Dell Technologies has been an active participant in the MLCommons™ Inference benchmark submission since day one. We have completed five rounds of inference submission.

This blog provides an overview of the latest results of MLPerf Inference v2.0 closed data center, closed data center power, closed edge, and closed edge power categories on Dell servers from our HPC & AI Innovation Lab. It shows optimal inference and power (performance per watt) performance for Dell GPU-based servers (DSS 8440, PowerEdge R750xa, PowerEdge XE2420, PowerEdge XE8545, and PowerEdge XR12). The previous blog about MLPerf Inference v1.1 performance results can be found here.

What is new?

• There were 3,800 performance results for this round compared to 1,800 performance results for v1.1. Additionally, 885 systems in v2.0 compared to 424 systems in v1.1 shows that there were more than twice the systems submitted for this round. 
• For the 3D U-Net benchmark, the dataset now used is the KiTs 2019 Kidney Tumor Segmentation set.
• Early stopping was introduced in this round to replace a deterministic minimum query count with a function that dynamically determines when further runs are not required to identify additional performance gain.

Results at a glance

 Dell Technologies submitted 167 results to the various categories. The Dell team made 86 submissions to the closed data center category, 28 submissions to the closed data center power category, and 53 submissions to the closed edge category. For the closed data center category, the Dell team submitted the second most results. In fact, Dell Technologies submitted results from 17 different system configurations with the NVIDIA TensorRT and NVIDIA Triton inference engines. Among these 17 configurations, the PowerEdge XE2420 server with T4 and A30 GPUs and the PowerEdge XR12 server with the A2 GPU were two new systems that have not been submitted before. Additionally, Dell Technologies submitted to the reintroduced Multiterm scenario. Only Dell Technologies submitted results for different host operating systems.

Noteworthy results

Noteworthy results include:

• The PowerEdge XE8545 and R750xa servers yield Number One results for performance per accelerator with NVIDIA A100 GPUs. The use cases for this top classification include Image Classification, Object Detection, Speech-to-text, Medical Imaging, Natural Language Processing, and Recommendation.
• The DSS 8440 server yields Number Two results for system performance for multiple benchmarks including Speech-to-text, Object Detection, Natural Language Processing, and Medical Image Segmentati on uses cases among all submissions.
• The PowerEdge R750xa server yields Number One results for the highest system performance for multiple benchmarks including Image Classification, Object Detection, Speech-to-text, Natural Language Processing, and Recommendation use cases among all the PCIe-based GPU servers.
• The PowerEdge XE8545 server yields Number One results for the lowest multistrand latency with NVIDIA Multi-Instance GPU (MIG) in the edge category for the Image Classification and Object Detection use cases.
• The PowerEdge XE2420 server yields Number One results for the highest T4 GPU inference results for the Image Classification, Speech-to-text, and Recommendation use cases.
• The PowerEdge XR12 server yields Number One results for the highest performance per watt with NVIDIA A2 GPU results in power for the Image Classification, Object Detection, Speech-to-text, Natural Language Processing, and Recommendation use cases.

MLPerf Inference v2.0 benchmark results

The following graphs highlight the performance metrics for the Server and Offline scenarios across the various benchmarks from MLCommons. Dell Technologies presents results as an method for our customers to identify options to suit their deep learning application demands. Additionally, this performance data serves as a reference point to enable sizing of deep learning clusters. Dell Technologies strives to submit as many results as possible to offer answers to ensure that customer questions are resolved.

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

Figure 1 through Figure 6 show the per card performance of the various SUTs on the ResNet 50, BERT, SSD, 3dUnet, RNNT, and DLRM modes respectively in the Server and Offline scenarios:

Figure 1: MLPerf Inference v2.0 ResNet 50 per card results

Figure 2: MLPerf Inference v2.0 BERT default and high accuracy per card results

Figure 3: MLPerf Inference v2.0 SSD-ResNet 34 per card results

Figure 4: MLPerf Inference v2.0 3D U-Net per card results

Figure 5: MLPerf Inference v2.0 RNNT per card results

Figure 6: MLPerf Inference v2.0 DLRM default and high accuracy per card results

Observations

The results in this blog have been officially submitted to and accepted by the MLCommons organization. These results have passed compliance tests, been peer reviewed, and adhered to the constraints enforced by MLCommons. Customers and partners can reproduce our results by following steps to run MLPerf Inference v2.0 in its GitHub repository.

