Dell  Technologies has participated in MLPerf submission for the past two years. The current submission is our fourth round to the MLPerf inference benchmarking suite.

This blog provides the latest MLPerf Inference v1.1 data center closed results on Dell EMC servers from our HPC & AI Innovation lab. The objective of this blog is to show optimal inference performance and performance/watt for the Dell EMC GPU servers (PowerEdge R750xa, DSS8440, and PowerEdge R7525). A blog about MLPerf Inference v1.0 performance can be found here. This blog also addresses the benchmarks rules, constraints, and submission categories. We recommend that you read it to become familiar with the MLPerf terminologies and rules.

Noteworthy results

Our noteworthy results include:

• The DSS8440 server (10 x A100-PCIE-80GB, TensorRT) yields Number One results across all the submitters for:
  • BERT 99 Offline and Server
  • BERT 99.9 Offline and Server
  • RNN-T Offline and Server
  • SSD-Resnet34 Offline and Server
• The R750xa server (4 x A100-PCIE-80GB, TensorRT) yields Number One results per PCIe accelerator for:
  • 3D UNet Offline and 3D UNet 99.9 Offline
  • Resnet50 Offline and Resnet50 Server
  • BERT 99 Offline and BERT 99 Server
  • BERT 99.9 Offline and BERT 99.9 Server
  • DLRM 99 Offline and DLRM Server
  • DLRM 99.9 Offline and DLRM 99.9 Server
  • RNN-T Offline and RNN-T Server
  • SSD-Resnet34 Offline and SSD-Resnet34 Server
• The R750xa server (4 x A100-PCIE-80GB, MIG) yields Number One results per PCIe accelerator MIG results for:
  • Resnet50 Offline and Resnet50 Server
  • BERT 99 Offline and BERT 99 Server
  • BERT 99.9 Offline and BERT 99.9 Server
  • SSD-Resnet34 Offline and SSD-Reset34 Server
• The R750xa server (4 x A100-PCIE-80GB, Triton) yields Number One results per PCIe accelerator Triton results for:
  • 3D UNet Offline and 3D UNet 99.9 Offline
  • Resnet50 Offline and Resnet50 Server
  • BERT 99 Server
  • BERT 99.9 Offline and BERT 99.9 Server
  • DLRM 99 Offline and DLRM Server
  • DLRM 99.9 Offline and DLRM 99.9 Server

To allow the like-to-like comparison of Dell Technologies results, we chose to test under the Datacenter closed division, as shown in this blog. Customers and partners can rely on our results, all of which  MLCommons^TM  has officially certified. Officially certified results are peer reviewed, have undergone compliance tests, and conform to the constraints enforced by MLCommons. If wanted, customers and partners can also reproduce our results. The blog that explains how to run MLPerf Inference v1.1 can be found here.

What is new?

The difference between MLPerf inference v1.1 and MLPerf inference v1.0 is that the Multistream scenario is deprecated. All other benchmarks and rules remain the same as for MLPerf inference v1.0.

For v1.1 submissions to MLCommons, over 1700 results were submitted. The number of submitters increased from 17 to 21.

Dell Technologies result submissions included new SUT configurations such as NVIDIA A100 Tensor Core 80GB GPU with 300 W TDP, A30, A100-MIG, and power results with NVIDIA-Certified R750xa servers.

MLPerf Inference 1.1 benchmark results

The following graphs include performance metrics for the Offline and Server scenarios. Overall, Dell Technologies results included approximately 200 performance results and 80 performance and power results. These results serve as a reference point to enable sizing deep learning clusters. The higher number of results in our submission helps further fine tune answers to specific questions that customers might have.

For the Offline scenario, the performance metric is Offline samples per second. For the Server scenario, the performance metric is queries per second (QPS). In general, the metrics represent throughput. A higher throughput is a better result. In the following graphs, the Y axis is an exponentially scaled axis representing the throughput and the X axis represents the SUTs and their corresponding models (described in the appendix).

Figures 1, 2, and 3  show the performance of different Dell EMC servers that were benchmarked for the different models. All these servers performed optimally and rendered high throughput. The backends included NVIDIA Triton, NVIDIA TensorRT on Offline and Server scenarios. Some of the results shown in figures 1 and 3 include MIG numbers.

