Dell EMC Servers Shine in MLPerf Inference v0.7 Benchmark
Mon, 02 Nov 2020 15:16:55 -0000
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As software applications and systems using Artificial Intelligence (AI) gain mainstream adoption across all industries, inference workloads for ongoing operations are becoming a larger resource consumer in the datacenter. MLPerf is a benchmark suite that is used to evaluate the performance profiles of systems for both training and inference AI tasks. In this blog we take a closer look at the recent results submitted by Dell EMC and how our various servers performed in the datacenter category.
The reason we do this type of work is to help customers understand which server platform makes the most sense for their use case. Dell Technologies wants to make the choice easier and reduce work for our customers, so they don’t waste their precious resources. We want customers to use their time focusing on the use case helping accelerate time to value for the business.
Dell Technologies has a total of 210 submissions for MLPerf Inference v0.7 in the Datacenter category using various server platforms and accelerators. Why so many? It is because many customers have never run AI in their environment, the use cases are endless across industries and expertise limited. Customers have told us they need help identifying the correct server platform based on their workloads.
We’re proud of what we’ve done, but it’s still all about helping customers adopt AI. By sharing our expertise and providing guidance on infrastructure for AI, we help customers become successful and get their use case into production.
MLPerf Benchmarks
MLPerf was founded in 2018 with a goal of accelerating improvements in ML system performance. Formed as a collaboration of companies and researchers from leading educational institutions, MLPerf leverages open source code, public state-of-the-art Machine Learning (ML) models and publicly available datasets contributed to the ML community. The MLPerf suites include MLPerf Training and MLPerf Inference.
MLPerf Training measures how fast a system can train machine learning models. Training benchmarks have been defined for image classification, lightweight and heavy-weight object detection, language translation, natural language processing, recommendation and reinforcement learning. Each benchmark includes specifications for input datasets, quality targets and reference implementation models. The first round of training submissions was published on the MLPerf website in December 2018 with results submitted by Google, Intel and NVIDIA.
The MLPerf Inference suite measures how quickly a trained neural network can evaluate new data and perform forecasting or classification for a wide range of applications. MLPerf Inference includes image classification, object detection and machine translation with specific models, datasets, quality, server latency and multi-stream latency constraints. MLPerf validated and published results for MLPerf Inference v0.7 on October 21, 2020. In this blog we take a closer look at the for MLPerf Inference v0.7 results submitted by Dell EMC and how the servers performed in the datacenter category.
A summary of the key highlights of the Dell EMC results are shown in Table 1. These are derived from the submitted results in MLPerf datacenter closed category. Ranking and claims are based on Dell analysis of published MLPerf data. Per accelerator is calculated by dividing the primary metric of total performance by the number of accelerators reported.
Rank | Category | Specifics | Use Cases |
#1 | Performance per Accelerator for NVIDIA A100-PCIe | PowerEdge R7525 | Medical Imaging, Image Classification |
#1 | Performance per Accelerator with NVIDIA T4 GPUs | PowerEdge XE2420, PowerEdge R7525, DSS8440 | Medical Imaging, NLP, Image Classification, Speech Recognition, Object Detection, Recommendation |
#1 | Highest inference results with Quadro RTX6000 and RTX8000 | PowerEdge R7525, DSS 8440 | Medical Imaging, NLP, Image Classification, Speech Recognition, Object Detection, Recommendation |
Dell EMC had a total of 210 submissions for MLPerf Inference v0.7 in the Datacenter category using various Dell EMC platforms and accelerators from leading vendors. We achieved impressive results when compared to other submissions in the same class of platforms.
MLPerf Inference Categories and Dell EMC Achievements
A benchmark suite is made up of tasks or models from vision, speech, language and commerce use cases. MLPerf Inference measures how fast a system can perform ML inference by using a load generator against the System Under Test (SUT) where the trained model is deployed.
