Multinode Performance of Dell PowerEdge Servers with MLPerfTM Training v1.1
Mon, 07 Mar 2022 12:32:48 -0000|
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The Dell MLPerf v1.1 submission included multinode results. This blog showcases performance across multiple nodes on Dell PowerEdge R750xa and XE8545 servers and demonstrates that the multinode scaling performance was excellent.
The compute requirement for deep learning training is growing at a rapid pace. It is imperative to train models across multiple nodes to attain a faster time-to-solution. Therefore, it is critical to showcase the scaling performance across multiple nodes. To demonstrate to customers the performance that they can expect across multiple nodes, our v1.1 submission includes multinode results. The following figures show multinode results for PowerEdge R750xa and XE8545 systems.
Figure 1: One-, two-, four-, and eight-node results with PowerEdge R750xa Resnet50 MLPerf v1.1 scaling performance
Figure 1 shows the performance of the PowerEdge R750xa server with Resnet50 training. These numbers scale from one node to eight nodes, from four NVIDIA A100-PCIE-80GB GPUs to 32 NVIDIA A100-PCIE-80GB GPUs. We can see that the scaling is almost linear across nodes. MLPerf training requires passing Reference Convergence Points (RCP) for compliance. These RCPs were inhibitors to show linear scaling for the 8x scaling case. The near linear scaling makes a PowerEdge R750xa node an excellent choice for multinode training setup.
The workload was distributed by using singularity on PowerEdge R750xa servers. Singularity is a secure containerization solution that is primarily used in traditional HPC GPU clusters. Our submission includes setup scripts with singularity that help traditional HPC customers run workloads without the need to fully restructure their existing cluster setup. The submission also includes Slurm Docker-based scripts.
Figure 2: Multinode submission results for PowerEdge XE8545 server with BERT, MaskRCNN, Resnet50, SSD, and RNNT
Figure 2 shows the submitted performance of the PowerEdge XE8545 server with BERT, MaskRCNN, Resnet50, SSD, and RNNT training. These numbers scale from one node to two nodes, from four NVIDIA A100-SXM-80GB GPUs to eight NVIDIA A100-SXM-80GB GPUs. All GPUs operate at 500W TDP for maximum performance. They were distributed using Slurm and Docker on PowerEdge XE8545 servers. The performance is nearly linear.
Note: The RNN-T single node results submitted for the PowerEdge XE8545x4A100-SXM-80GB system used a different set of hyperparameters than for two nodes. After the submission, we ran the RNN-T benchmark again on the PowerEdge XE8545x4A100-SXM-80GB system with the same hyperparameters and found that the new time to converge is approximately 77.37 minutes. Because we only had the resources to update the results for the 2xXE8545x4A100-SXM-80GB system before the submission deadline, the MLCommons results show 105.6 minutes for a single node XE8545x4100-SXM-80GB system.
The following figure shows the adjusted representation of performance for the PowerEdge XE8545x4A100-SXM-80GB system. RNN-T provides an unverified score of 77.31 minutes:
Figure 3: Revised multinode results with PowerEdge XE8545 BERT, MaskRCNN, Resnet50, SSD, and RNNT
Figure 3 shows the linear scaling abilities of the PowerEdge XE8545 server across different workloads such as BERT, MaskRCNN, ResNet, SSD, and RNNT. This linear scaling ability makes the PowerEdge XE8545 server an excellent choice to run large-scale multinode workloads.
Note: This rnnt.zip file includes log files for 10 runs that show that the averaged performance is 77.31 minutes.
- It is critical to measure deep learning performance across multiple nodes to assess the scalability component of training as deep learning workloads are growing rapidly.
- Our MLPerf training v1.1 submission includes multinode results that are linear and perform extremely well.
- Scaling numbers for the PowerEdge XE8545 and PowerEdge R750xa server make them excellent platform choices for enabling large scale deep learning training workloads across different areas and tasks.
 MLPerf v1.1 Training RNN-T; Result not verified by the MLCommonsTM Association. 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 http://www.mlcommons.org for more information.
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MLPerf™ Inference v2.0 Edge Workloads Powered by Dell PowerEdge Servers
Fri, 06 May 2022 19:23:11 -0000|
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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.
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
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.
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.
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.
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
PowerEdge XE2420 1x T4, TensorRT
PowerEdge XR12 1x A2, TensorRT
PowerEdge XR12 1x A2, MaxQ, TensorRT
PowerEdge XE2420 2x A30, TensorRT
MLPerf system ID
Intel Xeon Gold 6238 CPU @ 2.10 GHz
Intel Xeon Gold 6312U CPU @ 2.40 GHz
Intel Xeon Gold 6252N CPU @ 2.30 GHz
GPU form factor
Table 2: MLPerf Inference v1.1 system configuration of the PowerEdge XE8545 server
PowerEdge XE8545 4x A100-SXM-80GB-7x1g.10gb, TensorRT, Triton
MLPerf system ID
AMD EPYC 7763
NVIDIA A100-SXM-80GB (7x1g.10gb MIG)
GPU form factor
Performance of the Dell PowerEdge R750xa Server for MLPerf™ Inference v2.0
Thu, 21 Apr 2022 17:59:56 -0000|
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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
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
v1.1 software stack
v2.0 software stack
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
v1.1 system configuration
v2.0 system configuration
R750xa 4x A100-PCIE-80GB, TensorRT
R750xa 4xA100 TensorRT
MLPerf system ID
Intel Xeon Gold 6338 CPU @ 2.00 GHz
GPU form factor
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
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
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
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
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
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
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.
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.
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.