Reference Architecture: Acceleration over PCIe for Dell EMC PowerEdge MX7000
Wed, 12 Aug 2020 14:04:57 -0000
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Summary
Many of today's demanding applications require GPU resources. Our reference architecture incorporates GPUs to the PowerEdge MX infrastructure, utilizing the PowerEdge MX Scalable Fabric, Dell EMC DSS 8440 GPU Server, and Liqid Command Center Software. Request a remote demo of this reference architecture or a quote from Dell Technologies Design Solutions Experts at the Design Solutions Portal.
Background
The Dell EMC PowerEdge MX7000 Modular Chassis simplifies the deployment and management of today’s most challenging workloads by allowing IT administrators to dynamically assign, move and scale shared pools of compute, storage and networking resources. It provides IT administrators the ability to deliver fast results, eliminating managing and reconfiguring infrastructure to meet ever-changing needs of their end users. The addition of PCIe infrastructure to this managed pool of resources using Liqid technology designed on Dell EMC MX7000 expands the promise of software-defined composability for today’s AI-driven compute environments and high-value applications.
GPU Acceleration for PowerEdge MX7000
For workloads like AI that require parallel accelerated computing, the addition of GPU acceleration within the PowerEdge MX7000 is paramount. With Liqid technology and management software, GPUs of any form factor can be quickly added to any new or existing MX compute sled via the management interface, quickly delivering the resources needed to manage each step of the machine learning workflow including data ingest, cleansing, training, and inferencing. Spin-up new bare-metal servers with the exact number of accelerators required and then dynamically add or remove them as workload needs change.
Figure 1 Essential PowerEdge Expansion Components
GPU Expansion Over PCIe | |
Compute Sleds | Up to 8 x Compute Sleds per Chassis |
GPU Chassis | PCIe Expansion Chassis |
Interconnect | PCIe Gen3x4 Per Compute Sled |
GPU Expansion | 20x GPU (FHFL) |
GPU Supported | V100, A100, RTX, T4, Others |
OS Supported | Linux, Windows, VMWare and Others |
Devices Supported | GPU, FPGA, and NVMe Storage |
Form Factor | 14U Total = MX7000 (7U) + PCIe Expansion Chassis (7U) |
Figure 2
Figure 3 Liqid Command Center
Implementing GPU Expansion for MX
Figure 3 Liqid Command Center
GPUs are installed into the PCIe expansion chassis. Next, U.2 to four PCIe Gen3 adapters are added to each compute sled that requires GPU acceleration, and then they are connected to the expansion chassis (Figure 1). Liqid Command Center software enables discovery of all GPUs, making them ready to be added to the server over native PCIe. FPGA and NVMe storage can also be added to compute nodes in tandem. This PCIe expansion chassis & software are available from the Dell Design Solutions team.
Software Defined Composability
Once PCIe devices are connected to the MX7000, Liqid Command Center software enables the dynamic allocation of GPUs to MX compute sleds at the bare metal. Any amount of resources can be added to the compute sleds, via Liqid Command Center (GUI) or RESTful API, in any ratio (GPU hot-plug supported). To the operating system, the GPUs are presented as local resources direct connected to the MX compute sled over PCIe (Figure 3). All operating systems are supported including Linux, Windows, and VMware. As workload needs change, add or remove resources on the fly, via software including NVMe SSD and FPGA (Table 1).
Enabling GPU Peer-2-Peer Capability
A key feature included with the PCIe expansion solution for PowerEdge MX7000 is the ability for RDMA Peer-2-Peer between GPU devices. Direct RDMA transfers have a massive impact on both throughput and latency for the highest performing GPU-centric applications. Up to 10x improvement in performance has been achieved with RDMA Peer-2-Peer enabled. Below is the overview of how PCIe Peer-2-Peer functions (Figure 4).
Figure 4 PCIe Peer-2-Peer
Bypassing the x86 processor and enabling direct RDMA communication between GPUs, realizes a dramatic improvement in bandwidth and in addition a reduction in latency is also realized. This chart outlines the performance expected for GPUs that are composed to a single node with GPU RDMA Peer-2-Peer enabled (Table 2).
Application Level Performance
RDMA Peer-2-Peer is a key feature in GPU scaling for Artificial Intelligence, specifically machine learning based applications. Figure 5 outlines performance data measured on mainstream AI/ML applications on the MX7000 with GPU expansion over PCIe. It further demonstrates the performance scaling from 1-GPU to 8-GPU for a single MX740c compute sled. High scaling efficiency is observed for ResNet152, VGG16, Inception V3, and ResNet50 on MX7000 with composable PCIe GPUs measured with Peer-2-Peer enabled. These results indicate a near-linear growth pattern. and with the current capabilities of the Liqid PCIe 7U expansion sled one can allocate up to 20 GPUs to an application running on a single node.