There are three types of benchmark tests defined in MLPerf inference v0.7, one for datacenter systems, one for edge systems and one for mobile systems. MLPerf then has four different scenarios to enable representative testing of a wide variety of inference platforms and use cases:
- Single stream
- Multiple stream
- Server
- Offline
The single stream and multiple stream scenarios are only used for edge and mobile inference benchmarks. The data center benchmark type targets systems designed for data center deployments and requires evaluation of both the server and offline scenarios. The metrics used in the Datacenter category are inference operations/second. In the server scenario, the MLPerf load generator sends new queries to the SUT according to a Poisson distribution. This is representative of on-line AI applications such as translation, image tagging which have variable arrival patterns based on end-user traffic. Offline represents AI tasks done thru batch processing such as photo categorization where all the data is readily available ahead of time.
Dell EMC published multiple results in the datacenter systems category. Details on the models, dataset and the scenarios submitted for the different datacenter benchmark are shown in Table 2
Area | Task | Model | Dataset | Required Scenarios |
Vision | Image classification | ResNet50-v1.5 | Imagenet (224x224) | Server, Offline |
Object detection (large) | SSD-ResNet34 | COCO (1200x1200) | Server, Offline | |
Medical image segmentation | 3d Unet | BraTS 2019 (224x224x160) | Offline | |
Speech | Speech-to-text | RNNT | Librispeech dev-clean (samples < 15 seconds) | Server, Offline |
Language | Language processing | BERT | SQuAD v1.1 (max_seq_len=384) | Server, Offline |
Commerce | Recommendation | DLRM | 1TB Click Logs | Server, Offline |
Next we highlight some of the key performance achievements for the broad range of solutions available in the Dell EMC portfolio for inference use cases and deployments.
1. Dell EMC is #1 in total number of datacenter submissions in the closed division including bare metal submissions using different GPUs, Xeon CPUs, Xilinx FPGA and virtualized submission on VMware vSphere
The closed division enables head to head comparisons and consists of server platforms used from the Edge to private or public clouds. The Dell Technologies engineering team submitted 210 out of the total 509 results.
We remain committed to helping customers deploy inference workloads as efficiently as possible, meeting their unique requirements of power, density, budget and performance. The wide range of servers submitted by Dell Technologies’ is a testament to this commitment -
- The only vendor with submissions for a variety of inference solutions – leveraging GPU, FPGA and CPUs for the datacenter/private cloud and Edge
- Unique in the industry by submitting results across a multitude of servers that range from mainstream servers (R740/R7525) to dense GPU-optimized servers supporting up to 16 NVIDIA GPUs (DSS8440).
- Demonstrated that customers that demand real-time inferencing at the telco or retail Edge can deploy up to 4 GPUs in a short depth NEBS-compliant PowerEdge XE2420 server.
- Demonstrated efficient Inference performance using the 2nd Gen Intel Xeon Scalable platform on the PowerEdge R640 and PowerEdge R740 platforms for customers wanting to run inference on Intel CPUs.
- Dell submissions using Xilinx U280 in PowerEdge R740 demonstrated that customers wanting low latency inference can leverage FPGA solutions.
2. Dell EMC is #1 in performance “per Accelerator” with PowerEdge R7525 and A100-PCIe for multiple benchmarks
The Dell EMC PowerEdge R7525 was purpose-built for superior accelerated performance. The MLPerf results validated leading performance across many scenarios including:
Performance Rank “Per Accelerator” | Inference Throughput | Dell EMC System |
#1 ResNet50 (server) | 30,005 | PowerEdge R7525 (3x NVIDIA A100-PCIE) |
#1 3D-Unet-99 (offline) | 39 | PowerEdge R7525 (3x NVIDIA A100-PCIE) |
#1 3D-Unet-99 (offline) | 39 | PowerEdge R7525 (3x NVIDIA A100-PCIE) |
#2 DLRM-99 (server) | 192,543 | PowerEdge R7525 (2x NVIDIA A100-PCIE) |
#2 DLRM-99 (server) | 192,543 | PowerEdge R7525 (2x NVIDIA A100-PCIE) |
3. Dell achieved the highest inference scores with NVIDIA Quadro RTX GPUs using the DSS 8440 and R7525
Dell Technologies engineering understands that since training isn’t the only AI workload, using the right technology for each job is far more cost effective. Dell is the only vendor to submit results using NVIDIA RTX6000 and RTX8000 GPUs that provide up to 48GB HBM memory for large inference models. The DSS 8440 with 10 Quadro RTX achieved
- #2 and #3 highest system performance on RNN-T for Offline scenario.
