The Dell EMC PowerEdge R7525 server provides exceptional MLPerf Inference v0.7 Results, which indicate that:

• Dell Technologies holds the #1 spot in performance per GPU with the NVIDIA A100-PCIe GPU on the DLRM-99 Server scenario
• Dell Technologies holds the #1 spot in performance per GPU with the NVIDIA A100-PCIe on the DLRM-99.9 Server scenario
• Dell Technologies holds the #1 spot in performance per GPU with the NVIDIA A100-PCIe on the ResNet-50 Server scenario

Summary

In this blog, we provide the performance numbers of our recently released Dell EMC PowerEdge R7525 server with two NVIDIA A100 GPUs on all the results of the MLPerf Inference v0.7 benchmark. Our results indicate that the PowerEdge R7525 server is an excellent choice for inference workloads. It delivers optimal performance for different tasks that are in the MLPerf Inference v0.7 benchmark. These tasks include image classification, object detection, medical image segmentation, speech to text, language processing, and recommendation. 

The PowerEdge R7525 server is a two-socket, 2U rack server that is designed to run workloads using flexible I/O and network configurations. The PowerEdge R7525 server features the 2nd Gen AMD EPYC processor, supports up to 32 DIMMs, has PCI Express (PCIe) Gen 4.0-enabled expansion slots, and provides a choice of network interface technologies to cover networking options. 

The following figure shows the front view of the PowerEdge R7525 server:

Figure 1. Dell EMC PowerEdge R7525 server

The PowerEdge R7525 server is designed to handle demanding workloads and for AI applications such as AI training for different kinds of models and inference for different deployment scenarios. The PowerEdge R7525 server supports various accelerators such as NVIDIA T4, NVIDIA V100S, NVIDIA RTX, and NVIDIA A100 GPU s. The following sections compare the performance of NVIDIA A100 GPUs with NVIDIA T4 and NVIDIA RTX GPUs using MLPerf Inference v0.7 as a benchmark.

The following table provides details of the PowerEdge R7525 server configuration and software environment for MLPerf Inference v0.7:

For more information about how to run the benchmark, see Running the MLPerf Inference v0.7 Benchmark on Dell EMC Systems.

MLPerf Inference v0.7 performance results

The MLPerf inference benchmark measures how fast a system can perform machine learning (ML) inference using a trained model in various deployment scenarios. The following results represent the Offline and Server scenarios of the MLPerf Inference benchmark. For more information about different scenarios, models, datasets, accuracy targets, and latency constraints in MLPerf Inference v0.7, see Deep Learning Performance with MLPerf Inference v0.7 Benchmark.

In the MLPerf inference evaluation framework, the LoadGen load generator sends inference queries to the system under test, in our case, the PowerEdge R7525 server with various GPU configurations. The system under test uses a backend (for example, TensorRT, TensorFlow, or PyTorch) to perform inferencing and sends the results back to LoadGen.

MLPerf has identified four different scenarios that enable representative testing of a wide variety of inference platforms and use cases. In this blog, we discuss the Offline and Server scenario performance. The main differences between these scenarios are based on how the queries are sent and received:

• Offline—One query with all samples is sent to the system under test. The system under test can send the results back once or multiple times in any order. The performance metric is samples per second.
• Server—Queries are sent to the system under test following a Poisson distribution (to model real-world random events). One query has one sample. The performance metric is queries per second (QPS) within latency bound.

Note:  Both the performance metrics for Offline and Server scenario represent the throughput of the system. 

In all the benchmarks, two NVIDIA A100 GPUs outperform eight NVIDIA T4 GPUs and three NVIDIA RTX800 GPUs for the following models:

• ResNet-50 image classification model
• SSD-ResNet34 object detection model
• RNN-T speech recognition model
• BERT language processing model
• DLRM recommender model
• 3D U-Net medical image segmentation model

The following graphs show PowerEdge R7525 server performance with two NVIDIA A100 GPUs, eight NVIDIA T4 GPUs, and three NVIDIA RTX8000 GPUs with 99% accuracy target benchmarks and 99.9% accuracy targets for applicable benchmarks:

• 99% accuracy (default accuracy) target benchmarks: ResNet-50, SSD-Resnet34, and RNN-T
• 99% and 99.9% accuracy (high accuracy) target benchmarks: DLRM, BERT, and 3D-Unet

99% accuracy target benchmarks

ResNet-50

The following figure shows results for the ResNet-50 model:

Figure 2. ResNet-50 Offline and Server inference performance

From the graph, we can derive the per GPU values. We divide the system throughput (containing all the GPUs) by the number of GPUs to get the Per GPU results as they are linearly scaled. 

