
Introduction to MLPerf™ Inference v1.0 Performance with Dell EMC Servers
Wed, 15 Sep 2021 12:09:44 -0000
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This blog provides MLPerf inference v1.0 data center closed results on Dell servers running the MLPerf inference benchmarks. Our results show optimal inference performance for the systems and configurations on which we chose to run inference benchmarks.
The MLPerf benchmarking suite measures the performance of machine learning (ML) workloads. Currently, these benchmarks provide a consistent way to measure accuracy and throughput for the following aspects of the ML life cycle:
- Training—The MLPerf training benchmark suite measures how fast a system can train ML models.
- Inference—The MLPerf inference benchmark measures how fast a system can perform ML inference by using a trained model in various deployment scenarios.
MLPerf is now a part of the MLCommons™ Association. MLCommons is an open engineering consortium that promotes the acceleration of machine learning innovation. Its open collaborative engineering solutions support your machine learning needs. MLCommons provides:
- Benchmarks and metrics
- Datasets and models
- Best practices
MLPerf inference overview
As of March 2021, MLPerf inference has submitted three versions: v0.5, v0.7, and v1.0. The latest version, v1.0, uses the same benchmarks as v0.7 with the following exceptions:
- Power submission—Power submission, which is a wrapper around inference submission, is supported.
- Error connection code (ECC)—The ECC must set to ON.
- 10-minute runtime—The default benchmark run time is 10 minutes.
- Required number of runs for submission and audit tests—The number of runs that are required to submit Server scenario is one.
v1.0 meets v0.7 requirements, therefore v1.0 results are comparable to v0.7 results. Because the MLPerf v1.0 submissions are more restrictive, the v0.7 results do not meet v1.0 requirements.
In the MLPerf inference evaluation framework, the LoadGen load generator sends inference queries to the system under test (SUT). In our case, the SUTs are Dell EMC servers with various GPU configurations. The SUTs uses a backend (for example, TensorRT, TensorFlow, or PyTorch) to perform inferencing and returns the results to LoadGen.
MLPerf has identified four different scenarios that enable representative testing of a wide variety of inference platforms and use cases. The main differences between these scenarios are based on how the queries are sent and received:
- Offline—One query with all samples is sent to the SUT. The SUT can send the results back once or multiple times in any order. The performance metric is samples per second.
- Server—The queries are sent to the SUT following a Poisson distribution (to model real-world random events). One query has one sample. The performance metric is queries per second (QPS) within latency bound.
- Single-stream—One sample per query is sent to the SUT. The next query is not sent until the previous response is received. The performance metric is 90th percentile latency.
- Multi-stream—A query with N samples is sent with a fixed interval. The performance metric is max N when the latency of all queries is within a latency bound.
MLPerf Inference Rules describes detailed inference rules and latency constraints. This blog focuses on Offline and Server scenarios, which are designed for data center environments. Single-stream and Multi-stream scenarios are designed for non-datacenter (edge and IoT) settings.
MLPerf inference results are submitted under either of the following divisions:
- Closed division—The Closed division provides a “like-to-like” comparison of hardware platforms or software frameworks. It requires using the same model and optimizer as the reference implementation.
The Closed division requires using preprocessing, postprocessing, and model that is equivalent to the reference or alternative implementation. It allows calibration for quantization and does not allow retraining. MLPerf provides a reference implementation of each benchmark. The benchmark implementation must use a model that is equivalent, as defined in MLPerf Inference Rules, to the model used in the reference implementation.
- Open division—The Open division promotes faster models and optimizers and allows any ML approach that can reach the target quality. It allows using arbitrary preprocessing or postprocessing and model, including retraining. The benchmark implementation may use a different model to perform the same task.
To allow the like-to-like comparison of Dell Technologies results and enable our customers and partners to repeat our results, we chose to test under the Closed division, as the results in this blog show.
Criteria for MLPerf Inference v1.0 benchmark result submission
For any benchmark, the result submission must meet all the specifications shown in the following table. For example, if we choose the Resnet50 model, then the submission must meet the 76.46 percent target accuracy and the latency must be within 15 ms for the standard image dataset with dimensions of 224 x 224 x 3.
