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

Abstract

Dell Technologies recently submitted results to the MLPerf Inference v2.0 benchmark suite. The results provide information about the performance of Dell servers. This blog takes a closer look at the Dell PowerEdge R750xa server and its performance for MLPerf Inference v1.1 and v2.0.

We compare the v1.1 results with the v2.0 results. We show the performance difference between the software stack versions. We also use the PowerEdge R750xa server to demonstrate that the v1.1 results from all systems can be referenced for planning an ML workload on systems that are not available for MLPerf Inference v2.0.  

PowerEdge R750xa server

Built with state-of-the-art components, the PowerEdge R750xa server is ideal for artificial intelligence (AI), machine learning (ML), and deep learning (DL) workloads. The PowerEdge R750xa server is the GPU-optimized version of the PowerEdge R750 server. It supports accelerators as 4 x 300 W DW or 6 x 75 W SW. The GPUs are placed in the front of the PowerEdge R750xa server allowing for better airflow management. It has up to eight available PCIe Gen4 slots and supports up to eight NVMe SSDs.

The following figures show the PowerEdge R750xa server (source):

Figure 1: Front view of the PowerEdge R750xa server

Figure 2: Rear view of the PowerEdge R750xa server

Figure 3: Top view of the PowerEdge R750xa server

Configuration comparison

The following table describes the software stack configurations from the two rounds of submission for the closed data center division:

Table 1: MLPerf Inference v1.1 and v2.0 software stacks

Although the software has been updated across the two rounds of submission, performance is consistent, if not better, for the v2.0 submission. For MLPerf Inference v2.0, Triton performance results can be extrapolated from MLPerf Inference v1.1 except for the 3D U-Net benchmark, which is due to a v2.0 dataset change.

The following table describes the System Under Test (SUT) configurations from MLPerf Inference v1.1 and v2.0 of data center inference submissions:

Table 2: MLPerf Inference v1.1 and v2.0 system configuration of the PowerEdge R750xa server

In the v1.1 round of submission, Dell Technologies submitted four different configurations on the PowerEdge R750xa server. Although the GPU count of four was maintained, Dell Technologies submitted the 40 GB and the 80 GB versions of the NVIDIA A100 GPU. Additionally, Dell Technologies submitted Multi-Instance GPU (MIG) numbers using 28 instances of the one compute instance of the 10gb memory profile on the 80 GB A100 GPU. Furthermore, Dell Technologies submitted power numbers (MaxQ is a performance and power submission) for the 40 GB version of the A100 GPU and submitted with the Triton server on the 80 GB version of the A100 GPU. A discussion about the v1.1 submission by Dell Technologies can be found in this blog.

Performance comparison of the PowerEdge R70xa server for MLPerf Inference v2.0 and v1.1

ResNet 50

ReNet50 is a 50-layer deep convolution neural network that is made up of 48 convolution layers along with a single max pool and average pool layer. This model is used for computer vision applications including image classification, object detection, and object classification. For the ResNet 50 benchmark, the performance numbers from the v2.0 submission match and outperform in the server and offline scenarios respectively when compared to the v1.1 round of submission. As shown in the following figure, the v2.0 submission results are within 0.02 percent in the server scenario and outperform the previous round by 1 percent in the offline scenario:

Figure 4: MLPerf Inference v2.0 compared to v1.1 ResNet 50 per card results on the PowerEdge R750xa server

BERT

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. In the v2.0 round of submission, the PowerEdge R750xa server matched and slightly outperformed its performance from the previous round. In the default BERT server and offline scenarios, the extracted performance is within 0.06 and 2.33 percent respectively. In the high accuracy BERT server and offline scenarios, the extracted performance is within 0.14 and 1.25 percent respectively.

Figure 5: MLPerf Inference v2.0 compared to v1.1 BERT per card results on the PowerEdge R750xa server

SSD-ResNet 34

The SSD-ResNet 34 model falls under the computer vision category. This benchmark performs object detection. For the SSD-ResNet 34 benchmark, the results produced in the v2.0 round of submission are within 0.14 percent for the server scenario and show a 1 percent improvement in the offline scenario.

Figure 6: MLPerf Inference v2.0 compared to v1.1 SSD-ResNet 34 per card results on the PowerEdge R750xa server

DLRM

Deep Learning Recommendation Model (DLRM) is an effective benchmark for understanding workload requirements for building recommender systems. This model uses collaborative filtering and predicative analysis-based approaches to process large amounts of data. The DLRM benchmark consists of default and high accuracy modes, both containing the server and offline scenarios. For the server scenario in both the default and high accuracy modes, the v2.0 submissions results are within 0.003 percent. For the offline scenario across both modes, the PowerEdge R750xa server showed a 2.62 percent performance gain.

Figure 7: MLPerf Inference v2.0 compared to v1.1 DLRM per card results on the PowerEdge R750xa server

RNNT

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. For the RNNT benchmark, the PowerEdge R750xa server maintained similar performance behavior within 0.04 percent in the server mode and showing 1.46 percent performance gains in the offline mode.

Figure 8: MLPerf Inference v2.0 compared to v1.1 RNNT per card results on the PowerEdge R750xa server

3D U-Net

The 3D U-Net performance numbers have changed in terms of scale and are not comparable in a bar graph because of a change to the dataset. The new dataset for this model is the Kitts 2019 Kidney Tumor Segmentation set. However, the PowerEdge R750xa server yielded Number One results among the PCIe form factor systems that were submitted. This model falls under the computer vision category, but it specifically deals with medical image data.

Results summary

Figure 1 through Figure 8 show the consistent performance of the PowerEdge R750xa server across both rounds of submission.

The following figure shows that in the offline scenarios for the benchmarks there is a small but noticeable performance improvement:

Figure 9: Performance improvement in percentage of the PowerEdge R750xa server across MLPerf Inference v2.0 and v1.1

The small percentage delta in the server scenarios can be a result of noise and are consistent with the previous round of submission.

Conclusion

This blog confirms the consistent performance of the Dell PowerEdge R750xa server across the MLPerf Inference v1.1 and MLPerf Inference v2.0 submissions. Because an identical system from round v1.1 performed at a consistent level for MLPerf Inference v2.0, we see that the software stack upgrades had minimal impact on performance. Therefore, the optimal results from the v1.1 round of submission can be used for making informed decisions about server performance for a specific ML workload. Because Dell Technologies submitted a diverse set of configurations in the v1.1 round of submission, customers can take advantage of many results.