Multinode Performance of Dell PowerEdge Servers with MLPerfTM Training v1.1
Mon, 07 Mar 2022 19:51:12 -0000|
Read Time: 0 minutes
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.
Related Blog Posts
MLPerf™ Inference v3.1 Edge Workloads Powered by Dell PowerEdge Servers
Tue, 19 Sep 2023 12:07:00 -0000|
Read Time: 0 minutes
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 MLCommonsTM 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.
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
Dell PowerEdge XR7620 (1x L4, TensorRT)
Dell PowerEdge XR4520c (1x L4, TensorRT)
MLPerf system ID
Dual Intel Xeon Gold 6448Y CPU @ 2.10 GHz
Single Intel Xeon D-2776NT CPU @ 2.10
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
Dell PowerEdge XR5610 (1x L4, MaxQ, TensorRT) v3.0
Dell PowerEdge XR5610 (1x L4, MaxQ, TensorRT) v3.1
MLPerf system ID
Intel(R) Xeon(R) Gold 5423N CPU @ 2.10 GHz
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
Performance per unit of energy
1 query/mj -> queries/mj
8 queries/mj -> queries/mj
Samples/s / Watts -> performance per Watt
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
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.
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.
Dell PowerEdge Servers deliver excellent performance with MLCommonsTM Inference 3.1
Mon, 11 Sep 2023 16:33:00 -0000|
Read Time: 0 minutes
Today, MLCommons released the latest version (v3.1) of MLPerf Inference results. Dell Technologies has made submissions to the inference benchmark since its version 0.5 launch in 2019. We continue to demonstrate outstanding results across different models in the benchmark such as image classification, object detection, natural language processing, speech recognition, recommender system and medical image segmentation, and LLM summarization. See our MLPerf™ Inference v2.1 with NVIDIA GPU-Based Benchmarks on Dell PowerEdge Servers white paper that introduces the MLCommons Inference benchmark. Generative AI (GenAI) has taken deep learning computing needs by storm and there is an ever-increasing need to enable high-performance innovative inferencing approaches. This blog provides an overview of the performance summaries that Dell PowerEdge servers enable end users to deliver on their AI Inference transformation.
What is new with Inference 3.1?
Inference 3.1 and Dell’s submission include the following:
- The inference benchmark has added two exciting new benchmarks:
- LLM-based models, such as GPT-J
- DLRM-V2 with multi-hot encodings using the DLRM-DCNv2 architecture
- Dell’s submission has been expanded to include the new PowerEdge XE8640 and PowerEdge XE9640 servers accelerated by NVIDIA GPUs.
- Dell’s submission includes results of PowerEdge servers with Qualcomm accelerators.
- Besides accelerator-based results, Dell’s submission includes Intel-based CPU-only results.
Overview of results
Dell Technologies submitted 230 results across 20 different configurations. The most impressive results were generated by PowerEdge XE9680, XE9640, XE8640, R760xa, and servers with the new NVIDIA H100 PCIe and SXM Tensor Core GPUs, PowerEdge XR7620 and XR5610 servers with the NVIDIA L4 Tensor Core GPUs, and the PowerEdge R760xa server with the NVIDIA L40 GPU.
Overall, NVIDIA-based results include the following accelerators:
- (New) Four-way NVIDIA H100 Tensor Core GPU (SXM)
- (New) Four-way NVIDIA L40 GPU
- Eight-way NVIDIA H100 Tensor Core GPU (SXM)
- Four-way NVIDIA A100 Tensor Core GPU (PCIe)
- NVIDIA L4 Tensor Core GPU
These accelerators were benchmarked on different servers such as PowerEdge XE9680, XE8640, XE9640, R760xa, XR7620, XR5610, and R750xa servers across data center and edge suites.
The large number of result choices offers end users an opportunity to make system purchase decisions and set performance and design expectations.
Interesting Dell Datapoints
The most interesting datapoints include:
- The performance numbers on newly released Dell PowerEdge servers are outstanding.
- Among 21 submitters, Dell Technologies was one of the few companies that covered all benchmarks in all closed divisions for data center, edge, and edge power suites.
- The PowerEdge XE9680 system with eight NVIDIA H100 SXM GPUs procures the highest performance titles with ResNet Server, RetinaNet Server, RNNT Server and Offline, BERT 99 Server, BERT 99.9 Offline, DLRM-DCNv2 99, and DLRM-DNCv2 99.9 Offline benchmarks.
- The PowerEdge XE8640 system with four NVIDIA H100 SXM GPUs procures the highest performance titles with all the data center suite benchmarks.
- The PowerEdge XE9640 system with four NVIDIA H100 SXM GPUs procures the highest performance titles for all systems among other liquid cooled systems for all data center suite benchmarks.
- The PowerEdge XR5610 system with an NVIDIA L4 Tensor Core GPU offers approximately two- to three-times higher performance/watt compared to the last round and procures the highest power efficiency titles with Resnet RetinaNet 3d-unet 99, 3D U-Net 99.9 and Bert-99.
The following figure shows the different system performance for offline and server scenarios in the data center. These results provide an overview; future blogs will provide more details about the results.
The figure shows that these servers delivered excellent performance for all models in the benchmark such as ResNet, RetinaNet, 3D-U-Net, RNN-T, BERT, DLRM-v2, and GPT-J. It is important to recognize that different benchmarks operate on varied scales. They have all been showcased in the following figures to offer a comprehensive overview.
Fig 1: System throughput for submitted systems for the data center suite
The following figure shows single-stream and MultiStream scenario results for the edge for ResNet, RetinaNet, 3D-Unet, RNN-T, and BERT 99 and GPTJ benchmarks. The lower the latency, the better the results.
Fig 2: System throughput for submitted systems for the edge
We have provided MLCommons-compliant submissions to the Inference 3.1 benchmark across various benchmarks and suites for all tasks in the benchmark such as image classification, object detection, natural language processing, speech recognition, recommender systems and medical image segmentation, and LLM summarization. These results indicate that with the newer generation of Dell PowerEdge servers such as the PowerEdge XE9680, XE8640, XE9640, and R760xa servers and newer GPUs from NVIDIA, end users can benefit from higher performance from their data center and edge inference deployments. We have also secured numerous Number 1 titles that make Dell PowerEdge servers an excellent choice for inference data center and edge deployments. End users can refer to different results across various servers to make performance and sizing decisions. With these results, Dell Technologies can help fuel enterprises’ AI transformation, including Generative AI adoption and expansion effectively.
More blogs that provide an in-depth comparison of the performance of specific models with different accelerators are on their way soon. For any questions or requests, contact your local Dell representative.