Table 1: Closed division benchmarks for MLPerf inference v1.0 with expectations
Area | Task | Model | Dataset | QSL Size | Quality | Server latency constraint |
Vision | Image classification | Resnet50 – v1.5 | Standard image dataset (224 x 224 x3) | 1024 | 99% of FP32 (76.46%) | 15 ms |
Vision | Object detection (large) | SSD-Resnet34 | COCO (1200 x 1200) | 64 | 99% of FP32 (0.20 mAP) | 100 ms |
Vision | Medical image segmentation | 3D UNet | BraTs 2019 (224 x 224 x 160) | 16 | 99% of FP32 and 99.9% of FP32 (0.85300 mean DICE score) | N/A |
Speech | Speech-to-text | RNNT | Librispeech dev-clean (samples < 15 seconds) | 2513
| 99% of FP32 (1 - WER, where WER=7.452253714852645%)
| 1000 ms |
Language | Language processing | BERT | SQuAD v1.1 (max_seq_len=384) | 10833
| 99% of FP32 and 99.9% of FP32 (f1_score=90.874%) | 130 ms |
Commerce | Recommendation | DLRM | 1 TB Click Logs | 204800 | 99% of FP32 and 99.9% of FP32 (AUC=80.25%) | 30 ms |
It is not mandatory to submit all the benchmarks. However, if a specific benchmark is submitted, then all the required scenarios for that benchmark must also be submitted.
Each data center benchmark requires the scenarios in the following table:
Table 2: Tasks and corresponding required scenarios for data center benchmark suite in MLPerf inference v1.0.
Area | Task | Required scenario |
Vision | Image classification | Server, Offline |
Vision | Object detection (large) | Server, Offline |
Vision | Medical image segmentation | Offline |
Speech | Speech-to-text | Server, Offline |
Language | Language processing | Server, Offline |
Commerce | Recommendation | Server, Offline |
SUT configurations
We selected the following servers with different types of NVIDIA GPUs as our SUT to conduct data center inference benchmarks. The following table lists the MLPerf system configurations:
Table 3: MLPerf system configurations
Platform | Dell EMC DSS8440_A100 | Dell EMC DSS8440_A40 | PowerEdge R750xa | PowerEdge XE8545 |
MLPerf System ID | DSS8440_A100-PCIE-40GBx10_TRT | DSS8440_A40x10_TRT | R750xa_A100-PCIE-40GBx4_TRT | XE8545_7713_A100-SXM4-40GBx4 |
Operating system | CentOS 8.2.2004 | CentOS 8.2.2004 | CentOS 8.2.2004 | CentOS 8.2.2004 |
CPU | 2 x Intel Xeon Gold 6248 CPU @ 2.50 GHz | 2 x Intel Xeon Gold 6248R CPU @ 3.00 GHz | 2 x Intel Xeon Gold 6338 CPU @ 2.00 GHz | 2 x AMD EPYC 7713 |
Memory | 768 GB | 768 GB | 256 GB | 1 TB |
GPU | NVIDIA A100-PCIe-40GB | NVIDIA A40 | NVIDIA A100-PCIE-40GB | NVIDIA A100-SXM4-40GB |
GPU Form Factor | PCIE | PCIE | PCIE | SXM4 |
GPU count | 10 | 10 | 4 | 4 |
Software Stack | TensorRT 7.2.3, CUDA 11.1, cuDNN 8.1.1, Driver 460.32.03, DALI 0.30.0 | TensorRT 7.2.3, CUDA 11.1, cuDNN 8.1.1, Driver 460.32.03, DALI 0.30.0 | TensorRT 7.2.3, CUDA 11.1, cuDNN 8.1.1, Driver 460.32.03, DALI 0.30.0 | TensorRT 7.2.3, CUDA 11.1, cuDNN 8.1.1, Driver 460.32.03, DALI 0.30.0 |
MLPerf inference 1.0 benchmark results
The following graphs include performance metrics for the Offline and Server scenarios.