The #1 ranking was delivered using 8x NVIDIA A100 SXM4 that was introduced in May 2020 and is a powerful system for customer to train state of the art deep learning models. Dell Technologies took the #2 and #3 spots with the DSS8440 server equipped with 10x NVIDIA RTX8000 and DSS8440 with 10x NVIDIA RTX6000 providing a better power and cost efficiency for inference workloads compared to other submissions.
4. Dell EMC claims #1 spots for NVIDIA T4 platforms with DSS 8440, XE2420 and PowerEdge R7525
Dell Technologies provides system options for customers to deploy inference workloads that match their unique requirements. Today’s accelerators vary significantly in price, performance and power consumption. For example, the NVIDIA T4 is a low profile, lower power GPU option that is widely deployed for inference due to its superior power efficiency and economic value for that use case.
The MLPerf results corroborate the exemplary inference performance of NVIDIA T4 on Dell EMC Servers. The T4 leads for performance per GPU among the 20 servers used to submit scores using NVIDIA T4 GPUs
- #1 in performance per GPU on 3d-unet-99 and 3d-unet-99.9 Offline scenario
- #1 in performance per GPU on Bert-99 Server and Bert-99.9 Offline scenario
- #1, #2 and #3 in performance with T4 on DLRM-99 & DLRM-99.9 Server scenario
- #1 in performance per GPU on ResNet50 Offline scenario
- #1 in performance per GPU on RNN-T Server and Offline scenario
- #1 in performance per GPU on SSD-large Offline scenario
The best scores achieved for the NVIDIA T4 “Per GPU” rankings above and respective platforms are shown in the table:
Benchmark | Offline Scenario | Server Scenario | ||||
Rank | Throughput | Server | Rank | Throughput | Server | |
3d-unet-99 | #1 | 7.6 | XE2420 | n/a | ||
3d-unet-99.9 | #1 | 7.6 | XE2420 | n/a |
|
|
bert-99 | #3 | 449 | XE2420 | #1 | 402 | XE2420 |
bert-99.9 | #1 | 213 | DSS 8440 | #2 | 190 | XE2420 |
dlrm-99 | #2 | 35,054 | XE2420 | #1 | 32,507 | R7525 |
dlrm-99.9 | #2 | 35,054 | XE2420 | #1 | 32,507 | R7525 |
resnet | #1 | 6,285 | XE2420 | #4 | 5,663 | DSS 8440 |
rnnt | #1 | 1,560 | XE2420 | #1 | 1,146 | XE2420 |
ssd-large | #1 | 142 | XE2420 | #2 | 131 | DSS 8440 |
5. Dell is the only vendor to submit results on virtualized infrastructure with vCPUs and NVIDIA virtual GPUs (vGPU) on VMware vSphere
Customers interested in deploying inference workloads for AI on virtualized infrastructure can leverage Dell servers with VMware software to reap the benefits of virtualization.
To demonstrate efficient virtualized performance on Intel 2nd Generation Intel Xeon Scalable processors, Dell EMC and VMware submitted results using vSphere and OpenVino on the PowerEdge R640.
