Removing the barriers to hybrid-cloud flexibility for data analytics
Wed, 20 Jan 2021 18:46:33 -0000|
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The fundamental tasks of collecting data, storing data, and providing processing power for data analytics is getting more difficult. Increasing data volumes along with the number of remote data sources and the rapidly evolving options for extracting valuable information make forecasting needs challenging and investment risky. IT organizations need the ability to quickly provision resources and incrementally scale both compute and storage on-demand as need develops. The three largest hyper-scale cloud providers all offer a wide range of infrastructure, platform and analytics “as-a-service” but all require vastly different skill sets, security models, and connectivity investments. Organizations interested in having hybrid cloud flexibility for data analytics are forced to choose a single cloud partner or add significant IT complexity by managing multiple options with no common toolset. In this Solutions Insight, we describe how the Robin Cloud-Native Platform (CNP) hosted onsite with Dell EMC PowerEdge servers provide application and infrastructure topology awareness to streamline the provisioning and life cycle management of your data applications with true hybrid cloud flexibility.
Providing a robust self-service experience
Data analytics professionals want easy access to internally managed provisioning of resources for experimentation and development without complex interactions with IT. Many of these professionals have experience with self-service portals that work for a single cloud service but have not yet had any hybrid cloud flexibility. Robin provides a rich out-of-the-box portal capability that IT can offer to developers, data engineers, and data scientists. Data professionals save valuable development time at each stage of the application lifecycle by leveraging the automation framework of Robin. IT gets a fully functional automation framework for hosting many popular enterprise applications on the Robin Platform. The Robin platform comes out-of-the-box with cluster-aware application bundles including relational databases, big data, NoSQL, and several AI/ML tools.
Robin leverages cloud-native technologies such as Kubernetes and Docker to modernize the management of your data analytics infrastructure. The Robin Kubernetes-based architecture gives you complete freedom and offers a consistent self-service capability to provision and move workloads across private and/or public clouds. Native integration between Kubernetes, storage, network, and application management layer enables full automation managing both clusters and applications with all the advantages of a true hybrid cloud experience. Robin has built-in the capability to create managed application snapshots that enable cloning, backup, and migration of applications between on-prem and cloud or between datacenters within an enterprise. Robin fully automates the end-to-end cluster provisioning process for the most challenging platform deployments including Cloudera, Apache Spark, Kafka, TensorFlow, Pytorch, Kubeflow, Scikit-learn, Caffe, Torch, and even custom application configurations.
Organizations that adopt the Robin platform benefit from accelerated deployment and simplified management of complex applications that can be provisioned by end-users through a familiar portal experience and true hybrid cloud flexibility.
Moving from self-service sandboxes to enterprise scale
We described above how the Robin platform benefits both data and IT professionals that want a full-featured self-service data analytics capability with true hybrid cloud operations by layering additional platform awareness and automation to cloud-native technologies such as Kubernetes and Docker. Organizations can start with small deployments, and as applications grow, they can add more resources. Robin can be deployed on the full range of Dell EMC PowerEdge servers with a custom mix of memory, storage, and accelerator options making it easy to scale-out by adding additional servers with the right capabilities to match changing resource demands. The Robin management console provides a single interface to expand existing deployments and/or add new clusters. Consolidation of multiple workloads under Robin management can also improve hardware utilization without compromising SLAs or QoS. The Robin platform provides multi-tenancy with fine-grained Role Based Access Control (RBAC) enabling safe resource sharing on fewer clusters. Applications can be incubated on multi-tenancy, mixed application clusters and then easily migrated to production class clusters hosting one or multiple mission-critical applications using Robin backup and restore capability across clusters and/or clouds.