For the Offline scenario, the performance metric is Offline samples per second. For the Server scenario, the performance metric is queries per second (QPS). In general, the metrics represent throughput. A higher throughput is a better result.
Resnet50 results
Figure 1: Resnet50 v1.5 Offline and Server scenario with 99 percent accuracy target
Figure 2: Resnet50 v1.5 Offline and Server scenario with 99 percent accuracy target per card
Table 4: Per card numbers and scenario percentage difference
Dell Server | Offline throughput | Server throughput | Percentage difference between scenarios |
XE8545_7713_A100-SXM4-40GBx4 | 37800.5 | 33370.5 | 12.44 |
R750xa_A100-PCIE-40GBx4_TRT | 31834.25 | 28247 | 11.94 |
DSS8440_A100-PCIE-40GBx10_TRT | 29572.4 | 26399.8 | 11.33 |
DSS8440_A40x10_TRT | 19200 | 17698.3 | 8.139 |
The Offline per card throughput exceeds the Server per card throughput for all the servers in this study.
Table 5: Per card percentage difference from a XE8545_7713_A100-SXM4-40GBx4 system
Dell Server | Offline (in percentage) | Server (in percentage) |
XE8545_7713_A100-SXM4-40GBx4 | 0 | 0 |
R750xa_A100-PCIE-40GBx4_TRT | 17.13 | 16.63 |
DSS8440_A100-PCIE-40GBx10_TRT | 24.42 | 26.39 |
DSS8440_A40x10_TRT | 65.26 | 61.37 |
SSD-Resnet34 results
Figure 3: SSD with Resnet34 Offline and Server scenario with 99 percent accuracy target
Figure 4: SSD-Resnet34, Offline and Server scenario with 99 percent accuracy targets per card
Table 6: Per card numbers and scenario percentage difference on SSD-Resnet34
Dell Server | Offline throughput | Server throughput | Percentage difference between scenarios |
XE8545_7713_A100-SXM4-40GBx4 | 1189.945 | 950.4325 | 22.38 |
R750xa_A100-PCIE-40GBx4_TRT | 839.8275 | 750.3775 | 11.25 |
DSS8440_A100-PCIE-40GBx10_TRT | 761.179 | 826.478 | -8.22 |
DSS8440_A40x10_TRT | 475.978 | 400.236 | 17.28 |
Note: A negative value of percentage difference indicates the Server scenario outperformed the Offline scenario.
Table 7: Per card percentage difference from a XE8545_7713_A100-SXM4-40GBx4 system with an A100 SXM4 card
Dell Server | Offline (in percentage) | Server (in percentage) |
XE8545_7713_A100-SXM4-40GBx4 | 0 | 0 |
R750xa_A100-PCIE-40GBx4_TRT | 34.4982 | 23.52 |
DSS8440_A100-PCIE-40GBx10_TRT | 43.95067 | 13.95 |
DSS8440_A40x10_TRT | 85.71429 | 81.47 |
BERT Results
Figure 4: BERT Offline and Server scenario with 99 percent and 99.9 percent accuracy targets
Figure 5: BERT Offline and Server scenario with 99 percent and 99.9 percent accuracy targets per card
Table 8: Per card numbers and scenario percentage difference on BERT with 99 percent accuracy target
Dell Server | Offline throughput | Server throughput | Percentage difference between scenarios |
XE8545_7713_A100-SXM4-40GBx4 | 3586.275 | 3192.875 | 11.60617482 |
R750xa_A100-PCIE-40GBx4_TRT | 2932.25 | 2725.175 | 7.320468234 |
DSS8440_A100-PCIE-40GBx10_TRT | 2926.54 | 2674.86 | 8.986324847 |
DSS8440_A40x10_TRT | 1645.85 | 1390.02 | 16.85381785 |
Table 9: Per card percentage difference from an XE8545_7713_A100-SXM4-40GBx4 system with an A100 SXM4 card
Dell Server | 99% - Offline (in percentage) | 99% - Server (in percentage) |
XE8545_7713_A100-SXM4-40GBx4 | 0 | 0 |
R750xa_A100-PCIE-40GBx4_TRT | 20.06 | 15.8 |