SSD-Resnet34

The following figure shows the results for the SSD-Resnet34 model:

Figure 3. SSD-Resnet34 Offline and Server inference performance

RNN-T

The following figure shows the results for the RNN-T model:

Figure 4. RNN-T Offline and Server inference performance

99.9% accuracy target benchmarks

DLRM

The following figures show the results for the DLRM model with 99% and 99.9% accuracy:

Figure 5. DLRM Offline and Server Scenario inference performance – 99% and 99.9% accuracy                                   

 For the DLRM recommender and 3D U-Net medical image segmentation (see Figure 7) models, both 99% and 99.9% accuracy have the same throughput. The 99.9% accuracy benchmark also satisfies the required accuracy constraints with the same throughput as that of 99%.

BERT

The following figures show the results for the BERT model with 99%  and 99.9% accuracy:

Figure 6. BERT Offline and Server inference performance – 99% and 99.9% accuracy 

For the BERT language processing model, two NVIDIA A100 GPUs outperform eight NVIDIA T4 GPUs and three NVIDIA RTX8000 GPUs. However, the performance of three NVIDIA RTX8000 GPUs is a little better than that of eight NVIDIA T4 GPUs.

3D U-Net

For the 3D-Unet medical image segmentation model, only the Offline scenario benchmark is available.

The following figure shows the results for the 3D U-Net model Offline scenario:

Figure 7. 3D U-Net Offline inference performance 

For the 3D-Unet medical image segmentation model, since there is only offline scenario benchmark for 3D-Unet the above graph represents only Offline scenario.

The following table compares the throughput between two NVIDIA A100 GPUs, eight NVIDIA T4 GPUs, and three NVIDIA RTX8000 GPUs with 99% accuracy target benchmarks and 99.9% accuracy targets:

Conclusion

With support of NVIDIA A100, NVIDIA T4, or NVIDIA RTX8000 GPUs, Dell EMC PowerEdge R7525 server is an exceptional choice for various workloads that involve deep learning inference. However, the higher throughput that we observed with NVIDIA A100 GPUs translates to performance gains and faster business value for inference applications. 

Dell EMC PowerEdge R7525 server with two NVIDIA A100 GPUs delivers optimal performance for various inference workloads, whether it is in a batch inference setting such as Offline scenario or Online inference setting such as Server scenario. 

Next steps

In future blogs, we will discuss sizing the system (server and GPU configurations) correctly based on the type of workload (area and task).

Abstract

Dell Technologies recently submitted results to the MLPerf Inference v3.1 benchmark suite. This blog examines the results on the Dell PowerEdge XR4520c, PowerEdge XR7620, and PowerEdge XR5610 servers with the NVIDIA L4 GPU.

MLPerf Inference background

The MLPerf Inference benchmarking suite is a comprehensive framework designed to fairly evaluate the performance of a wide range of machine learning inference tasks on various hardware and software configurations. The MLCommons^TM community aims to provide a standardized set of deep learning workloads with which to work and as fair measuring and auditing methodologies. The MLPerf Inference submission results serve as valuable information for researchers, customers, and partners to make informed decisions about inference capabilities on various edge and data center systems.

The MLPerf Inference edge suite includes three scenarios:

• Single-stream—This scenario’s performance metric is 90 percent latency. A common use case is the Siri voice assistant on iOS products on which Siri’s engine waits until the query has been asked and then returns results.
• Multi-stream—This scenario has a higher performance metric with a 99 percent latency. An example use case is self-driving cars. Self-driving cars use multiple cameras and lidar inputs to real-time driving decisions that have a direct impact on what happens on the road.
• Offline—This scenario is measured by throughput. An example of Offline processing on the edge is a phone sharing an album suggestion that is based on a recent set of photos and videos from a particular event.