Submissions from Dell Technologies included approximately 140 performance results and 28 performance and power results. Across the various workload tasks including Image Classification, Object Detection, Medical Image Segmentation, Speech-to-text, Language Processing, and Recommendation, server performance from Dell Technologies was promising.

Dell servers performed with optimal performance and power results. They were configured with different GPUs such as:

• NVIDIA A30 Tensor Core GPU
• NVIDIA A100 (PCIe and SXM)
• NVIDIA T4 Tensor Core GPU
• NVIDIA A2 Tensor Core GPU, which is newly released

More information about performance for specific configurations that are not discussed in this blog can be found in the v1.1 or v1.0 results.

The submission included results from different inference backends such as NVIDIA TensorRT and NVIDIA Triton. The appendix provides a summary of the full hardware and software stacks.

Conclusion

This blog quantifies the performance of Dell servers in the MLPerf Inference v2.0 round of submission. Readers can use these results to make informed planning and purchasing decisions for their AI workload needs.

Appendix

Software stack

The NVIDIA Triton Inference Server is an open-source inferencing software tool that aids in the deployment and execution of AI models at scale in production. Triton not only works with all major frameworks but also with customizable backends, further enabling developers to focus on their AI development. It is a versatile tool because it supports any inference type and can be deployed on any platform including CPU, GPU, data center, cloud, or edge. Additionally, Triton supports the rapid and reliable deployment of AI models at scale by integrating well with Kubernetes, Kubeflow, Prometheus, and Grafana. Triton supports the HTTP/REST and GRPC protocols that allow remote clients to request inferencing for any model that the server manages.

The NVIDIA TensorRT SDK delivers high-performance deep learning inference that includes an inference optimizer and runtime. It is powered by CUDA and offers a unified solution to deploy on various platforms including edge or data center. TensorRT supports the major frameworks including PyTorch, TensorFlow, ONNX, and MATLAB. It can import models trained in these frameworks by using integrated parsers. For inference, TensorRT performs orders of magnitude faster than its CPU-only counterparts.

NVIDIA MIG can partition GPUs into several instances that extend compute resources among users. MIG enables predictable performance and maximum GPU use by running jobs simultaneously on the different instances with dedicated resources for compute, memory, and memory bandwidth.

SUT configuration

The following table describes the SUT from this round of data center inference submission:

Table 1: MLPerf Inference v2.0 system configurations for DSS 8440 and PowerEdge R750xa servers

Table 2: MLPerf Inference v2.0 system configurations for PowerEdge XE2420 servers

Table 3: MLPerf Inference v2.0 system configurations for PowerEdge XE8545 servers

Table 4: MLPerf Inference v2.0 system configurations for PowerEdge XR12 servers

Dell Technologies submitted several benchmark results for the latest MLCommons^TM Inference v3.0 benchmark suite. An objective was to provide information to help customers choose a favorable server and GPU combination for their workload. This blog reviews the Edge benchmark results and provides information about how to determine the best server and GPU configuration for different types of ML applications.

Results overview

For computer vision workloads, which are widely used in security systems, industrial applications, and even in self-driven cars, ResNet and RetinaNet results were submitted. ResNet is an image classification task and RetinaNet is an object detection task. The following figures show that for intensive processing, the NVIDIA A30 GPU, which is a double-wide card, provides the best performance with almost two times more images per second than the NVIDIA L4 GPU. However, the NVIDIA L4 GPU is a single-wide card that requires only 43 percent of the energy consumption of the NVIDIA A30 GPU, considering nominal Thermal Design Power (TDP) of each GPU. This low-energy consumption provides a great advantage for applications that need lower power consumption or in environments that are more challenging to cool. The NVIDIA L4 GPU is the replacement for the best-selling NVIDIA T4 GPU, and provides twice the performance with the same form factor. Therefore, we see that this card is the best option for most Edge AI workloads.

Conversely, the NVIDIA A2 GPU exhibits the most economical price (compared to the  NVIDIA A30 GPU's price), power consumption (TDP), and performance levels among all available options in the market. Therefore, if the application is compatible with this GPU, it has the potential to deliver the lowest total cost of ownership (TCO).

