corporations and academic institutions have made the search for the best platform to execute these demanding tasks a top priority.

Here we have a technological match made in heaven: VMware vSAN and Intel Gen 4 Xeon Scalable processors. This potent union creates a superb environment for processing AI workloads. Each has its own advantages, but when combined they provide a solid foundation for AI. Let’s find out!

Benefits of running AI workloads on VMware vSAN

The scalability and adaptability required by AI workloads are met by vSAN. It reduces overhead by making it easier to provision and manage storage resources. It provides high performance and availability and scales well in both directions. Key advantages include the following:

  1. Simplified Management: vSAN consolidates storage and compute into a single pool that can be administered with standard VMware tools like vSphere Client, vRealize Operations, and PowerCLI.
  2. Lower TCO: vSAN reduces TCO by doing away with the need for costly storage area network (SAN) or network-attached storage (NAS) devices by pooling together in-server, direct-attached storage devices.
  3. Scalability: Since AI workloads tend to grow unexpectedly, it’s important to have a platform that can easily scale to accommodate this growth, and vSAN provides this.
  4. Data Protection and Security: vSAN’s native vSphere integration means it can be used with vSphere Replication and Site Recovery Manager to provide encrypted data at rest and disaster recovery.

Advantages of Intel Gen 4 Xeon Scalable Processors

The new Intel Gen 4 Xeon Scalable processors have powerful artificial intelligence (AI) accelerators (AMX and DSA) built into their architecture.

  1. Advanced Matrix Extensions (AMX): The Xeon Scalable processor has had its capabilities enhanced with new matrix extensions designed specifically for artificial intelligence and high-performance computing. They provide more parallelism and vectorization, which improves the efficiency of deep learning and machine learning programmes.
  2. Data Streaming Accelerator (DSA): This is a hardware accelerator designed to process data quickly and with minimal delay. DSA is essential for processing the large amounts of data inherent in AI workloads due to its ability to improve compression, storage efficiency, and security.

The Perfect Synergy for AI Workloads

Companies can run AI workloads with confidence on a scalable, secure, and robust platform thanks to the combination of vSAN and Intel Gen 4 Xeon Scalable processors.

Businesses can quickly scale to meet the demands of AI applications thanks to the scalability, ease of management, and cost-effectiveness of vSAN and the AI-tailored hardware acceleration of Intel Gen 4 Xeon Scalable processors. In addition to providing an ideal platform for AI, this potent combination simplifies data management, reduces overhead, and boosts performance.

Additionally, sensitive data used in AI workloads is safeguarded with in-built security and encryption features, allowing for both regulatory compliance and peace of mind.

When put together, VMware vSAN and Intel Gen 4 Xeon Scalable processors create a highly reliable, fast, and scalable environment for AI workloads. Organizations can forge ahead with their AI initiatives with the assurance that their infrastructure can handle the rigours of AI by taking advantage of vSAN and the AMX and DSA accelerators on the Intel CPU.

Oracle performance on HPE Synergy, vSAN and Intel Optane

Image result for hpe synergy

I had the privilege recently to work with a customer who had asked HPE to perform some performance benchmarks not just with HCI Bench, but because they run quite a lot of Oracle workloads they wanted to determine if the performance of vSAN on HPe Synergy would be sufficient in order to run their workloads.

Whilst agreeing on the hardware specification, the customer had referenced my previous post on Optane™ Performance and had asked HPE to perform the tests using Optane™ as the cache tier in the Synergy configuration, this was not only to provide a superior performance experience, but it also would free up two capacity slots in the disk tray of the chassis per Synergy compute node meaning the customer could have more capacity.

Synergy Specification:

  • HPE Virtual Connect SE 40Gb F8 Module for Synergy
  • HPE Synergy D3940 Storage Module with SAS expanders
  • 3x HPE Synergy 480 Gen10 nodes, each equipped with:
  • 2x Intel® Xeon® Gold 6154 CPU @ 3.00GHz
  • 2x Intel® Optane™ 750 GB SSD DC P4800X Series PCIe (x4)
  • 768 GB Memory (24x 32 GB RDIMM @ 2666 MHz)
  • 2x Disk Group config with 1x Optane + 3x 800GB SAS per Disk Group
  • LACP based 40GbE interconnection between Compute Nodes

Please note: At the time of writing the 750GB U.2 Optane drives were undergoing certification for HPE Synergy.

