Abstract

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

Configuration comparison

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

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

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

ResNet 50

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

BERT

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

SSD-ResNet 34

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

DLRM

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

RNNT

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

3D U-Net

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.

Results summary

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.

Conclusion

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.

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.