Figure 1: Performance comparison of NVIDIA A30, L4, T4, and A2 GPUs for the ResNet Offline benchmark

Figure 2: Performance comparison of NVIDIA A30, L4, T4, and A2 GPUs for the RetinaNet Offline benchmark

The 3D-UNet benchmark is the other computer vision image-related benchmark. It uses medical images for volumetric segmentation. We saw the same results for default accuracy and high accuracy. Moreover, the NVIDIA A30 GPU delivered significantly better performance over the NVIDIA L4 GPU. However, the same comparison between energy consumption, space, and cooling capacity discussed previously applies when considering which GPU to use for each application and use case.

Figure 3: Performance comparison of NVIDIA A30, L4, T4, and A2 GPUs for the 3D-UNet Offline benchmark 

Another important benchmark is for BERT, which is a Natural Language Processing model that performs tasks such as question answering and text summarization. We observed similar performance differences between the NVIDIA A30, L4, T4, and A2 GPUs. The higher the value, the better.

Figure 4: Performance comparison of NVIDIA A30, L4, T4, and A2 GPUs for the BERT Offline benchmark

MLPerf benchmarks also include latency results, which are the time that systems take to process requests. For some use cases, this processing time can be more critical than the number of requests that can be processed per second. For example, if it takes several seconds to respond to a conversational algorithm or an object detection query that needs a real-time response, this time can be particularly impactful on the experience of the user or application.

As shown in the following figures, the NVIDIA A30 and NVIDIA L4 GPUs have similar latency results. Depending on the workload, the results can vary due to which GPU provides the lowest latency. For customers planning to replace the NVIDIA T4 GPU or seeking a better response time for their applications, the NVIDIA L4 GPU is an excellent option. The NVIDIA A2 GPU can also be used for applications that require low latency because it performed better than the NVIDIA T4 GPU in single stream workloads. The lower the value, the better.

Figure 4: Latency comparison of NVIDIA A30, L4, T4, and A2 GPUs for the ResNet single-stream and multistream benchmark

Figure 5: Latency comparison of NVIDIA A30, L4, T4, and A2 GPUs for the RetinaNet single-stream and multistream benchmark and the BERT single-stream benchmark

Dell Technologies submitted to various benchmarks to help understand which configuration is the most environmentally friendly as the data center’s carbon footprint is a concern today. This concern is relevant because some edge locations have power and cooling limitations. Therefore, it is important to understand performance compared to power consumption.

The following figure affirms that the NVIDIA L4 GPU has equal or better performance per watt compared to the NVIDIA A2 GPU, even with higher power consumption. For Throughput and Perf/watt values, higher is better; for Power(watt) values, lower is better.

Figure 6: NVIDIA L4 and A2 GPU power consumption comparison

Conclusion

With measured workload benchmarks on MLPerf Inference 3.0, we can conclude that all NVIDIA GPUs tested for Edge workloads have characteristics that address several use cases. Customers must evaluate size, performance, latency, power consumption, and price. When choosing which GPU to use and depending on the requirements of the application, one of the evaluated GPUs will provide a better result for the final use case.

Another important conclusion is that the NVIDIA L4 GPU can be considered as an exceptional upgrade for customers and applications running on NVIDIA T4 GPUs. The migration to this new GPU can help consolidate the amount of equipment, reduce the power consumption, and reduce the TCO; one NVIDIA L4 GPU can provide twice the performance of the NVIDIA T4 GPU for some workloads.

Dell Technologies demonstrates on this benchmark the broad Dell portfolio that provides the infrastructure for any type of customer requirement.

The following blogs provide analyses of other MLPerf^TM benchmark results:

• Dell Servers Excel in MLPerf™ Inference 3.0 Performance
• Dell Technologies’ NVIDIA H100 SXM GPU submission to MLPerf™ Inference 3.0
• Empowering Enterprises with Generative AI: How Does MLPerf™ Help Support
• Comparison of Top Accelerators from Dell Technologies’ MLPerf™

References

For more information about Dell Power Edge servers, go to the following links:

• Dell’s PowerEdge XR7620 for Telecom/Edge Compute
• Dell’s PowerEdge XR5610 for Telecom/Edge Compute
• PowerEdge XR4520c Compute Sled specification sheet
• PowerEdge XE2420 Spec Sheet

For more information about NVIDIA GPUs, go to the following  links:

• NVIDIA L4 Datasheet
• NVIDIA A30 Datasheet
• NVIDIA A2 Datasheet
• NVIDIA T4 Datasheet

MLCommons^TM Inference v3.0 results presented in this document are based on following system IDs: 

Table 1: MLPerf^TM system IDs