While open-source Kubernetes has become the de facto platform for deploying on-demand applications, there remains a need for additional investment by organizations that need multi-cluster production deployments and service orchestration that can automate and manage day-0 and day-n lifecycle operations at scale. The Robin Automation Platform combines simplicity, usability, performance, and scale with a modern UI to provide bare metal, cluster, and application-as-a-service for both infrastructure and service orchestration. With Robin Bare Metal-as-a-Service, hundreds of thousands of bare-metal servers can be provisioned with specific BIOS, firmware, OS and other software packages or configurations depending on the needs of the application. With Robin, it is equally easy to manage upgrades, as well as a wide array of PowerEdge server options including firmware, OS, and application software across container platforms.
Automating day-n operations for stateful applications
Several priorities are driving interest in running stateful applications on Kubernetes. These include operational consistency, extending agility of containerization to data, faster collaboration, and the need for simplifying the delivery of data services. Robin solves the storage and network persistency challenges in Kubernetes to enable its use in the provisioning, management, high availability and fault tolerance of mission-critical stateful applications.
Creating a persistent storage volume for a single container is becoming a routine operation. However, when it comes to provisioning storage for complex stateful applications that span multiple pods and services, it requires automation of the cluster resources coordinated with storage management. Managing the changing requirements of stateful applications on a day-to-day basis requires data and storage management services such as snapshotting, backup, and cloning. Traditionally, this capability has resided only on high-end storage systems managed by the IT storage administrator teams. In order to provide true self-service capabilities to data professionals, organizations need simple storage and data management solution for Kubernetes that hides all the above complexities and provides simple commands that are developer-friendly and can easily be incorporated into development and production workflows.
With Robin CNP, analytics and DevOps teams can be self-sufficient while managing complex stateful applications without requiring specific storage expertise. Data management is supported with a Robin managed CSI-compliant block storage access layer with bare-metal performance. Storage management seamlessly integrates with Kubernetes-native administrative tooling such as Kubectl, Helm Charts, and Operators through standard APIs.
Robin CNP simplifies storage operations such as provisioning storage, ensuring data availability, maintaining low latency I/O performance, and detecting and repairing disk and I/O errors. Robin CNP also provides simple commands for data management operations such as backup/recovery, snapshots/rollback, and cloning of entire applications including data, metadata, and application configuration.
Robin CNP offers several improvements on the networking layer over open-source Kubernetes. These improvements are required to run enterprise-scale data and network-centric applications on Kubernetes. With Robin CNP developers/IT can set networking options while deploying applications and clusters in Kubernetes and preserve IP addresses during restarts and application migration. Robin’s flexible networking built on OVS and Calico supports overlay networking. Robin also supports dual-stack (IPV4/IPV6).
IT organizations adopting the Robin platform benefit from a single approach to application and infrastructure management from experimentation to dev/test to a production environment that can span multiple clouds. Robin excels at managing heterogeneous infrastructure assets with a mix of compute, storage, and workload accelerators that can match the changing needs of fast-moving enterprise-wide demand for resources. Dell Technologies provides a wide range of PowerEdge rack servers with innovative designs to transform IT and maximize performance across the widest range of applications. PowerEdge servers match well with the three main types of infrastructure assets typically needed for a Robin managed implementation:
The PowerEdge R640 is the ideal dual-socket platform for dense scale-out data center computing.
The PowerEdge R740xd delivers a perfect balance between storage scalability and performance. The 2U two-socket platform is ideal for software defined storage.
The PowerEdge R740 was designed to accelerate application performance leveraging accelerator cards and storage scalability. The 2-socket, 2U platform has the optimum balance of resources to power the most demanding environments.
Up to two 2nd Generation Intel® Xeon® Scalable processors, up to 28 cores per processor
Up to 24 NVMe drives and a total of 32 x 2.5” or 18 x 3.5” drives in a 2U dual-socket platform.