- Virtualization overhead for a single VM was observed to be minimal and testing showed that using multiple VMs could be deployed on a single server to achieve ~26% better throughput compared to a bare metal environment.
Dell EMC has published guidance on virtualizing GPUs using DirectPath I/O, NVIDIA Virtual Compute Server (vCS) and more. Dell EMC and VMware used NVIDIA vCS virtualization software in vSphere for MLPerf Inference benchmarks on virtualized NVIDIA T4 GPUs
- VMware vSphere using NVIDIA vCS delivers near bare metal performance for MLPerf Inference v0.7 benchmarks. The inference throughput (queries processed per second) increases linearly as the number of vGPUs attached to the VM increases.
Blogs covering these virtualized tests in greater detail are published at VMware’s performance Blog site.
This finishes our coverage of the top 5 highlights out of the 200+ submissions done by Dell EMC in the datacenter division. Next we discuss other aspects of the GPU optimized portfolio that are important for customers – quality and support.
Dell has the highest number of NVIDIA GPU submissions using NVIDIA NGC Ready systems
Dell GPU enabled platforms are part of NVIDIA NGC-Ready and NGC-Ready for Edge validation programs. At Dell, we understand that performance is critical, but customers are not willing to compromise quality and reliability to achieve maximum performance. Customers can confidently deploy inference and other software applications from the NVIDIA NGC catalog knowing that the Dell systems meet all the requirements set by NVIDIA to deploy customer workloads on-premises or at the Edge.
NVIDIA NGC validated configs that were used for this round of MLPerf submissions are:
- Dell EMC PowerEdge XE2420 (4x T4)
- Dell EMC DSS 8440 (10x Quadro RTX 8000)
- Dell EMC DSS 8440 (12x T4)
- Dell EMC DSS 8440 (16x T4)
- Dell EMC DSS 8440 (8x Quadro RTX 8000)
- Dell EMC PowerEdge R740 (4x T4)
- Dell EMC PowerEdge R7515 (4x T4)
- Dell EMC PowerEdge R7525 (2x A100-PCIE)
- Dell EMC PowerEdge R7525 (3x Quadro RTX 8000)
Dell EMC portfolio can address customers inference needs from on-premises to the edge
In this blog, we highlighted the results submitted by Dell EMC to demonstrate how our various servers performed in the datacenter category. The Dell EMC server portfolio provides many options for customer wanting to deploy AI inference in their datacenters or on the edge. We also offer a wide range of accelerator options including both multiple GPU and FPGA models for running inference either on bare metal or virtualized infrastructure that can meet specific application and deployment requirements.
Finally, we list the performance for a subset of the server platforms that we see mostly commonly used by customers today for running inference workloads. These rankings highlight that the platform can support a wide range of inference use cases that are showcased in the MLPerf suite.
1. The Dell EMC PowerEdge XE2420 with 4x NVIDIA T4 GPUs: Ranked between #1 and #3 in 14 out of 16 benchmark categories when compared with other T4 Servers
Dell EMC PowerEdge XE2420 (4x T4) Per Accelerator Ranking* | |||
| Offline | Server |
|
3d-unet-99 | #1 | n/a
| |
3d-unet-99.9 | #1 | ||
bert-99 | #3 | #1 | |
bert-99.9 | #2 | #2 | |
dlrm-99 | #1 | #3 | |
dlrm-99.9 | #1 | #3 | |
resnet | #1 |
| |
rnnt | #1 | #1 | |
ssd-large | #1 |
| |