In order to perform the Oracle workload testing HPE engaged with their own internal Oracle specialists to determine the correct workloads that needed to be performed, and with a target of <2ms specified by the customer they decided to use Kevin Closson’s SLOB tool, SLOB was configured in the following way:

  • 128 SLOB Schemas
  • Each Schema was 8GB in Size
  • Total of 1TB Test data

For the purpose of testing HPE decided that they would perform different tests in the following way:

  • (A) Single Oracle VM Instance with 128 Schemas, 70% Read, 30% Write
  • (B) Single Oracle VM Instance with 128 Schemas, 50% Read, 50% Write
  • (C&D) Single Oracle VM Instance with Heavy REDO activity and Large SGA and REDO_STRESS=Heavy, 50% Read, 50% Write, with 128 Schemas and 32 Schemas
  • (E) Single Oracle VM Instance
  • (F & G) 2 Parallel Oracle VM Instances with 64 / 128 Schemas Each, 70% Read, 30% Write

Before the tests were performed, and Oracle I/O Calibration was performed which is a feature of Oracle Databases and is used to assess the performance of the I/O subsystem by issuing an I/O intensive read-only workload in order to determine the maximum IOPS and throughput whilst maintaining close to 0ms latency.

Each test ran for 60 minutes in order to ensure enough data was filling up the write buffer, so let’s take a look at the results:

As you can see from the results, the target of <2ms was achieved successfully and at one point with two oracle VMs achieveing a staggering 250k IOPS at 1.305ms latency is very impressive across a 3-Node cluster, not only was the customer pleased with the results, but the Oracle Specialist within HPE said that the results exceeded their expectations also.

So as you can see, a composable infrastructure deployment of vSAN such as HPE Synergy with Intel Optane™ can still deliver the same levels of performance as standard rack mount servers, combined with VMware Cloud Foundation delivering a full SDDC package from both a hardware and software perspective.

Optane Performance

Many times over the past few months I have been asked about the benefits of using Intel Optane NVMe in a vSAN environment, although there was marketing material from Intel that boasted a good performance boost I decided (purely out of curiosity) to do some performance benchmarking and compare Optane as the cache devices versus SAS as the cache devices. The performance benchmark test used exactly the same servers and networking in order to provide a level playing field, the only thing that was changed was the cache devices being used in the disk groups.

Server Specification:

  • 6x Dell PowerEdge R730xd
  • Intel Xeon CPU E5-2630 v3 @ 2.40GHz
  • 128GB RAM
  • 2x Dell PERC H730 Controllers
  • 2x Intel Dual Port 10Gb ethernet adapters (Configured with LACP)

Disk group config for the SAS test:

  • 3x Disk Groups
  • 3x 400GB SAS SSD per disk group
  • 1x 400GB SAS SSD per disk group

Disk group config for the Optane test

  • 2x Disk Groups
  • 3x 400GB SAS SSD per disk group
  • 1x 750GB Optane NVMe P4800X per disk group

Whilst you could say that the configurations are not identical, since the Write Buffer is limited to 600GB per disk group then both configurations have the same amount of write buffer, the SAS config has more backend disks which would serve as an advantage.

For the purpose of the Benchmark, we used HCI Bench to automate the Oracle VDBench workload testing and each test was based on the following, the test was designed to max-out the system hence the high number of VMDKs used here (250)

  • 50 Virtual Machines
  • 5 VMDKs per virtual machine
  • 2 threads per VMDK
  • 20% working set
  • 4k, 8k, 16k, 32k, 64k and 128k block size
  • 0%, 30%, 70%, 100% write workload
  • 900 second test time for each test

So what were the results?

4K Blocksize:

8K Blocksize:

16K Blocksize:

32K Blocksize:

64K Blocksize:

128K Blocksize:

As you can see Optane really did boost the performance even though the server platform wasn’t the ideal platform for the Optane devices (Dell said those cards will not be certified in the 13G platform), however despite the fact that the workload was designed to max-out the system, in some cases latency was reduced to almost a third and throughput was was increased in some cases to 3x.

Conclusion: Optane really does live up to expectations, and it isn’t just marketing, I have yet to test a full NVMe system to see how much it can really be pushed, but I hope the numbers above go someway to convice you why you should consider optane as the cache tier in vSAN.