Dell Technologies has been an active participant in the MLCommons™ Inference benchmark submission since day one. We have completed five rounds of inference submission.

This blog provides an overview of the latest results of MLPerf Inference v2.0 closed data center, closed data center power, closed edge, and closed edge power categories on Dell servers from our HPC & AI Innovation Lab. It shows optimal inference and power (performance per watt) performance for Dell GPU-based servers (DSS 8440, PowerEdge R750xa, PowerEdge XE2420, PowerEdge XE8545, and PowerEdge XR12). The previous blog about MLPerf Inference v1.1 performance results can be found here.

What is new?

• There were 3,800 performance results for this round compared to 1,800 performance results for v1.1. Additionally, 885 systems in v2.0 compared to 424 systems in v1.1 shows that there were more than twice the systems submitted for this round. 
• For the 3D U-Net benchmark, the dataset now used is the KiTs 2019 Kidney Tumor Segmentation set.
• Early stopping was introduced in this round to replace a deterministic minimum query count with a function that dynamically determines when further runs are not required to identify additional performance gain.

Results at a glance

 Dell Technologies submitted 167 results to the various categories. The Dell team made 86 submissions to the closed data center category, 28 submissions to the closed data center power category, and 53 submissions to the closed edge category. For the closed data center category, the Dell team submitted the second most results. In fact, Dell Technologies submitted results from 17 different system configurations with the NVIDIA TensorRT and NVIDIA Triton inference engines. Among these 17 configurations, the PowerEdge XE2420 server with T4 and A30 GPUs and the PowerEdge XR12 server with the A2 GPU were two new systems that have not been submitted before. Additionally, Dell Technologies submitted to the reintroduced Multiterm scenario. Only Dell Technologies submitted results for different host operating systems.

Noteworthy results

Noteworthy results include:

• The PowerEdge XE8545 and R750xa servers yield Number One results for performance per accelerator with NVIDIA A100 GPUs. The use cases for this top classification include Image Classification, Object Detection, Speech-to-text, Medical Imaging, Natural Language Processing, and Recommendation.
• The DSS 8440 server yields Number Two results for system performance for multiple benchmarks including Speech-to-text, Object Detection, Natural Language Processing, and Medical Image Segmentati on uses cases among all submissions.
• The PowerEdge R750xa server yields Number One results for the highest system performance for multiple benchmarks including Image Classification, Object Detection, Speech-to-text, Natural Language Processing, and Recommendation use cases among all the PCIe-based GPU servers.
• The PowerEdge XE8545 server yields Number One results for the lowest multistrand latency with NVIDIA Multi-Instance GPU (MIG) in the edge category for the Image Classification and Object Detection use cases.
• The PowerEdge XE2420 server yields Number One results for the highest T4 GPU inference results for the Image Classification, Speech-to-text, and Recommendation use cases.
• The PowerEdge XR12 server yields Number One results for the highest performance per watt with NVIDIA A2 GPU results in power for the Image Classification, Object Detection, Speech-to-text, Natural Language Processing, and Recommendation use cases.

MLPerf Inference v2.0 benchmark results

The following graphs highlight the performance metrics for the Server and Offline scenarios across the various benchmarks from MLCommons. Dell Technologies presents results as an method for our customers to identify options to suit their deep learning application demands. Additionally, this performance data serves as a reference point to enable sizing of deep learning clusters. Dell Technologies strives to submit as many results as possible to offer answers to ensure that customer questions are resolved.

For the Server scenario, the performance metric is queries per second (QPS). For the Offline scenario, the performance metric is Offline samples per second. In general, the metrics represent throughput, and a higher throughput indicates a better result. In the following graphs, the Y axis is an exponentially scaled axis representing throughput and the X axis represents the systems under test (SUTs) and their corresponding models. The SUTs are described in the appendix.

Figure 1 through Figure 6 show the per card performance of the various SUTs on the ResNet 50, BERT, SSD, 3dUnet, RNNT, and DLRM modes respectively in the Server and Offline scenarios:

Figure 1: MLPerf Inference v2.0 ResNet 50 per card results

Figure 2: MLPerf Inference v2.0 BERT default and high accuracy per card results

Figure 3: MLPerf Inference v2.0 SSD-ResNet 34 per card results

Figure 4: MLPerf Inference v2.0 3D U-Net per card results

Figure 5: MLPerf Inference v2.0 RNNT per card results

Figure 6: MLPerf Inference v2.0 DLRM default and high accuracy per card results

Observations

The results in this blog have been officially submitted to and accepted by the MLCommons organization. These results have passed compliance tests, been peer reviewed, and adhered to the constraints enforced by MLCommons. Customers and partners can reproduce our results by following steps to run MLPerf Inference v2.0 in its GitHub repository.