The scalable business architecture of the R740 can scale up to three 300W or six 150W GPUs, or up to three double-width or four single-width FPGAs
24 DDR4 DIMM slots, Supports RDIMM /LRDIMM, speeds up to 2933MT/s, 3TB max
Up to 12 NVDIMM, 192 GB Max
Up to 12 Intel® Optane™ DC persistent memory DCPMM, 6.14TB max, (7.68TB max with DPCMM + LRDIMM)
Front bays: Up to 24 x 2.5” SAS/SATA (HDD/SSD), NVMe SSD max 184.32TB or up to 12 x 3.5” SAS/SATA HDD max 192TB
Mid bay: Up to 4 x 2.5”, max 30.72TB SAS/SATA (HDD/SSD), or up to 4 x 3.5” SAS/SATA HDD, max 64TB
Rear bays: Up to 4 x 2.5”, max 30.72TB SAS/SATA (HDD/SSD), or up to 2 x 3.5” SAS/SATA HDD max 32TB
Robin is the ideal platform for hosting both stateful and stateless applications with support for both virtual machines and Docker-based applications. It includes a storage layer that provides data services, including snapshots, clones, backup/restore, and replication that enable hybrid cloud and multi-cloud operations for stateful applications that are not possible with pure open-source cloud-native technologies.
With the Robin platform on Dell EMC PowerEdge servers, organizations can:
· Decouple and scale compute and storage independently
· Provision/Decommission compute only clusters within minutes for ephemeral workloads
· All operations can be fully integrated with simple API commands from your development and/or production workflows.
· Migrate data workloads among data centers and public clouds
· Provide self-service capability for developers and data scientists to improve productivity
· Eliminate planning delays, start small and dynamically scale-up/out nodes to meet demand
· Consolidate multiple workloads on shared infrastructure to improve hardware utilization
· Trade resources among application clusters to manage cyclical compute requirements and surges
This results in,
· Reduced Costs
· Delivering faster insights
· Future-proofing the enterprise
For more information
Dell Technologies and Robin Systems welcome your feedback on this article and the information presented herein. Contact the Dell Technologies Solutions team by email or provide your comments by completing our documentation survey.
You can also contact our regional sales teams for more information via email at the following addresses:
North America: email@example.com
Thank you for your interest.
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Comparison of Top Accelerators from Dell Technologies’ MLPerf™ Inference v3.0 Submission
Fri, 21 Apr 2023 21:43:39 -0000|
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Dell Technologies recently submitted results to MLPerfTM Inference v3.0 in the closed division. This blog highlights the NVIDIA H100 PCIe GPU and compares the results to the NVIDIA A100 PCIe GPU with the PCIe form factor held constant.
MLPerf Inference v3.0 submission falls under the benchmarking pillar of the MLCommonsTM consortium with the objective to make fair comparisons across server configurations. Submissions that are made to the closed division warrant an equitable comparison of the systems.
This blog highlights the closed division submissions Dell Technologies made with the NVIDIA A100 GPU using the PCIe (peripheral component interconnect express) form factor. The PCIe form factor is an interfacing standard for connecting various high-speed components in hardware such as a computer or a server. Servers include a certain number of PCIe slots in which to insert GPUs or other additional cards. Note that there are different physical configurations for the slots to indicate the number of lanes for data to travel to and from the PCIe card. The NVIDIA H100 GPU is truly the latest and greatest GPU with NVIDIA AI Enterprise included; it is a dual-slot air cooled PCIe generation 5.0 GPU. This GPU runs at a memory bandwidth speed of over 2,000 megabits per second and up to seven Multi-Instance GPUs at 10 gigabytes each. The NVIDIA A100 80 GB GPU is a dual-slot PCIe generation 4.0 GPU that runs at a memory bandwidth speed of over 2,000 megabits per second.
NVIDIA H100 PCIe GPU and NVIDIA A100 PCIe GPU comparison
In addition to making a submission with the NVIDIA A100 GPU, Dell Technologies made a submission with the NVIDIA H100 GPU. To make a fair comparison, the systems were identical and the PCIe form factor was held constant.