DSS8440_A100-PCIE-40GBx10_TRT | 20.25 | 17.65 |
DSS8440_A40x10_TRT | 74.17 | 78.67 |
Table 10: Per card numbers and scenario percentage difference on BERT with 99.9 percent accuracy target
Dell Server | 99.9% - Offline throughput | 99.9% Server throughput | Percentage difference between scenarios |
XE8545_7713_A100-SXM4-40GBx4 | 1727.44 | 1575.35 | 9.2097893 |
R750xa_A100-PCIE-40GBx4_TRT | 1420.6225 | 1300.365 | 8.8392541 |
DSS8440_A100-PCIE-40GBx10_TRT | 1427.8 | 1211.94 | 16.354641 |
DSS8440_A40x10_TRT | 798.677 | 580.207 | 31.687945 |
Table 11: Per card percentage difference from an XE8545_7713_A100-SXM4-40GBx4 system with an A100 SXM4 card
Dell Server | 99.9% - Offline (in percentage) | 99.9% - Server (in percentage) |
XE8545_7713_A100-SXM4-40GBx4 | 0 | 0 |
R750xa_A100-PCIE-40GBx4_TRT | 19.49 | 19.12 |
DSS8440_A100-PCIE-40GBx10_TRT | 18.99 | 26.07 |
DSS8440_A40x10_TRT | 73.53 | 92.33 |
RNN-T Results
Figure 6: RNN-T Offline and Server scenario with 99 percent accuracy target
Figure 7: RNN-T Offline and Server scenario with 99 percent accuracy target per card
Table 12: Per card numbers and scenario percentage difference on RNNT with 99 percent accuracy target
Dell Server | Offline throughput | Server throughput | Percentage difference between scenarios |
XE8545_7713_A100-SXM4-40GBx4 | 13157.025 | 12421.025 | 5.754934 |
R750xa_A100-PCIE-40GBx4_TRT | 10872.675 | 10996.575 | -1.1331 |
DSS8440_A100-PCIE-40GBx10_TRT | 10726.9 | 10798.7 | -0.66711 |
DSS8440_A40x10_TRT | 5919.17 | 3739.11 | 45.14386 |
Note: A negative value for the percentage difference indicates that Server scenario performed better than Offline scenario.
Table 13: Per card percentage difference from an XE8545_7713_A100-SXM4-40GBx4 system with an A100 SXM4 card
Dell Server | Offline (in percentage) | Server (in percentage) |
XE8545_7713_A100-SXM4-40GBx4 | 0 | 0 |
R750xa_A100-PCIE-40GBx4_TRT | 19.01 | 12.16 |
DSS8440_A100-PCIE-40GBx10_TRT | 20.34 | 13.97 |
DSS8440_A40x10_TRT | 75.88 | 107.44 |
3D-UNet Results
Figure 8: 3D-UNet Offline and Server scenario with 99 percent and 99.9 percent accuracy target
Figure 9: 3D-UNet Offline and Server scenario with 99 percent and 99.9 percent accuracy target
Conclusion
In this blog, we quantified the MLCommons MLPerf inference v1.0 performance on Dell EMC DSS8440, PowerEdge R750xa, and PowerEdge XE8545 servers with A100 PCIE and SXM form factors using benchmarks such as Resnet50, SSD w/ Resnet34, BERT, RNN-T, and 3D-UNet. These benchmarks span tasks from vision to recommendation. Dell EMC servers delivered top inference performance normalized to processor count among commercially available results.
The PowerEdge XE8545 server outperforms the per card numbers of other servers in this study. This result can be attributed to its SXM GPU, which offers higher base and boost clock rate.
The SSD-Resnet34 image segmentation model benefits significantly from an SXM form factor-based GPU. The results show an approximate 34 percent performance difference compared to a PCIE from factor, relative to other models that average approximately 20 percent.
The PowerEdge R750xa server with an A100 GPU performs better in the Server scenario than in the Offline scenario for RNN-T model.