Submissions from Dell Technologies included approximately 140 performance results and 28 performance and power results. Across the various workload tasks including Image Classification, Object Detection, Medical Image Segmentation, Speech-to-text, Language Processing, and Recommendation, server performance from Dell Technologies was promising.

Dell servers performed with optimal performance and power results. They were configured with different GPUs such as:

• NVIDIA A30 Tensor Core GPU
• NVIDIA A100 (PCIe and SXM)
• NVIDIA T4 Tensor Core GPU
• NVIDIA A2 Tensor Core GPU, which is newly released

More information about performance for specific configurations that are not discussed in this blog can be found in the v1.1 or v1.0 results.

The submission included results from different inference backends such as NVIDIA TensorRT and NVIDIA Triton. The appendix provides a summary of the full hardware and software stacks.

Conclusion

This blog quantifies the performance of Dell servers in the MLPerf Inference v2.0 round of submission. Readers can use these results to make informed planning and purchasing decisions for their AI workload needs.

Appendix

Software stack

The NVIDIA Triton Inference Server is an open-source inferencing software tool that aids in the deployment and execution of AI models at scale in production. Triton not only works with all major frameworks but also with customizable backends, further enabling developers to focus on their AI development. It is a versatile tool because it supports any inference type and can be deployed on any platform including CPU, GPU, data center, cloud, or edge. Additionally, Triton supports the rapid and reliable deployment of AI models at scale by integrating well with Kubernetes, Kubeflow, Prometheus, and Grafana. Triton supports the HTTP/REST and GRPC protocols that allow remote clients to request inferencing for any model that the server manages.

The NVIDIA TensorRT SDK delivers high-performance deep learning inference that includes an inference optimizer and runtime. It is powered by CUDA and offers a unified solution to deploy on various platforms including edge or data center. TensorRT supports the major frameworks including PyTorch, TensorFlow, ONNX, and MATLAB. It can import models trained in these frameworks by using integrated parsers. For inference, TensorRT performs orders of magnitude faster than its CPU-only counterparts.

NVIDIA MIG can partition GPUs into several instances that extend compute resources among users. MIG enables predictable performance and maximum GPU use by running jobs simultaneously on the different instances with dedicated resources for compute, memory, and memory bandwidth.

SUT configuration

The following table describes the SUT from this round of data center inference submission:

Table 1: MLPerf Inference v2.0 system configurations for DSS 8440 and PowerEdge R750xa servers

Table 2: MLPerf Inference v2.0 system configurations for PowerEdge XE2420 servers

Table 3: MLPerf Inference v2.0 system configurations for PowerEdge XE8545 servers

Table 4: MLPerf Inference v2.0 system configurations for PowerEdge XR12 servers

Dell Technologies has released the new PowerEdge R750xa server, a GPU workload-based platform that is designed to support artificial intelligence, machine learning, and high-performance computing solutions. The dual socket/2U platform supports 3^rd Gen Intel Xeon processors (code named Ice Lake). It supports up to 40 cores per processor, has eight memory channels per CPU, and up to 32 DDR4 DIMMs at 3200 MT/s DIMM speed. This server can accommodate up to four double-width PCIe GPUs that are located in the front left and the front right of the server.

Compared with the previous generation PowerEdge C4140 and PowerEdge R740 GPU platform options, the new PowerEdge R750xa server supports larger storage capacity, provides more flexible GPU offerings, and improves the thermal requirement

Figure 1 PowerEdge R750xa server

The NVIDIA A100 GPUs are built on the NVIDIA Ampere architecture to enable double precision workloads. This blog evaluates the new PowerEdge R750xa server and compares its performance with the previous generation PowerEdge C4140 server.

The following table shows the specifications for the NVIDIA GPU that is discussed in this blog and compares the performance improvement from the previous generation.

Table 1 NVIDIA GPU specifications

*with sparsity

Test bed and applications

This blog quantifies the performance improvement of the GPUs with the new PowerEdge GPU platform.