2. Dell EMC PowerEdge R7525 with 8x T4 GPUs: Ranked between #1 and #5 in 11 out of 16 benchmark categories in T4 server submission
Dell EMC PowerEdge R7525 (8x T4) Per Accelerator Ranking* | |||
| Offline | Server |
|
3d-unet-99 | #4 | n/a
| |
3d-unet-99.9 | #4 | ||
bert-99 | #4 |
| |
dlrm-99 | #2 | #1 | |
dlrm-99.9 | #2 | #1 | |
rnnt | #2 | #5 | |
ssd-large | #5 |
| |
3. The Dell EMC PowerEdge R7525 with up to 3xA100-PCIe: ranked between #3 and #10 in 15 out of 16 benchmark categories across all datacenter submissions
Dell EMC PowerEdge R7525 (2|3x A100-PCIe) Per Accelerator | |||
| Offline | Server |
|
3d-unet-99 | #4 | n/a
| |
3d-unet-99.9 | #4 | ||
bert-99 | #8 | #9 | |
bert-99.9 | #7 | #8 | |
dlrm-99 | #6 | #4 | |
dlrm-99.9 | #6 | #4 | |
resnet | #10 | #3 | |
rnnt | #6 | #7 | |
ssd-large | #10 |
| |
4. The Dell EMC DSS 8440 with 16x T4 ranked between #3 and #7 when compared against all submissions using T4
Dell EMC DSS 8440 (16x T4) | |||
| Offline | Server |
|
3d-unet-99 | #4 | n/a
| |
3d-unet-99.9 | #4 | ||
bert-99 | #6 | #4 | |
bert-99.9 | #7 | #5 | |
dlrm-99 | #3 | #3 | |
dlrm-99.9 | #3 | #3 | |
resnet | #6 | #4 | |
rnnt | #5 | #5 | |
ssd-large | #7 | #5 | |
5. The Dell EMC DSS 8440 with 10x RTX6000 ranked between #2 and #6 in 14 out of 16 benchmarks when compared against all submissions
Dell EMC DSS 8440 (10x Quadro RTX6000) | |||
| Offline | Server |
|
3d-unet-99 | #4 | n/a
| |
3d-unet-99.9 | #4 | ||
bert-99 | #4 | #5 | |
bert-99.9 | #4 | #5 | |
dlrm-99 |
|
| |
dlrm-99.9 |
|
| |
resnet | #5 | #6 | |
rnnt | #2 | #5 | |
ssd-large | #5 | #6 | |
6. Dell EMC DSS 8440 with 10x RTX8000 ranked between #2 and #6 when compared against all submissions
Dell EMC DSS 8440 (10x Quadro RTX8000) | |||
| Offline | Server |
|
3d-unet-99 | #5 | n/a
| |
3d-unet-99.9 | #5 | ||
bert-99 | #5 | #4 | |
bert-99.9 | #5 | #4 | |
dlrm-99 | #3 | #3 | |
dlrm-99.9 | #3 | #3 | |
resnet | #6 | #5 | |
rnnt | #3 | #6 | |
ssd-large | #6 | #5 | |
Get more information on MLPerf results at www.mlperf.org and earn more about PowerEdge servers that are optimized for AI / ML / DL at www.DellTechnologies.com/Servers
Acknowledgements: These impressive results were made possible by the work of the following Dell EMC and partner team members - Shubham Billus, Trevor Cockrell, Bagus Hanindhito (Univ. of Texas, Austin), Uday Kurkure (VMWare), Guy Laporte, Anton Lokhmotov (Dividiti), Bhavesh Patel, Vilmara Sanchez, Rakshith Vasudev, Lan Vu (VMware) and Nicholas Wakou. We would also like to thank our partners – NVIDIA, Intel and Xilinx for their help and support in MLPerf v0.7 Inference submissions.
Related Blog Posts
Dell EMC Servers Offer Excellent Deep Learning Performance with the MLPerf™ Training v1.1 Benchmark
Wed, 01 Dec 2021 19:20:14 -0000
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Overview
Dell Technologies has submitted results to the MLPerf Training benchmarking suite for the fifth round. This blog provides an overview of our submissions for the latest version, v1.1. Submission results indicate that different Dell EMC servers (Dell EMC DSS8440, PowerEdge R750xa, and PowerEdge XE8545 servers) offer promising performance for deep learning workloads. These workloads are across different problem types such as image classification, medical image segmentation, lightweight object detection, heavyweight object detection, speech recognition, natural language processing, recommendation, and reinforcement learning.