Figure 1: Resnet50, BERT default, and high accuracy results

Figure 2: RNN-T, DLRM default, and high accuracy results

Figure 3: SSD-Resnet34, 3D-UNet default, and high accuracy results

Figure 4 shows the performance of the Dell EMC R750xa server that was benchmarked for the 3D-UNet, BERT 99, BERT 99.9, Resnet and SSD-Resnet34 models. The SUT provided high throughput while maintaining low power consumption. Higher throughputs were achieved with similar power usage for different models. These throughputs established our results in the optimal performance and optimal performance per watt category.

Figure 4:  Performance and power submission with inference v1.1 with R750xa and 4 x NVIDIA A100–40G

Observations about results from Dell Technologies

All the preceding results are officially submitted to the MLCommons^TM consortium and verified. Submissions include performance and power-related numbers. Dell Technologies submissions include approximately 200 performance results and 80 performance and power results.

Different types of workload tasks such as image classification, object detection, medical image segmentation, speech to text, language processing, and recommendation were a part of these results, which were promising. These models met the quality-of-service targets as expected by the MLCommons consortium.

With different kinds of GPUs such as the NVIDIA A30 Tensor Core GPU, different NVIDIA A100 variants such as A100 40 GB PCIe and A100 80 GB PCIe, and different CPUs from AMD and Intel, Dell EMC servers performed with optimal performance and power results. Other Dell EMC SUT configuration results for the NVIDIA A40, RTX8000, and T4 GPUs can be found in the v1.0 results, which can be used for comparison with the v1.1 results.

The submission included results from different inference backends such as NVIDIA TensorRT, , and Multi-Instance GPU (MIG). The appendix includes a summary of the NVIDIA software stack.

All our systems are air-cooled. This feature allows data center administrators to perform minimal to no changes to accommodate these systems while delivering high throughput inference performance. Furthermore, Dell EMC servers offer high performance per watt more effectively without adding significant power constraints.

Conclusion

In this blog, we quantified the MLCommons inference v1.1 performance on different Dell EMC servers such as DSS8440 and PowerEdge R750xa and R7525 servers, producing many results. Customers can use these results to address the relative inference performance delivered by these servers. Dell EMC servers are powerful compute machines that deliver high throughput inference capabilities for customers inferencing requirements across different scenarios and workload types.

Next steps

In future blogs, we plan to describe:

• How to run MLPerf Inference v1.1
• The R750xa server as a platform for inference v1.1
• The DSS8440 server as a platform for inference v1.1
• Comparison of inference v1.0 performance with inference v1.1 performance

Appendix

NVIDIA software stack

NVIDIA Triton Inference Server is open-source software that aids the deployment of AI models at scale in production. It is an inferencing solution optimized for both CPUs and GPUs. Triton supports an HTTP/REST and GRPC protocol that allows remote clients to request inferencing for any model that the server manages. It adds support for multiple deep learning frameworks, enables high-performance inference, and is designed to consider IT, DevOps, and MLOps.

NVIDIA TensorRT  is an SDK for high-performance, deep learning inference that includes an inference optimizer and runtime. It enables developers to import trained models from all major deep learning frameworks and optimizes them for deployment with the highest throughput and lowest latency, while preserving the accuracy of predictions. TensorRT-optimized applications perform up to 40 times faster on NVIDIA GPUs than CPU-only platforms during inference.

MIG can partition the A100 GPU into as many as seven instances, each fully isolated with their own high-bandwidth memory, cache, and compute cores. Administrators can support every workload, from the smallest to the largest, offering a right-sized GPU with guaranteed quality of service (QoS) for every job, optimizing utilization, and extending the reach of accelerated computing resources to every user. 

SUT configurations

We selected servers with different types of NVIDIA GPUs as our SUT to conduct data center inference benchmarks. The following tables list the MLPerf system configurations for these servers.

Note: In the following tables, the main difference in the software stack is the use of NVIDIA Triton Inference Servers.

Table 3: MLPerf system configurations for Dell EMC DSS 8440 servers

Table 4: MLPerf system configurations for PowerEdge servers

Abstract

Dell Technologies recently submitted results to the MLPerf Inference v2.0 benchmark suite. This blog examines the results of two specialty edge servers: the Dell PowerEdge XE2420 server with the NVIDIA T4 Tensor Core GPU and the Dell PowerEdge XR12 server with the NVIDIA A2 Tensor Core GPU.

