Dell Technologies has released the new PowerEdge R750xa server, a GPU workload-based platform that is designed to support artificial intelligence, machine learning, and high-performance computing solutions. The dual socket/2U platform supports 3^rd Gen Intel Xeon processors (code named Ice Lake). It supports up to 40 cores per processor, has eight memory channels per CPU, and up to 32 DDR4 DIMMs at 3200 MT/s DIMM speed. This server can accommodate up to four double-width PCIe GPUs that are located in the front left and the front right of the server.

Compared with the previous generation PowerEdge C4140 and PowerEdge R740 GPU platform options, the new PowerEdge R750xa server supports larger storage capacity, provides more flexible GPU offerings, and improves the thermal requirement

Figure 1 PowerEdge R750xa server

The NVIDIA A100 GPUs are built on the NVIDIA Ampere architecture to enable double precision workloads. This blog evaluates the new PowerEdge R750xa server and compares its performance with the previous generation PowerEdge C4140 server.

The following table shows the specifications for the NVIDIA GPU that is discussed in this blog and compares the performance improvement from the previous generation.

Table 1 NVIDIA GPU specifications

*with sparsity

Test bed and applications

This blog quantifies the performance improvement of the GPUs with the new PowerEdge GPU platform.

Using a single node PowerEdge R750xa server in the Dell HPC & AI Innovation Lab, we derived all results presented in this blog from this test bed. This section describes the test bed and the applications that were evaluated as part of the study. The following table provides test environment details:

Table 2 Server configuration

The following table provides information about the applications and benchmarks used:

Table 3 Benchmark and application details

LAMMPS

Large-Scale Atomic/Molecular Massively Parallel simulator (LAMMPS) is distributed by Sandia National Labs and the US Department of Energy. LAMMPS is open-source code that has different accelerated models for performance on CPUs and GPUs. For our test, we compiled the binary using the KOKKOS package, which runs efficiently on GPUs.

Figure 2 LAMMPS Performance on PowerEdge R750xa and PowerEdge C4140 servers

With the newer generation GPUs, this application improves 2.4 times compared to single GPU performance. The overall performance from a single server improved twice with the PowerEdge R750xa server and NVIDIA A100 GPUs.

GROMACS

GROMACS is a free and open-source parallel molecular dynamics package designed for simulations of biochemical molecules such as proteins, lipids, and nucleic acids. It is used by a wide variety of researchers, particularly for biomolecular and chemistry simulations. GROMACS supports all the usual algorithms expected from modern molecular dynamics implementation. It is open-source software with the latest versions available under the GNU Lesser General Public License (LGPL).

Figure 3 GROMACS performance on PowerEdge C4140 and r750xa servers

With the newer generation GPUs, this application improved approximately 1.5 times across the dataset compared to single GPU performance. The overall performance from a single server improved 1.5 times with a PowerEdge R750xa server and NVIDIA A100 GPUs.

High-Performance Linpack

High-Performance Linpack (HPL) needs no introduction in the HPC arena. It is a widely used standard benchmark tests in the industry.  

 Figure 4 HPL Performance on the PowerEdge R750xa server with A100 GPU and PowerEdge C4140 server with V100 GPU

Figure 5 Power use of the HPL running on NVIDIA GPUs

From Figure 4 and Figure 5, the following results were observed: 

• Performance—For GPU count, the NVIDIA A100 GPU demonstrates twice the performance of the NVIDIA V100 GPU. Higher memory size, double precision FLOPS, and a newer architecture contribute to the improvement for the NVIDIA A100 GPU.
• Scalability—The PowerEdge R750xa server with four NVIDIA A100-PCIe-40 GB GPUs delivers 3.6 times higher HPL performance compared to one NVIDIA A100-PCIE-40 GB GPU. The NVIDIA A100 GPUs scale well inside the PowerEdge R750xa server for the HPL benchmark.  
• Higher Rpeak—The HPL code on NVIDIA A100 GPUs uses the new double-precision Tensor cores. The theoretical peak for each GPU is 19.5 TFlops, as opposed to 9.7 TFlops. 
• Power—Figure 5 shows power consumption of a complete HPL run with the PowerEdge R750xa server using four A100-PCIe GPUs. This result was measured with iDRAC commands, and the peak power consumption was observed as 2022 Watts. Based on our previous observations, we know that the PowerEdge C4140 server consumes approximately 1800 W of power.

HPCG

Figure 6 Scaling GPU performance data for HPCG Benchmark

As discussed in other blogs, high performance conjugate gradient (HPCG) is another standard benchmark to test data access patterns of sparse matrix calculations. From the graph, we see that the HPCG benchmark scales well with this benchmark resulting in 1.6 times performance improvement over the previous generation PowerEdge C4140 server with an NVIDIA V100 GPU.

The 72 percent improvement in memory bandwidth of the NVIDIA A100 GPU over the NVIDIA V100 GPU contributes to the performance improvement.

Conclusion

In this blog, we introduced the latest generation PowerEdge R750xa platform and discussed the performance improvement over the previous generation PowerEdge C4140 server. The PowerEdge R750xa server is a good option for customers looking for an Intel Xeon scalable CPU-based platform powered with NVIDIA GPUs.