Figure 5 GPU Performance Scaling Comparison: MX7000 Leverages RTX8000 in PCIe expansion chassis measured with P2P Enabled
Conclusion
Liqid PCIe expansion for the Dell EMC PowerEdge MX7000 unlocks the ability to manage the most demanding workloads in which accelerators are required for both new and existing deployments. Liqid collaborated with Dell Technologies Design Solutions to accelerate applications by through the addition of GPUs to the Dell EMC MX compute sleds over PCIe.
Learn More | See a Demo | Get a Quote
This reference architecture is available as part of the Dell Technologies Design Solutions.
Related Blog Posts
Direct from Development - Acceleration over Ethernet for Dell EMC PowerEdge MX7000
Mon, 09 Nov 2020 21:14:10 -0000
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Summary
Many of today’s demanding applications require GPU resources. This reference architecture incorporates GPUs to the PowerEdge MX infrastructure, utilizing the PowerEdge MX Scalable Fabric, Dell EMC DSS 8440 GPU Server and Liqid Command Center Software.
Request a remote demo of this reference architecture or a quote from Dell Technologies Design Solutions Experts at the Design Solutions Portal
Background
Emerging workloads, like AI represent a powerfully uneven series of compute processes, such as data-heavy ingest and GPU-heavy data training. When coupled with the fact that these workloads can demand even more resources over time, it becomes clear this complex new paradigm demands a new type of IT infrastructure.
Dell EMC PowerEdge MX7000 modular chassis simplifies the deployment and management of today’s challenging workloads by allowing IT to dynamically assign, move and scale shared pools of compute, storage and networking. It provides IT the ability to deliver fast results, not spend time managing and reconfiguring infrastructure to meet ever-changing needs. Composable GPU Infrastructure from Liqid powered by Dell Technologies expands the promise of software-defined composability for today’s AI-driven compute environments and high value applications.
GPU Acceleration for MX7000
For unique workloads like AI that require accelerated computing, the addition of GPU acceleration within the MX7000 is paramount. With Liqid, supported GPUs can be quickly added to any new or existing MX7000 compute sled, delivering the resources needed to effectively handle each step of the AI workflow including data ingest, cleaning/tagging, training, and inference. Spin-up new bare-metal servers with the exact number of GPUs required, and add or remove dynamically as needed, via Liqid software.
Essential PowerEdge Components and Ethernet Cabling
Liqid Command Center Software
The first step in the GPU expansion process, is to install up to 16x HHHL or 10x FHFL GPUs into a Dell EMC DSS 8440 server. As noted in the table 1, this solution supports several GPU device options. The next step is to connect the DSS 8440 to Fabric A on the MX7000 via 100GbE.
Liqid Command Center software resides on the fabric and will discover the GPU devices in the DS8440 and enable them for utilization by the MX7000 compute nodes. The users can distribute GPU-centric jobs from any compute sled on the MX7000 to GPUs located within the DSS 8440.
Accelerator Performance
To effectively demonstrate the performance of GPU accelerated MX7000 compute sleds, we tested it against DSS 8440 server with local GPUs and measured minimal to no overhead. The deep learning benchmark tests were run on the following networks: ResNet-50, ResNet-152, Inception V3, VGG-16. The DS8440 was outfitted with 8x NVDIA Tesla RTX8000 GPUs. The results clearly demonstrate that GPU enabled MX7000 delivers unrestricted performance on various industry standard benchmarks, using accelerator optimized Dell PowerEdge infrastructure.
In Conclusion
GPU expansion for the MX7000 unlocks the ability to handle the most demanding compute workloads for both new and existing AI and HPC deployments. Liqid Command Center on Dell EMC PowerEdge Servers accelerates applications by dynamically composing GPU resources directly to workloads without a power cycle on the compute sled.