Introduction

It is 6:00 am on a Saturday morning. You drag yourself out of bed, splash water on your face, brush your hair, and head to your dimly lit kitchen for a bite to eat before your morning run. Today, you have decided to explore a new part of the neighborhood because your dog’s nose needs new bushes to sniff. As you wait for your bagel to toast, you ask your voice assistant “what’s the weather like?” Within a couple of seconds, you know that you need to grab an extra layer because there is a slight chance of rain. Edge computing has saved your morning run.

Although this use case is covered in the MLPerf Mobile benchmarks, the data discussed in this blog is from the MLPerf Inference benchmark that has been run on Dell servers.

Edge computing is computing that takes place at the “edge of networks.” Edge of networks refers to where devices such as phones, tablets, laptops, smart speakers, and even industrial robots can access the rest of the network. In this case, smart speakers can perform speech-to-text recognition to offload processing that ordinarily must be accomplished in the cloud. This offloading not only improves response time but also decreases the amount of sensitive data that is sent and stored in the cloud. The scope for edge computing expands far beyond voice assistants with use cases including autonomous vehicles, 5G mobile computing, smart cities, security, and more.

The Dell PowerEdge XE2420 and PowerEdge XR 12 servers are designed for edge computing workloads. The design criteria is based on real life scenarios such as extreme heat, dust, and vibration from factory floors, for example. However, despite these servers not being physically located in a data center, server reliability and performance are not compromised.

PowerEdge XE2420 server

The PowerEdge XE2420 server is a specialty edge server that delivers high performance in harsh environments. This server is designed for demanding edge applications such as streaming analytics, manufacturing logistics, 5G cell processing, and other AI applications. It is a short-depth, dense, dual-socket, 2U server that can handle great environmental stress on its electrical and physical components. Also, this server is ideal for low-latency and large-storage edge applications because it supports 16x DDR4 RDIMM/LR-DIMM (12 DIMMs are balanced) up to 2993 MT/s. Importantly, this server can support the following GPU/Flash PCI card configurations:

• Up to 2 x PCIe x16, up to 300 W passive FHFL cards (for example, NVIDIA V100/s or NVIDIA RTX6000)
• Up to 4 x PCIe x8; 75 W passive (for example, NVIDIA T4 GPU)
• Up to 2 x FE1 storage expansion cards (up to 20 x M.2 drives on each)

The following figures show the PowerEdge XE2420 server (source):

Figure 1: Front view of the PowerEdge XE2420 server

Figure 2: Rear view of the PowerEdge XE2420 server

PowerEdge XR12 server

The PowerEdge XR12 server is part of a line of rugged servers that deliver high performance and reliability in extreme conditions. This server is a marine-compliant, single-socket 2U server that offers boosted services for the edge. It includes one CPU that has up to 36 x86 cores, support for accelerators, DDR4, PCIe 4.0, persistent memory and up to six drives. Also, the PowerEdge XR12 server offers 3rd Generation Intel Xeon Scalable Processors.

The following figures show the PowerEdge XR12 server (source):

Figure 3: Front view of the PowerEdge XR12 server

Figure 4: Rear view of the PowerEdge XR12 server

Performance discussion

The following figure shows the comparison of the ResNet 50 Offline performance of various server and GPU configurations, including:

• PowerEdge XE8545 server with the 80 GB A100 Multi-Instance GPU (MIG) with seven instances of the one compute instance of the 10gb memory profile
•  PowerEdge XR12 server with the A2 GPU
•  PowerEdge XE2420 server with the T4 and A30 GPU

Figure 5: MLPerf Inference ResNet 50 Offline performance

ResNet 50 falls under the computer vision category of applications because it includes image classification, object detection, and object classification detection workloads.

The MIG numbers are per card and have been divided by 28 because of the four physical GPU cards in the systems multiplied by second instances of the MIG profile. The non-MIG numbers are also per card.

For the ResNet 50 benchmark, the PowerEdge XE2420 server with the T4 GPU showed more than double the performance of the PowerEdge XR12 server with the A2 GPU. The PowerEdge XE8545 server with the A100 MIG showed competitive performance when compared to the PowerEdge XE2420 server with the T4 GPU. The performance delta of 12.8 percent favors the PowerEdge XE2420 system. However, the PowerEdge XE2420 server with A30 GPU card takes the top spot in this comparison as it shows almost triple the performance over the PowerEdge XE2420 server with the T4 GPU.

The following figure shows a comparison of the SSD-ResNet 34 Offline performance of the PowerEdge XE8545 server with the A100 MIG and the PowerEdge XE2420 server with the A30 GPU.