Using a single node PowerEdge R750xa server in the Dell HPC & AI Innovation Lab, we derived all results presented in this blog from this test bed. This section describes the test bed and the applications that were evaluated as part of the study. The following table provides test environment details:

Table 2 Server configuration

The following table provides information about the applications and benchmarks used:

Table 3 Benchmark and application details

LAMMPS

Large-Scale Atomic/Molecular Massively Parallel simulator (LAMMPS) is distributed by Sandia National Labs and the US Department of Energy. LAMMPS is open-source code that has different accelerated models for performance on CPUs and GPUs. For our test, we compiled the binary using the KOKKOS package, which runs efficiently on GPUs.

Figure 2 LAMMPS Performance on PowerEdge R750xa and PowerEdge C4140 servers

With the newer generation GPUs, this application improves 2.4 times compared to single GPU performance. The overall performance from a single server improved twice with the PowerEdge R750xa server and NVIDIA A100 GPUs.

GROMACS

GROMACS is a free and open-source parallel molecular dynamics package designed for simulations of biochemical molecules such as proteins, lipids, and nucleic acids. It is used by a wide variety of researchers, particularly for biomolecular and chemistry simulations. GROMACS supports all the usual algorithms expected from modern molecular dynamics implementation. It is open-source software with the latest versions available under the GNU Lesser General Public License (LGPL).

Figure 3 GROMACS performance on PowerEdge C4140 and r750xa servers

With the newer generation GPUs, this application improved approximately 1.5 times across the dataset compared to single GPU performance. The overall performance from a single server improved 1.5 times with a PowerEdge R750xa server and NVIDIA A100 GPUs.

High-Performance Linpack

High-Performance Linpack (HPL) needs no introduction in the HPC arena. It is a widely used standard benchmark tests in the industry.  

 Figure 4 HPL Performance on the PowerEdge R750xa server with A100 GPU and PowerEdge C4140 server with V100 GPU

Figure 5 Power use of the HPL running on NVIDIA GPUs

From Figure 4 and Figure 5, the following results were observed: 

• Performance—For GPU count, the NVIDIA A100 GPU demonstrates twice the performance of the NVIDIA V100 GPU. Higher memory size, double precision FLOPS, and a newer architecture contribute to the improvement for the NVIDIA A100 GPU.
• Scalability—The PowerEdge R750xa server with four NVIDIA A100-PCIe-40 GB GPUs delivers 3.6 times higher HPL performance compared to one NVIDIA A100-PCIE-40 GB GPU. The NVIDIA A100 GPUs scale well inside the PowerEdge R750xa server for the HPL benchmark.  
• Higher Rpeak—The HPL code on NVIDIA A100 GPUs uses the new double-precision Tensor cores. The theoretical peak for each GPU is 19.5 TFlops, as opposed to 9.7 TFlops. 
• Power—Figure 5 shows power consumption of a complete HPL run with the PowerEdge R750xa server using four A100-PCIe GPUs. This result was measured with iDRAC commands, and the peak power consumption was observed as 2022 Watts. Based on our previous observations, we know that the PowerEdge C4140 server consumes approximately 1800 W of power.

HPCG

Figure 6 Scaling GPU performance data for HPCG Benchmark

As discussed in other blogs, high performance conjugate gradient (HPCG) is another standard benchmark to test data access patterns of sparse matrix calculations. From the graph, we see that the HPCG benchmark scales well with this benchmark resulting in 1.6 times performance improvement over the previous generation PowerEdge C4140 server with an NVIDIA V100 GPU.

The 72 percent improvement in memory bandwidth of the NVIDIA A100 GPU over the NVIDIA V100 GPU contributes to the performance improvement.

Conclusion

In this blog, we introduced the latest generation PowerEdge R750xa platform and discussed the performance improvement over the previous generation PowerEdge C4140 server. The PowerEdge R750xa server is a good option for customers looking for an Intel Xeon scalable CPU-based platform powered with NVIDIA GPUs.

With the newer generation PowerEdge R750xa server and NVIDIA A100 GPUs, the applications discussed in this blog show significant performance improvement.

Next steps

In future blogs, we plan to evaluate NVLINK bridge support, which is another important feature of the PowerEdge R750xa server and NVIDIA A100 GPUs.