Omnia: Open-source deployment of high-performance clusters to run simulation, AI, and data analytics workloads
Tue, 02 Feb 2021 16:07:10 -0000
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High Performance Computing (HPC), in which clusters of machines work together as one supercomputer, is changing the way we live and how we work. These clusters of CPU, memory, accelerators, and other resources help us forecast the weather and understand climate change, understand diseases, design new drugs and therapies, develop safe cars and planes, improve solar panels, and even simulate life and the evolution of the universe itself. The cluster architecture model that makes this compute-intensive research possible is also well suited for high performance data analytics (HPDA) and developing machine learning models. With the Big Data era in full swing and the Artificial Intelligence (AI) gold rush underway, we have seen marketing teams with their own Hadoop clusters attempting to transition to HPDA and finance teams managing their own GPU farms. Everyone has the same goals: to gain new, better insights faster by using HPDA and by developing advanced machine learning models using techniques such as deep learning and reinforcement learning. Today, everyone has a use for their own high-performance computing cluster. It’s the age of the clusters!
Today's AI-driven IT Headache: Siloed Clusters and Cluster Sprawl
Unfortunately, cluster sprawl has taken over our data centers and consumes inordinate amounts of IT resources. Large research organizations and businesses have a cluster for this and a cluster for that. Perhaps each group has a little “sandbox” cluster, or each type of workload has a different cluster. Many of these clusters look remarkably similar, but they each need a dedicated system administrator (or set of administrators), have different authorization credentials, different operating models, and sit in different racks in your data center. What if there was a way to bring them all together?
That’s why Dell Technologies started the Omnia project.
The Omnia Project
The Omnia project is an open-source initiative with a simple aim: To make consolidated infrastructure easy and painless to deploy using open open source and free use software. By bringing the best open source software tools together with the domain expertise of Dell Technologies' HPC & AI Innovation Lab, HPC & AI Centers of Excellence, and the broader HPC Community, Omnia gives customers decades of accumulated expertise in deploying state-of-the-art systems for HPC, AI, and Data Analytics – all in a set of easily executable Ansible playbooks. In a single day, a stack of servers, networking switches, and storage arrays can be transformed into one consolidated cluster for running all your HPC, AI, and Data Analytics workloads.
Omnia project logo
Simple by Design
Omnia’s design philosophy is simplicity. We look for the best, most straightforward approach to solving each task.
- Need to run the Slurm workload manager? Omnia assembles Ansible plays which build the right rpm files and deploy them correctly, making sure all the correct dependencies are installed and functional.
- Need to run the Kubernetes container orchestrator? Omnia takes advantage of community supported package repositories for Linux (currently CentOS) and automates all the steps for creating a functional multi-node Kubernetes cluster.
- Need a multi-user, interactive Python/R/Julia development environment? Omnia takes advantage of best-of-breed deployments for Kubernetes through Helm and OperatorHub, provides configuration files for dynamic and persistent storage, points to optimized containers in DockerHub, Nvidia GPU Cloud (NGC), or other container registries for unaccelerated and accelerated workloads, and automatically deploys machine learning platforms like Kubeflow.
Before we go through the process of building something from scratch, we will make sure there isn’t already a community actively maintaining that toolset. We’d rather leverage others' great work than reinvent the wheel.
Omnia installs software from various repositories, or compiles from source if appropriate, to create a cohesive software stack for doing parallel data analytics, AI, and simulation/modeling workloads on the same high performance infrastructureInclusive by Nature
Omnia’s contribution philosophy is inclusivity. From code and documentation updates to feature requests and bug reports, every user’s contributions are welcomed with open arms. We provide an open forum for conversations about feature ideas and potential implementation solutions, making use of issue threads on GitHub. And as the project grows and expands, we expect the technical governance committee to grow to include the top contributors and stakeholders from the community.
What's Next?
Omnia is just getting started. Right now, we can easily deploy Slurm and Kubernetes clusters from a stack of pre-provisioned, pre-networked servers, but our aim is higher than that. We are currently adding capabilities for performing bare-metal provisioning and supporting new and varying types of accelerators. In the future, we want to collect information from the iDRAC out-of-band management system on Dell EMC PowerEdge servers, configure Dell EMC PowerSwitch Ethernet switches, and much more.
What does the future hold? While we have plans in the near-term for additional feature integrations, we are looking to partner with the community to define and develop future integrations. Omnia will grow and develop based on community feedback and your contributions. In the end, the Omnia project will not only install and configure the open source software we at Dell Technologies think is important, but the software you – the community – want it to, as well! We can’t think of a better way for our customers to be able to easily setup clusters for HPC, AI, and HPDA workloads, all while leveraging the expertise of the entire Dell Technologies' HPC Community.
Omnia is available today on GitHub at https://github.com/dellhpc/omnia. Join the community now and help guide the design and development of the next generation of open-source consolidated cluster deployment tools!