Figure 6: MLPerf Inference SSD-ResNet 34 Offline performance

The SSD-ResNet 34 model falls under the computer vision category because it performs object detection. The PowerEdge XE2420 server with the A30 GPU card performed more than three times better than the PowerEdge XE8545 server with the A100 MIG.

The following figure shows a comparison of the Recurrent Neural Network Transducers (RNNT) Offline performance of the PowerEdge XR12 server with the A2 GPU and the PowerEdge XE2420 server with the T4 GPU:

Figure 7: MLPerf Inference RNNT Offline performance

The RNNT model falls under the speech recognition category, which can be used for applications such as automatic closed captioning in YouTube videos and voice commands on smartphones. However, for speech recognition workloads, the PowerEdge XE2420 server with the T4 GPU and the PowerEdge XR12 server with the A2 GPU are closer in terms of performance. There is only a 32 percent performance delta.

The following figure shows a comparison of the BERT Offline performance of default and high accuracy runs of the PowerEdge XR12 server with the A2 GPU and the PowerEdge XE2420 server with the A30 GPU:

Figure 8: MLPerf Inference BERT Offline performance

BERT is a state-of-the-art, language-representational model for Natural Language Processing applications such as sentiment analysis. Although the PowerEdge XE2420 server with the A30 GPU shows significant performance gains, the PowerEdge XR12 server with the A2 GPU exceeds when considering achieved performance based on the money spent.

The following figure shows a comparison of the Deep Learning Recommendation Model (DLRM) Offline performance for the PowerEdge XE2420 server with the T4 GPU and the PowerEdge XR12 server with the A2 GPU:

Figure 9: MLPerf Inference DLRM Offline performance

DLRM uses collaborative filtering and predicative analysis-based approaches to make recommendations, based on the dataset provided. Recommender systems are extremely important in search, online shopping, and online social networks. The performance of the PowerEdge XE2420 T4 in the offline mode was 40 percent better than the PowerEdge XR12 server with the A2 GPU.

Despite the higher performance from the PowerEdge XE2420 server with the T4 GPU, the PowerEdge XR12 server with the A2 GPU is an excellent option for edge-related workloads. The A2 GPU is designed for high performance at the edge and consumes less power than the T4 GPU for similar workloads. Also, the A2 GPU is the more cost-effective option.

Power Discussion

It is important to budget power consumption for the critical load in a data center. The critical load includes components such as servers, routers, storage devices, and security devices. For the MLPerf Inference v2.0 submission, Dell Technologies submitted power numbers for the PowerEdge XR12 server with the A2 GPU. Figures 8 through 11 showcase the performance and power results achieved on the PowerEdge XR12 system. The blue bars are the performance results, and the green bars are the system power results. For all power submissions with the A2 GPU, Dell Technologies took the Number One claim for performance per watt for the ResNet 50, RNNT, BERT, and DLRM benchmarks.

Figure 10: MLPerf Inference v2.0 ResNet 50 power results on the Dell PowerEdge XR12 server

Figure 11: MLPerf Inference v2.0 RNNT power results on the Dell PowerEdge XR12 server

Figure 12: MLPerf Inference v2.0 BERT power results on the Dell PowerEdge XR12 server

Figure 13: MLPerf Inference v2.0 DLRM power results on the Dell PowerEdge XR12 server

Note: During our submission to MLPerf Inference v2.0 including power numbers, the PowerEdge XR12 server was not tuned for optimal performance per watt score. These results reflect the performance-optimized power consumption numbers of the server.

Conclusion

This blog takes a closer look at Dell Technologies’ MLPerf Inference v2.0 edge-related submissions. Readers can compare performance results between the Dell PowerEdge XE2420 server with the T4 GPU and the Dell PowerEdge XR12 server with the A2 GPU with other systems with different accelerators. This comparison helps readers make informed decisions about ML workloads on the edge. Performance, power consumption, and cost are the important factors to consider when planning any ML workload. Both the PowerEdge XR12 and XE2420 servers are excellent choices for Deep Learning workloads on the edge.

Appendix

SUT configuration

The following table describes the System Under Test (SUT) configurations from MLPerf Inference v2.0 submissions:

Table 1: MLPerf Inference v2.0 system configuration of the PowerEdge XE2420 and XR12 servers

Table 2: MLPerf Inference v1.1 system configuration of the PowerEdge XE8545 server