Edge computing

In traditional cloud computing at the data center, data from phones, tablets, sensors, and machines are sent to physically distant data centers to be processed. The location of where the data has been gathered and where it is processed are separate. The concept of edge computing shifts this methodology by processing data on the device itself or on local compute resources that are available nearby. The available compute resources nearby are known as the “devices on the edge.” Edge computing is prevalent in several industries such as self-driving cars, retail analytics, truck fleet management, smart grid energy distribution, healthcare, and manufacturing.

Edge computing complements traditional cloud computing by reducing processing speed in terms of lowering latency, improving efficiency, enhancing security, and enabling higher reliability. By processing data on the edge, the load on central data centers is eased as is the time to receive a response for any type of inference queries. With the offloading of computation in data centers, network congestion for cloud users becomes less of a concern. Also, because sensitive data is processed at the edge and is not exposed to threats across a wider network, the risk of sensitive data being compromised is less. Furthermore, if connectivity to the cloud is disrupted and is intermittent, edge computing can enable systems to continue functioning. With several devices on the edge acting as computational minidata centers, the problem of a single point of failure is mitigated and additional scalability becomes easily achievable.

Dell PowerEdge system and GPU overview

Dell PowerEdge XR4520c server

For projects that need a robust and adaptable server to handle demanding AI workloads on the edge, the PowerEdge XR4520c server is an excellent option. Dell Technologies designed the PowerEdge XR4520c server with reliability to withstand challenging edge environments. The PowerEdge XR4520c server delivers the power and compute required for real-time analytics on the edge with Intel Xeon Scalable processors. The edge-optimized design decisions include a rugged exterior and an extended temperature range to operate in remote locations and industrial environments. Also, the compact form factor and space-efficient design enable deployment on the edge. Like all Dell PowerEdge products, this server comes with world class Dell support and Dell’s (Integrated Dell Remote Access Controller (iDRAC) for remote management. For additional information about the technical specifications of the PowerEdge XR4520c server, see to the specification sheet.

Figure 1: Front view of the Dell PowerEdge XR4520c server

Figure 2: Top view of the Dell PowerEdge XR4520c server

Dell PowerEdge XR7620 server

The PowerEdge XR7620 server is top-of-the-line for deep learning in the edge. Powered with the latest Intel Xeon Scalable processors, the reduced training time and additional number of inferences is remarkable on the PowerEdge XR7620 server. Dell Technologies has designed this as a half-width server for rugged environments with a dust and particle filter and extended temperature range from –5C to 55C (23 F to 131 F). Furthermore, Dell’s comprehensive security and data protection features include data encryption and zero-trust logic for the protection of sensitive data. For additional information about the technical specifications of the PowerEdge XR7620 server, see the specification sheet.

Figure 3: Front view of the Dell PowerEdge XR7620 server

Figure 4: Rear view of the Dell PowerEdge XR7620 server

Dell PowerEdge XR5610 server

The Dell PowerEdge XR5610 server is an excellent option for AI workloads on the edge. This all-pupose, rugged single-socket server is a versatile edge server that has been built for telecom, defense, retail and other demanding edge environments. As shown in the following figures, the short chassis can fit in space-constrained environments and is also a formidable option when considering power efficiency. This server is driven by Intel Xeon Scalable processors and is boosted with NVIDIA GPUs as well as high-speed NVIDIA NVLink interconnects. For additional information about the technical specifications of the PowerEdge XR5610 server, see the specification sheet.

Figure 5: Front view of the Dell PowerEdge XR5610 server

Figure 6: Top view of the Dell PowerEdge XR5610 server

NVIDIA L4 GPU

The NVIDIA L4 GPU is an excellent strategic option for the edge as it consumes less energy and space but delivers exceptional performance. The NVIDIA L4 GPU is based on the Ada Lovelace architecture and delivers extraordinary performance for video, AI, graphics, and virtualization. The NVIDIA L4 GPU comes with NVIDIA’s cutting-edge AI software stack including CUDA, cuDNN, and support for several deep learning frameworks like Tensorflow and PyTorch.

Systems Under Test

The following table lists the Systems Under Test (SUT) that are described in this blog.