The DSS 8440 server with an A100 GPU performs better in the Server scenario than the Offline scenario for BERT, RNN-T, and SSD-Resnet34 models.
Furthermore, we found that the performance of the DSS8440 server with 10 x A100 PCIE cards exceeded other MLCommons MLPerf inference v1.0 submissions for the RNN-T Server benchmark.
Next Steps
In future blogs, we plan to describe how to:
- Run MLCommons MLPerf inference v1.0
- Understand MLCommons MLPerf inference results on recently released PowerEdge R750xa and PowerEdge XE8545 servers
- Run benchmarks on other servers
Related Blog Posts

Quantifying Performance of Dell EMC PowerEdge R7525 Servers with NVIDIA A100 GPUs for Deep Learning Inference
Tue, 17 Nov 2020 21:10:22 -0000
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The Dell EMC PowerEdge R7525 server provides exceptional MLPerf Inference v0.7 Results, which indicate that:
- Dell Technologies holds the #1 spot in performance per GPU with the NVIDIA A100-PCIe GPU on the DLRM-99 Server scenario
- Dell Technologies holds the #1 spot in performance per GPU with the NVIDIA A100-PCIe on the DLRM-99.9 Server scenario
- Dell Technologies holds the #1 spot in performance per GPU with the NVIDIA A100-PCIe on the ResNet-50 Server scenario
Summary
In this blog, we provide the performance numbers of our recently released Dell EMC PowerEdge R7525 server with two NVIDIA A100 GPUs on all the results of the MLPerf Inference v0.7 benchmark. Our results indicate that the PowerEdge R7525 server is an excellent choice for inference workloads. It delivers optimal performance for different tasks that are in the MLPerf Inference v0.7 benchmark. These tasks include image classification, object detection, medical image segmentation, speech to text, language processing, and recommendation.
The PowerEdge R7525 server is a two-socket, 2U rack server that is designed to run workloads using flexible I/O and network configurations. The PowerEdge R7525 server features the 2nd Gen AMD EPYC processor, supports up to 32 DIMMs, has PCI Express (PCIe) Gen 4.0-enabled expansion slots, and provides a choice of network interface technologies to cover networking options.
The following figure shows the front view of the PowerEdge R7525 server:
Figure 1. Dell EMC PowerEdge R7525 server
The PowerEdge R7525 server is designed to handle demanding workloads and for AI applications such as AI training for different kinds of models and inference for different deployment scenarios. The PowerEdge R7525 server supports various accelerators such as NVIDIA T4, NVIDIA V100S, NVIDIA RTX, and NVIDIA A100 GPU s. The following sections compare the performance of NVIDIA A100 GPUs with NVIDIA T4 and NVIDIA RTX GPUs using MLPerf Inference v0.7 as a benchmark.
The following table provides details of the PowerEdge R7525 server configuration and software environment for MLPerf Inference v0.7:
Component | Description |
Processor | AMD EPYC 7502 32-Core Processor |
Memory | 512 GB (32 GB 3200 MT/s * 16) |
Local disk | 2x 1.8 TB SSD (No RAID) |
Operating system | CentOS Linux release 8.1 |
GPU | NVIDIA A100-PCIe-40G, T4-16G, and RTX8000 |
CUDA Driver | 450.51.05 |
CUDA Toolkit | 11.0 |
Other CUDA-related libraries | TensorRT 7.2, CUDA 11.0, cuDNN 8.0.2, cuBLAS 11.2.0, libjemalloc2, cub 1.8.0, tensorrt-laboratory mlperf branch |
Other software stack | Docker 19.03.12, Python 3.6.8, GCC 5.5.0, ONNX 1.3.0, TensorFlow 1.13.1, PyTorch 1.1.0, torchvision 0.3.0, PyCUDA 2019.1, SacreBLEU 1.3.3, simplejson, OpenCV 4.1.1 |
System profiles | Performance |
For more information about how to run the benchmark, see Running the MLPerf Inference v0.7 Benchmark on Dell EMC Systems.