Dell PowerEdge R750xa (4x A100-PCIe-80GB, TensorRT)
Dell PowerEdge R750xa (4x H100-PCIe-80GB, TensorRT)
MLPerf System ID
Intel Xeon Gold 6338 CPU @ 2.00 GHz
GPU form factor
GPU memory configuration
Table 1: Software stack of submissions made on NVIDIA A100 PCIe and NVIDIA H100 PCIe GPUs for MLPerf Inference v3.0 on the Dell PowerEdge R750xa server
In the following figure, the per card numbers are normalized over the NVIDIA A100 GPU results to show a readable comparison of the GPUs on the same system. Across object detection, medical image segmentation, and speech to text and natural language processing, the latest NVIDIA H100 GPU outperforms its predecessor in all categories. Note the outstanding performance of the Dell PowerEdge R750xa server with NVIDIA H100 GPUs with the BERT benchmark in the high accuracy mode. With the advancements in generative artificial intelligence, the Dell PowerEdge R750xa server is a versatile, reliable, and high performing platform.
Figure 1: Normalized per GPU comparison of NVIDIA A100 and NVIDIA H100 GPUs on the Dell PowerEdge R750xa server
The following figures show absolute numbers for a comparison of the NVIDIA H100 and NVIDIA A100 GPUs.
Figure 2: Per GPU comparison of NVIDIA A100 and NVIDIA H100 GPUs for RetinaNet on the PowerEdge R750xa server
Figure 3: Per GPU comparison of NVIDIA A100 and NVIDIA H100 GPUs for 3D-Unet on the PowerEdge R750xa server
Figure 4: Per GPU comparison of NVIDIA A100 and NVIDIA H100 GPUs for RNNT on the PowerEdge R750xa server
Figure 5: Per GPU comparison of NVIDIA A100 and NVIDIA H100 GPUs for BERT on the PowerEdge R750xa server
These results can be found on the MLCommons website.
Submissions made with the NVIDIA A100 PCIe GPU
In this round of submissions, Dell Technologies submitted results on the PowerEdge R750xa server packaged with four NVIDIA A100 80 GB PCIe GPUs. In previous rounds, the PowerEdge R750xa server showed outstanding performance across all the benchmarks. For a deeper dive of a previous round's submission, check out our blog from MLPerf Inference v2.0. From the previous round of MLPerf Inference v2.1 submissions, Dell Technologies submitted results on an identical system. However, across the two rounds of submissions, the main difference is the upgrades in the software stack, as described in the following table:
Dell PowerEdge R750xa (4x A100-PCIe-80GB, TensorRT)
Dell PowerEdge R750xa (4x A100-PCIe-80GB, TensorRT)
MLPerf System ID
Intel Xeon Gold 6338 CPU @ 2.00 GHz
GPU form factor
GPU memory configuration
Table 2: Software stack for submissions made on the NVIDIA A100 PCIe GPU in MLPerf Inference v3.0 and v2.1
Comparison of PowerEdge R750xa NVIDIA A100 results from Inference v3.0 and v2.1
The RetinaNet benchmark falls under the object detection category and uses the OpenImages dataset. The results from Inference v3.0 show a less than 0.05 percent difference in the Server scenario and a 21.53 percent difference in the Offline scenario. A potential reason for this result might be NVIDIA’s optimizations, as outlined in their technical blog.
Figure 6: RetinaNet Server and Offline results on the PowerEdge R750xa server from Inference v3.0 and Inference v2.1
Medical image segmentation
The 3D-Unet benchmark performs the KiTS 2019 kidney tumor segmentation task. Across the two rounds of submission, the PowerEdge R750xa server performed consistently well with a 0.3 percent difference in both the default and high accuracy modes.
