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

Dell Technologies has completed the successful submission of MLPerf Training, which marks the seventh round of submission to MLCommons™. This blog provides an overview and highlights the performance of the Dell PowerEdge R750xa, XE8545, and DSS8440 servers that were used for the submission.

What’s new in MLPerf Training v2.1?

This round of submission does not include new benchmarks or changes in the existing benchmarks. A change is introduced in the submission compliance checker. 

This round adds one-sided normalization to the checker to reduce variance in the number of steps to converge. This change means that if a result converges faster than the RCP mean within a certain range, the checker normalizes the results to the RCP mean. This normalization was not available in earlier rounds of submission.

What’s new in MLPerf Training v2.1 with Dell submissions?

For Dell submission for MLPerf Training v2.1, we included:

• Improved performance with BERT and Mask R-CNN models
• Minigo submission results on Dell PowerEdge R750xa server with A100 PCIe GPUs

Overall Dell Submissions

Figure 1.     Overall submissions for all Dell PowerEdge servers in MLPerf Training v2.1

Figure 1 shows our submission in which the workloads span across image classification, lightweight and heavy object detection, speech recognition, natural language processing, recommender systems, medical image segmentation, and reinforcement learning. There were different NVIDIA GPUs including the A100, with PCIe and SXM4 form factors having 40 GB and 80 GB VRAM and A30.

The Minigo on the PowerEdge R750xa server is a first-time submission, and it takes around 516 minutes to run to target quality. That submission has 4x A100 PCIe 80 GB GPUs.

Our results have increased in count from 41 to 45. This increased number of submissions helps customers see the performance of the systems using different PowerEdge servers, GPUs, and CPUs. With more results, customers can expect to see the influence of using different hardware settings that can play a vital role in time to convergence.

We have several procured winning titles that demonstrate the higher performance of our systems in relation to other submitters, starting with the highest number of results across all the submitters. Some other titles include the top position in the time to converge for BERT, ResNet, and Mask R-CNN with our PowerEdge XE8545 server powered by NVIDIA A100-40GB GPUs.

Improvement in Performance for BERT and Mask R-CNN

Figure 2.     Performance gains from MLPerf v2.0 to MLPerf v2.1 running BERT

Figure 2 shows the improvements seen with the PowerEdge R750xa and PowerEdge XE8545 servers with A100 GPUs from MLPerf training v2.0 to MLPerf training v2.1 running BERT language model workload. The PowerEdge XE8545 server with A100-80GB has the fastest time to convergence and the highest improvement at 13.1 percent, whereas the PowerEdge XE8545 server with A100-40GB has 7.74 percent followed by the PowerEdge R750xa server with A100-PCIe at 5.35 percent.

Figure 3.     Performance gains from MLPerf v2.0 to MLPerf v2.1 running Mask R-CNN

Figure 3 shows the improvements seen with the PowerEdge XE8545 server with A100 GPUs. There is a 3.31 percent improvement in time to convergence with MLPerf v2.1.

For both BERT and Mask R-CNN, the improvements are software-based. These results show that software-only improvements can reduce convergence time. Customers can benefit from similar improvements without any changes in their hardware environment.

The following sections compare the performance differences between SXM and PCIe form factor GPUs.

Performance Difference Between PCIe and SXM4 Form Factor with A100 GPUs

Figure 4.     SXM4 form factor compared to PCIe for the BERT

Figure 5.     SXM4 form factor compared to PCIe for Resnet50 v1.5

Figure 6.     SXM4 form factor compared to PCIe for the RNN-T

Table 1:

Figures 4, 5, and 6 and Table 1 show that SXM form factor is faster than the PCIe form factor for BERT, Resnet50 v1.5, and RNN-T workloads.

The SXM form factor typically consumes more power and is faster than PCIe. For the above workloads, the minimum percentage improvement in convergence that customers can expect is in double digits, ranging from approximately 12 percent to 40 percent, depending on the workload.

Multinode Results Comparison

Multinode performance assessment is more important than ever. With the advent of large models and different parallelism techniques, customers have an ever-increasing need to find results faster. Therefore, we have submitted several multinode results to assess scaling performance.

Figure 7.     BERT multinode results with PowerEdge R750xa and XE8545 servers

Figure 7 indicates multinode results from three different systems with the following configurations:

1. R750xa with 4 A100-PCIe-80GB GPUs
2. XE8545 with 4 A100-SXM-40GB GPUs
3. XE8545 with 4 A100-SXM-80GB GPUs

Every node of the above system has four GPUs each. When the graph shows eight GPUs, it means that the performance results are derived from two nodes. Similarly, for 16 GPUs the results are derived from four nodes, and so on.

Figure 8.     Resnet50 multinode results with R750xa and XE8545 servers

Figure 9.     Mask R-CNN multinode results with R750xa and XE8545 servers

As shown in Figures 7, 8, and 9, the multinode scaling results of the BERT, Resnet50, and Mask R-CNN are linear or nearly linear scaled. This shows that Dell servers offer outstanding performance with single-node and multinode scaling.

Conclusion

The findings described in this blog show that:

• Dell servers can run all types of workloads in the MLPerf Training submission.
• Software-only enhancements reduce time to solution for our customers, as shown in our MLPerf Training v2.1 submission, and customers can expect to see improvements in their environments.
• Dell PowerEdge XE8545 and PowerEdge R750xa servers with NVIDIA A100 with PCIe and SXM4 form factors are both great selections for all deep learning models.
• PCIe-based PowerEdge R750xa servers can deliver reinforcement learning workloads in addition to other classes of workloads, such as image classification, lightweight and heavy object detection, speech recognition, natural language processing, and medical image segmentation.
• The single-node results of our submission indicate that Dell servers deliver outstanding performance and that multinode run scales well and helps to reduce time to solution across a distinct set of workload types, making Dell servers apt for single-node and multinode deep learning training workloads.
• The single-node results of our submission indicate that Dell servers deliver outstanding performance and that multinode results show a well-scaled performance that helps to reduce time to solution across a distinct set of workload types. This makes Dell servers apt for both small training workloads on single nodes and large deep learning training workloads on multinodes.

Appendix

System Under Test

MLPerf system configurations for PowerEdge XE8545 systems

MLPerf system configurations for Dell PowerEdge R750xa servers