Dell PowerEdge Servers Achieve Stellar Scores with MLPerf™ Training v3.1
Wed, 08 Nov 2023 17:43:48 -0000
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Abstract
MLPerf is an industry-standard AI performance benchmark. For more information about the MLPerf benchmarks, see Benchmark Work | Benchmarks MLCommons.
Today marks the release of a new set of results for MLPerf Training v3.1. The Dell PowerEdge XE9680, XE8640, and XE9640 servers in the submission demonstrated excellent performance. The tasks included image classification, medical image segmentation, lightweight and heavy-weight object detection, speech recognition, language modeling, recommendation, and text to image. MLPerf Training v3.1 results provide a baseline for end users to set performance expectations.
What is new with MLPerf Training 3.1 and the Dell Technologies submissions?
The following are new for this submission:
- For the benchmarking suite, a new benchmark was added: stable diffusion with the Laion400 dataset.
- Dell Technologies submitted the newly introduced Liquid Assisted Air Cooled (LAAC) PowerEdge XE9640 system, which is a part of the latest generation Dell PowerEdge servers.
Overview of results
Dell Technologies submitted 30 results. These results were submitted using five different systems. We submitted results for the PowerEdge XE9680, XE8640, and XE9640 servers. We also submitted multinode results for the PowerEdge XE9680 and XE8640 servers. The PowerEdge XE9680 server was powered by eight NVIDIA H100 Tensor Core GPUs, while the PowerEdge XE8640 and XE9640 servers were powered by four NVIDIA H100 Tensor Core GPUs each.
Datapoints of interest
Interesting datapoints include:
- Our new stable diffusion results with the PowerEdge XE9680 server have been submitted for the first time and are exclusive. Dell Technologies, NVIDIA, and Habana Labs are the only submitters to have made an official submission. This submission is important because of the explosion of Generative AI workloads. The submission uses the NVIDIA NeMo framework, included in NVIDIA AI Enterprise for secure, supported, and stable production AI.
- Dell PowerEdge XE8640 and XE9640 servers secured several top performer titles (#1 titles) among other systems equipped with four NVIDIA H100 GPUs. The tasks included language modeling, recommendation, heavy-weight object detection, speech to text, and medical image segmentation.
- A number of multinode results were submitted for the previous round, which can be compared with this round. PowerEdge XE9680 multinode results were submitted. Additionally, this round was the first time multinode results with the newer generation PowerEdge XE8640 servers were submitted. The results show near linear scaling. Furthermore, Dell Technologies is the only submitter in addition to NVIDIA, Habana Labs, and Intel making multinode, on-premises result submissions.
- The results for the PowerEdge XE9640 server with liquid assisted air cooling (LAAC) are similar to the PowerEdge XE8640 air-cooled server.
The following figure shows all the convergence times for Dell systems and corresponding workloads in the benchmark. Because different benchmarks are included in the same graph, the y axis is expressed logarithmically. Overall, these numbers show an excellent time to converge for the workload in question.
Figure 1. Logarithmic y axis: Overview of Dell MLPerf Training v3.1 results
Conclusion
We submitted compliant results for the MLCommons Training v3.1 benchmark. These results are based on the latest generation of Dell PowerEdge XE9680, XE8640, and XE9640 servers, powered by NVIDIA H100 Tensor Core GPUs. All results are stellar. They demonstrate that multinode scaling is linear and that more servers can help to solve the same problem faster. Different results allow end users to make decisions about expected performance before deploying their compute-intensive training workloads. The workloads in the submission include image classification, medical image segmentation, lightweight and heavy-weight object detection, speech recognition, language modeling, recommendation, and text to image. Enterprises can enable and maximize their AI transformation with Dell Technologies efficiently with Dell solutions.
MLCommons Results
https://mlcommons.org/benchmarks/training/
The preceding graphs are MLCommons results for MLPerf IDs from 3.1-2005 to 3.1-2009.
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