Table 1: MLPerf Inference v3.1 system configuration of the Dell PowerEdge XR7620 and the PowerEdge XR4520c servers

Performance from Inference v3.1

The following figure compares the Dell PowerEdge XR4520c and PowerEdge XR7620 servers across the ResNet50, RetinaNet, RNNT and BERT 99 Single-stream, Multi-stream, and Offline benchmarks. Across all the benchmarks in this comparison, we can state that the performance in the image classification, object detection, speech to text and language processing workloads packaged with NVIDIA L4 GPUs for both servers provide exceptional performance.

Figure 7: Dell PowerEdge XR4520c and PowerEdge XR7620 servers across the ResNet50, RetinaNet, RNNT, and BERT 99 Single and Multi-stream benchmarks

Figure 8: Dell PowerEdge XR4520c and PowerEdge XR7620 servers across the ResNet50, RetinaNet, RNNT, and BERT 99 Offline benchmarks

Like ResNet50 and RetinaNet, the 3D-Unet benchmark falls under the vision area but focuses on the medical image segmentation task. The following figures show identical performance of the two servers in both the default and high accuracy modes in the Single-stream and Offline scenarios.

Figure 9: Dell PowerEdge XR4520c and PowerEdge XR7620 servers across 3D-Unet Single-stream

Figure 10: Dell PowerEdge XR4520c and PowerEdge XR7620 server across 3D-Unet Offline

Dell PowerEdge XR5610 power submission

In the MLPerf Inference v3.0 round of submissions, Dell Technologies made a power submission under the preview category for the Dell PowerEdge XR5610 server with the NVIDIA L4 GPU. For the v3.1 round of submissions, Dell Technologies made another power submission for the same server in the closed edge category. As shown in the following table, the detailed configurations of both the systems across the rounds of submissions show that the hardware remained consistent, but that the software stack was updated. In terms of system performance per watt, the PowerEdge XR 5610 server claims the top spot in image classification, object detection, speech-to-text, language processing, and medical image segmentation workloads.

Table 2: MLPerf Inference v3.0 and v3.1 system configuration of the Dell PowerEdge XR5610 server

The power submission includes extra power results in each submission. For each submitted benchmark, there is a power metric that is paired with it. The metric for the Single-stream and Multi-stream performance results is Latency in milliseconds and the corresponding power consumption is noted in millijoules (mj). The Offline performance numbers are recorded in samples per second(samples/s), and the corresponding power readings are delivered in watts. The following table shows a breakdown for the calculations for queries per millijoules and samples/s per watt have been calculated.

Table 3: Breakdown of reading a power submission

The following figure shows the improvements in the performance per energy used on the Dell PowerEdge XR5610 server across the v3.1 and v3.0 rounds of submission. Across all the benchmarks, the server extracted double the performance per energy. For the RNNT Single-stream benchmark, the servers showed a brilliant performance jump of close to five times greater. The performance improvements came from hardware and software optimizations. Also, BIOS firmware upgrades also contributed significantly.

Figure 11: Dell PowerEdge XR5610 with NVIDIA L4 GPU power submission for v3.1 compared to v3.0

The following figure shows the Single-stream and Multi-stream latency results from the Dell PowerEdge XR5610 server:

Figure 12: Dell PowerEdge XR5610 NVIDIA L4 GPU L4 v3.1 server

Conclusion

Both the Dell PowerEdge XR4520c and Dell PowerEdge XR7620 servers continue to showcase excellent performance in the edge suite for MLPerf Inference. The Dell PowerEdge XR5610 server showed a consistent doubling in performance per energy across all benchmarks confirming itself as a power efficient server option. Built for the edge, the Dell PowerEdge XR portfolio proves to be an outstanding option with consistent performance in the MLPerf Inference v3.1 submission. As the need for edge computing continues to grow, the MLPerf Inference edge suite shows that Dell PowerEdge servers continue to be an excellent option for any Artificial Intelligence workload.

MLCommons results

https://mlcommons.org/en/inference-edge-31/

MLPerf Inference v3.1 system IDs:

• 3.1-0072 - Dell PowerEdge XR4520c (1x L4, TensorRT)
• 3.1-0073 - Dell PowerEdge XR5610 (1x L4, MaxQ, TensorRT)
• 3.1-0074 - Dell PowerEdge XR7620 (1x L4, TensorRT)

The MLPerf™ name and logo are trademarks of MLCommons Association in the United States and other countries. All rights reserved. Unauthorized use strictly prohibited. See www.mlcommons.org for more information.