With the newer generation PowerEdge R750xa server and NVIDIA A100 GPUs, the applications discussed in this blog show significant performance improvement.

Next steps

In future blogs, we plan to evaluate NVLINK bridge support, which is another important feature of the PowerEdge R750xa server and NVIDIA A100 GPUs.

Running containerized applications on Microsoft Azure’s hybrid ecosystem

 Introduction

A vast array of services and tooling has evolved in support of microservices and container-based application development patterns.  One indispensable asset in the technology value stream found in most of these patterns is Kubernetes (K8s).  Technology professionals like K8s because it has become the de-facto standard for container orchestration. Business leaders like it for its potential to help disrupt their chosen marketplace.   However, deploying and maintaining a Kubernetes cluster and its complimentary technologies can be a daunting task to the uninitiated.  

Enter Microsoft Azure’s portfolio of services, tools, and documented guidance for developing and maintaining containerized applications.  Microsoft continues to invest heavily in simplifying this application modernization journey without sacrificing features and functionality.  The differentiators of the Microsoft approach are two-fold.   First, the applications can be hosted wherever the business requirements dictate – i.e. public cloud, on-premises, or spanning both.  More importantly, there is a single control plane, Azure Resource Manager (ARM), for managing and governing these highly distributed applications.       

In this blog series, we share the results of hands-on testing in the Dell Technologies labs with container-related services that span both Public Azure and on-premises with Azure Stack Hub.  Azure Stack Hub provides a discrete instance of ARM, which allows us to leverage a consistent control plane even in environments with no connectivity to the Internet.  It might be helpful to review articles rationalizing the myriad of announcements made at Microsoft Ignite 2019 about Microsoft’s hybrid approach from industry experts like Kenny Lowe, Thomas Maurer, and Mary Branscombe before delving into the hands-on activities in this blog.

Services available in Public Azure

Azure Kubernetes Service (AKS) is a fully managed platform service hosted in Public Azure.  AKS makes it simple to define, deploy, debug, and upgrade even the most complex Kubernetes applications.  With AKS, organizations can accelerate past the effort of deploying and maintaining the clusters to leveraging the clusters as target platforms for their CI/CD pipelines.  DevOps professionals only need to concern themselves with the management and maintenance of the K8s agent nodes and leave the management of the master nodes to Public Azure.  

AKS is just one of Public Azure’s container-related services.  Azure Monitor, Azure Active Directory, and Kubernetes role-based access controls (RBAC) provide the critical governance needed to successfully operate AKS.  Serverless Kubernetes using Azure Container Instances (ACI) can add compute capacity without any concern about the underlying infrastructure.  In fact, ACI can be used to elastically burst from AKS clusters when workload demand spikes.  Azure Container Registry (ACR) delivers a fully managed private registry for storing, securing, and replicating container images and artifacts.  This is perfect for organizations that do not want to store container images in publicly available registries like Docker Hub.

Leveraging the hybrid approach

Microsoft is working diligently to deliver the fully managed AKS resource provider to Azure Stack Hub.   The first step in this journey is to use AKS engine to bootstrap K8s clusters on Azure Stack Hub.  AKS engine provides a command-line tool that helps you create, upgrade, scale, and maintain clusters.  Customers interested in running production-grade and fully supported self-managed K8s clusters on Azure Stack Hub will want to use AKS engine for deployment and not the Kubernetes Cluster (preview) marketplace gallery item.  This marketplace item is only for demonstration and POC purposes.

AKS engine can also upgrade and scale the K8s cluster it deployed on Azure Stack Hub.  However, unlike the fully managed AKS in Public Azure, the master nodes and the agent nodes need to be maintained by the Azure Stack Hub operator.  In other words, this is not a fully managed solution today.  The same warning applies to the self-hosted Docker Container Registry that can be deployed to an on-premises Azure Stack Hub via a QuickStart template.  Unlike ACR in Public Azure, Azure Stack Hub operators must consider backup and recovery of the images.  They would also need to deploy new versions of the QuickStart template as they become available to upgrade the OS or the container registry itself.

If no requirements prohibit the sending of monitoring data to Public Azure and the proper connectivity exists, Azure Monitor for containers can be leveraged for feature-rich monitoring of the K8s clusters deployed on Azure Stack Hub with AKS engine.   In addition, Azure Arc for Data Services can be leveraged to run containerized images of Azure SQL Managed Instances or Azure PostgreSQL Hyperscale on this same K8s cluster.  The Azure Monitor and Azure Arc for Data Services options would not be available in submarine scenarios where there would be no connectivity to Azure whatsoever.   In the disconnected scenario, the customer would have to determine how best to monitor and run data services on their K8s cluster independent of Public Azure.   

Here is a summary of the articles in this blog post series:

Part 1: Running containerized applications on Microsoft Azure’s hybrid ecosystem – Provides an overview of the concepts covered in the blog series.

Part 2:  Deploy K8s Cluster into an Azure Stack Hub user subscription – Setup an AKS engine client VM, deploy a cluster using AKS engine, and onboard the cluster to Azure Monitor for Containers.

Part 3:  Deploy a self-hosted Docker Container Registry – Use one of the Azure Stack Hub QuickStart templates to setup container registry and push images to this registry. Then, pull these images from the registry into the K8s cluster deployed with AKS engine in Part 2.