Figure 7: 3D-UNet Offline results on the PowerEdge R750xa server from Inference v3.0 and v2.1
Speech to text
The Recurrent Neural Network Transducers (RNNT) model falls under the speech recognition category. This benchmark accepts raw audio samples and produces the corresponding character transcription. In the Server scenario, the results are within a 2.25 percent difference and 0.41 percent difference in the Offline scenario.
Figure 8: RNNT Server and Offline results on the Dell PowerEdge R750xa server from Inference v3.0 and v2.1
Natural language processing
Bidirectional Encoder Representation from Transformers (BERT) is a state-of-the-art language representational model for Natural Language Processing applications. This benchmark performs the SQuAD question answering task. The BERT benchmark consists of default and high accuracy modes for the Offline and Server scenarios. For the Server scenarios, the default mode results are within a 1.69 percent range and 3.12 percent range for the high accuracy mode. For the Offline scenarios, a similar behavior is noticeable in which the default mode results are within a 0.86 percent range and 3.65 percent range in the high accuracy mode.
Figure 9: BERT Server and Offline results on the PowerEdge R750xa server from Inference v3.0 and v2.1
Across the various rounds of submissions to the MLPerf Inference benchmark suite, the PowerEdge R750xa server has been a consistent top performer for any machine learning tasks ranging from object detection, medical image segmentation, speech to text and natural language processing. The PowerEdge R750xa server continues to be an excellent server choice for machine learning inference workloads. Customers can take advantage of the diverse results submitted on the Dell PowerEdge R750xa server with the NVIDIA H100 GPU to make an informed decision for their specific solution needs.
Dell Servers Excel in MLPerf™ Inference 3.0 Performance
Fri, 07 Apr 2023 10:42:23 -0000|
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MLCommons has released the latest version (version 3.0) of MLPerf Inference results. Dell Technologies has been an MLCommons member and has been making submissions since the inception of the MLPerf Inference benchmark. Our latest results exhibit stellar performance from our servers and continue to shine in all areas of the benchmark including image classification, object detection, natural language processing, speech recognition, recommender system, and medical image segmentation. We encourage you to see our previous whitepaper about Inference v2.1, which introduces the MLCommons inference benchmark. AI and the most recent awareness of Generative AI, with application examples such as ChatGPT, have led to an increase in understanding about the performance objectives needed to enable customers with faster time-to-model and results. The latest results reflect the continued innovation that Dell Technologies brings to help customers achieve those performance objectives and speed up their initiatives to assess and support workloads including Generative AI in their enterprise.
What’s new with Inference 3.0?
New features for Inference 3.0 include:
- The inference benchmark rules did not make any significant changes. However, our submission has been expanded with the new generation of Dell PowerEdge servers:
- Our submission includes the new PowerEdge XE9680, XR7620, and XR5610 servers.
- Our results address new accelerators from our partners such as NVIDIA and Qualcomm.
- We submitted virtualized results with VMware running on NVIDIA AI Enterprise software and NVIDIA accelerators.
- Besides accelerator-based numbers, we submitted Intel-based CPU-only results.
Overview of results
Dell Technologies submitted 255 results across 27 different systems. The most outstanding results were generated from PowerEdge R750xa and XE9680 servers with the new H100 PCIe and SXM accelerators, respectively, as well as PowerEdge XR5610 and XR7620 servers with the L4 cards. Our overall NVIDIA-based results include the following accelerators:
- (New) Eight-way NVIDIA H100 Tensor Core GPU (SXM)
- (New) Four-way NVIDIA H100 Tensor Core GPU (PCIe)
- (New) Eight-way NVIDIA A100 Tensor Core GPU (SXM)
- Four-way NVIDIA A100 Tensor Core GPU (PCIe)
- NVIDIA A30 Tensor Core GPU
- (New) NVIDIA L4 Tensor Core GPU
- NVIDIA A2 GPU
- NVIDIA T4 GPU
We ran these accelerators on different PowerEdge XE9680, R750xa, R7525, XE8545, XR7620, XR5610 and other server configurations.