MLPerf Inference v0.7 performance results
The MLPerf inference benchmark measures how fast a system can perform machine learning (ML) inference using a trained model in various deployment scenarios. The following results represent the Offline and Server scenarios of the MLPerf Inference benchmark. For more information about different scenarios, models, datasets, accuracy targets, and latency constraints in MLPerf Inference v0.7, see Deep Learning Performance with MLPerf Inference v0.7 Benchmark.
In the MLPerf inference evaluation framework, the LoadGen load generator sends inference queries to the system under test, in our case, the PowerEdge R7525 server with various GPU configurations. The system under test uses a backend (for example, TensorRT, TensorFlow, or PyTorch) to perform inferencing and sends the results back to LoadGen.
MLPerf has identified four different scenarios that enable representative testing of a wide variety of inference platforms and use cases. In this blog, we discuss the Offline and Server scenario performance. The main differences between these scenarios are based on how the queries are sent and received:
- Offline—One query with all samples is sent to the system under test. The system under test can send the results back once or multiple times in any order. The performance metric is samples per second.
- Server—Queries are sent to the system under test following a Poisson distribution (to model real-world random events). One query has one sample. The performance metric is queries per second (QPS) within latency bound.
Note: Both the performance metrics for Offline and Server scenario represent the throughput of the system.
In all the benchmarks, two NVIDIA A100 GPUs outperform eight NVIDIA T4 GPUs and three NVIDIA RTX800 GPUs for the following models:
- ResNet-50 image classification model
- SSD-ResNet34 object detection model
- RNN-T speech recognition model
- BERT language processing model
- DLRM recommender model
- 3D U-Net medical image segmentation model
The following graphs show PowerEdge R7525 server performance with two NVIDIA A100 GPUs, eight NVIDIA T4 GPUs, and three NVIDIA RTX8000 GPUs with 99% accuracy target benchmarks and 99.9% accuracy targets for applicable benchmarks:
- 99% accuracy (default accuracy) target benchmarks: ResNet-50, SSD-Resnet34, and RNN-T
- 99% and 99.9% accuracy (high accuracy) target benchmarks: DLRM, BERT, and 3D-Unet
99% accuracy target benchmarks
ResNet-50
The following figure shows results for the ResNet-50 model:
Figure 2. ResNet-50 Offline and Server inference performance
From the graph, we can derive the per GPU values. We divide the system throughput (containing all the GPUs) by the number of GPUs to get the Per GPU results as they are linearly scaled.
SSD-Resnet34
The following figure shows the results for the SSD-Resnet34 model:
Figure 3. SSD-Resnet34 Offline and Server inference performance
RNN-T
The following figure shows the results for the RNN-T model:
Figure 4. RNN-T Offline and Server inference performance
99.9% accuracy target benchmarks
DLRM
The following figures show the results for the DLRM model with 99% and 99.9% accuracy:
Figure 5. DLRM Offline and Server Scenario inference performance – 99% and 99.9% accuracy
For the DLRM recommender and 3D U-Net medical image segmentation (see Figure 7) models, both 99% and 99.9% accuracy have the same throughput. The 99.9% accuracy benchmark also satisfies the required accuracy constraints with the same throughput as that of 99%.
BERT
The following figures show the results for the BERT model with 99% and 99.9% accuracy:
Figure 6. BERT Offline and Server inference performance – 99% and 99.9% accuracy
For the BERT language processing model, two NVIDIA A100 GPUs outperform eight NVIDIA T4 GPUs and three NVIDIA RTX8000 GPUs. However, the performance of three NVIDIA RTX8000 GPUs is a little better than that of eight NVIDIA T4 GPUs.
3D U-Net
For the 3D-Unet medical image segmentation model, only the Offline scenario benchmark is available.
The following figure shows the results for the 3D U-Net model Offline scenario:
Figure 7. 3D U-Net Offline inference performance
For the 3D-Unet medical image segmentation model, since there is only offline scenario benchmark for 3D-Unet the above graph represents only Offline scenario.
The following table compares the throughput between two NVIDIA A100 GPUs, eight NVIDIA T4 GPUs, and three NVIDIA RTX8000 GPUs with 99% accuracy target benchmarks and 99.9% accuracy targets:
Model | Scenario | Accuracy | 2 x A100 GPUs vs 8 x T4 GPUs | 2 x A100 GPUs vs 3 x RTX8000 GPUs |
ResNet-50 | Offline | 99% | 5.21x | 2.10x |
Server | 4.68x | 1.89x | ||
SSD-Resnet34 | Offline | 6.00x | 2.35x | |
Server | 5.99x | 2.21x | ||
RNN-T | Offline | 5.55x | 2.14x | |
Server | 6.71x | 2.43x | ||
DLRM | Offline | 6.55x | 2.52x | |
Server | 5.92x | 2.47x | ||
Offline | 99.9% | 6.55x | 2.52x | |
Server | 5.92x | 2.47x | ||
BERT | Offline | 99% | 6.26x | 2.31x |
Server | 6.80x | 2.72x | ||
Offline | 99.9% | 7.04x | 2.22x | |
Server | 6.84x | 2.20x | ||
3D U-Net | Offline | 99% | 5.05x | 2.06x |
Server | 99.9% | 5.05x | 2.06x |
Conclusion
With support of NVIDIA A100, NVIDIA T4, or NVIDIA RTX8000 GPUs, Dell EMC PowerEdge R7525 server is an exceptional choice for various workloads that involve deep learning inference. However, the higher throughput that we observed with NVIDIA A100 GPUs translates to performance gains and faster business value for inference applications.
Dell EMC PowerEdge R7525 server with two NVIDIA A100 GPUs delivers optimal performance for various inference workloads, whether it is in a batch inference setting such as Offline scenario or Online inference setting such as Server scenario.
Next steps
In future blogs, we will discuss sizing the system (server and GPU configurations) correctly based on the type of workload (area and task).

MLPerf™ Inference v2.0 Edge Workloads Powered by Dell PowerEdge Servers
Fri, 06 May 2022 19:54:11 -0000
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Abstract
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.
Introduction
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
Performance discussion
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.
Power Discussion
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.
Conclusion
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.
Appendix
SUT configuration
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
Platform | PowerEdge XE2420 1x T4, TensorRT | PowerEdge XR12 1x A2, TensorRT | PowerEdge XR12 1x A2, MaxQ, TensorRT | PowerEdge XE2420 2x A30, TensorRT |
MLPerf system ID | XE2420_T4x1_edge_TRT | XR12_edge_A2x1_TRT | XR12_A2x1_TRT_MaxQ | XE2420_A30x2_TRT |
Operating system | CentOS 8.2.2004 | Ubuntu 20.04.4 | ||
CPU | Intel Xeon Gold 6238 CPU @ 2.10 GHz | Intel Xeon Gold 6312U CPU @ 2.40 GHz | Intel Xeon Gold 6252N CPU @ 2.30 GHz | |
Memory | 256 GB | 1 TB | ||
GPU | NVIDIA T4 | NVIDIA A2 | NVIDIA A30 | |
GPU form factor | PCIe | |||
GPU count | 1 | 2 | ||
Software stack | TensorRT 8.4.0 CUDA 11.6 cuDNN 8.3.2 Driver 510.47.03 DALI 0.31.0 |
Table 2: MLPerf Inference v1.1 system configuration of the PowerEdge XE8545 server
Platform | PowerEdge XE8545 4x A100-SXM-80GB-7x1g.10gb, TensorRT, Triton |
MLPerf system ID | XE8545_A100-SXM-80GB-MIG_28x1g.10gb_TRT_Triton |
Operating system | Ubuntu 20.04.2 |
CPU | AMD EPYC 7763 |
Memory | 1 TB |
GPU | NVIDIA A100-SXM-80GB (7x1g.10gb MIG) |
GPU form factor | SXM |
GPU count | 4 |
Software stack | TensorRT 8.0.2 CUDA 11.3 cuDNN 8.2.1 Driver 470.57.02 DALI 0.31.0 |