This variety of results across different servers, accelerators, and deep learning use cases allows customers to use them as datapoints to make purchasing decisions and set performance expectations.
Interesting Dell datapoints
The most interesting datapoints include:
- Among 21 submitters, Dell Technologies was one of the few companies that submitted results for all closed scenarios including data center, data center power, Edge, and Edge power.
- The PowerEdge XE9680 server procures the highest performance titles with RetinaNet Server and Offline, RNN-T Server, and BERT 99 Server benchmarks. It procures number 2 performance with Resnet Server and Offline, 3D-UNet Offline and 3D-UNet Offline 99.9, BERT 99 Offline, BERT 99.9 Server, and RNN-T Offline benchmarks.
- The PowerEdge XR5610 server procured highest system performance per watt with the NVIDIA L4 accelerator on the ResNet Single Stream, Resnet Multi Stream, RetinaNet Single Stream, RetinaNet Offline, RetinaNet Multi Stream, 3D-UNet 99, 3D-UNet 99.9 Offline, RNN-T Offline, Single Stream, BERT 99 Offline, BERT-99 Single Stream benchmarks.
- Our results included not only various systems, but also exceeded performance gains compared to the last round because of the newer generation of hardware acceleration from the newer server and accelerator.
- The Bert 99.9 benchmark was implemented with FP8 the first time. Because of the accuracy requirement, in the past, the Bert 99.9 benchmark was implemented with FP16 while all other models ran under INT8.
In the following figure, the BERT 99.9 v3.0 Offline scenario renders 843 percent more improvement compared to Inference v2.1. This result is due to the new PowerEdge XE9680 server, which is an eight–way NVIDIA H100 SXM system, compared to the previous PowerEdge XE8545 four-way NVIDIA A100 SXM. Also, the NVIDIA H100 GPU features a Transformer Engine with FP8 precision that speeds up results dramatically.
* MLPerf ID 2.1-0014 and MLPerf ID 3.0-0013
Figure 1: Performance gains from Inference v2.1 to Inference v3.0 due to the new system
Results at a glance
The following figure shows the system performance for Offline and Server scenarios. These results provide an overview as upcoming blogs will provide details about these results. High accuracy versions of the benchmark are included for DLRM and 3D-UNet as high accuracy version results are identical to the default version. For the BERT benchmark, both default and high accuracy versions are included as they are different.
Figure 2: System throughput for data center suite-submitted systems
The following figure shows the Single Stream and Multi Stream scenario latency for the ResNet, RetinaNet, 3D-UNet, RNN-T, and BERT-99 benchmarks. The lower the latency, the better the results.
Fig 3: Latency of the systems for different benchmark
Edge benchmark results include Single Stream, Multi Stream, and Offline scenarios. The following figure shows the offline scenario performance.
Figure 4: Offline Scenario system throughput for Edge suite
The preceding figures show that PowerEdge servers delivered excellent performance across various benchmarks and scenarios.
We have provided MLCommons-compliant submissions to the Inference 3.0 benchmark across various benchmarks and suites. These results indicate that with the newer generations of servers, such as the PowerEdge XE9680 server, and newer accelerators, such as the NVIDIA H100 GPU, customers can derive higher performance from their data center and edge deployments. Upgrading to newer hardware can yield between 304 and 843 percent improvement across all the benchmarks such as image classification, object detection, natural language processing, speech recognition, recommender systems, and medical image segmentation involved in the MLPerf inference benchmark. From our submissions for new servers such as the PowerEdge XR5610 and XR7620 servers with the NVIDIA L4 GPU, we see exceptional results. These results show that the new PowerEdge servers are an excellent edge platform choice. Furthermore, our variety of submissions to the benchmark can serve as a baseline to enable different performance expectations and cater to purchasing decisions. With these results, Dell Technologies can help fuel enterprises’ AI transformation, including Generative AI adoption, and deployment precisely and efficiently.