The previous blog about MLPerf v1.0 contains an introduction to MLCommons™ and the benchmarks in the MLPerf training benchmarking suite. We recommend that you read this blog for an overview of the benchmarks. All the benchmarks and rules remain the same as for v1.0.
The following graph with an exponentially scaled y axis indicates time to converge for the servers and benchmarks in question:
Fig 1: All Dell Technologies submission results for MLPerf Training v1.1
Figure 1 shows that this round of Dell Technologies submissions includes many results. We provided 51 results. These results encompass different Dell Technologies servers including Dell EMC DSS8440, PowerEdge R750xa, and PowerEdge XE8545 servers with various NVIDIA A100 accelerator configurations with different form factors: PCIe, SXM4, and different VRAM variants including 40 GB and 80 GB versions. These variants also include 300 W, 400 W, and 500 W TDP variants.
Note: For the hardware and software specifications of the systems in the graph, see https://github.com/mlcommons/training_results_v1.1/tree/master/Dell/systems.
Different benchmarks were submitted that span areas of image classification, medical image segmentation, lightweight object detection, heavy weight object detection, speech recognition, natural language processing, recommendation, and reinforcement learning. In all these areas, the Dell EMC DSS8440, PowerEdge R750xa, and PowerEdge XE8545 server performance is outstanding.
Highlights
Full coverage
Dell Technologies not only submitted the most results but also comprehensive results from a single system. PowerEdge XE8545 x 4 A100-SXM-80GB server results include submissions across the full spectrum of benchmarked models in the MLPerf training v1.1 suite such as BERT, DLRM, MaskR-CNN, Minigo, ResNet, SSD, RNNT, and 3D U-Net.
Multinode results
The performance scaling of the multinode results is nearly linear or linear and results scale well. This scaling makes the performance of Dell EMC servers in a multinode environment more conducive to faster time to value. Furthermore, among other submitters that include NVIDIA accelerator-based submissions, we are one of three submitters that encompass multinode results.
Improvements from v1.0 to v1.1
Updates for the Dell Technologies v1.1 submission include:
- The v1.1 submission includes results from the PowerEdge R750xa server. The PowerEdge R750xa server offers compelling performance, well suited for artificial intelligence, machine learning, and deep learning training and inferencing workloads.
- Our results include numbers for 10 GPUs with 80 GB A100 variants on the Dell EMC DSS8440 server. The results for 10 GPUs are useful because more GPUs in a server help to train the model faster, if constrained in a single node environment for training.
Fig 2: Performance comparison of BERT between v1.0 and v1.1 across Dell EMC DSS8440 and PowerEdge XE8545 servers
We noticed the performance improvement of v1.1 over v1.0 with the BERT model, especially with the PowerEdge XE8545 server. While many deep learning workloads were similar in performance between v1.0 and v1.1, the many results that we submitted help customers understand the performance difference across versions.
Conclusion
- Our number of submissions was significant (51 submissions). They help customers observe performance with different Dell EMC servers across various configurations. A higher number of results helps customers understand server performance that enables a faster time to solution across different configuration types, benchmarks, and multinode settings.
- Among other submissions that include NVIDIA accelerator-based submissions, we are one of three submitters that encompass multinode results. It is imperative to understand scaling performance across multiple servers as deep learning compute needs continue to increase with different kinds of deep learning models and parallelism techniques.
- PowerEdge XE8545 x 4A100-SXM-80GB server results include all the models in the MLPerf v1.1 benchmark.
- PowerEdge R750xa server results were published for this round; they offer excellent performance.
Next steps
In future blogs, we plan to compare the performance of NVLINK Bridged systems with non-NVLINK Bridged systems.
Dell EMC PowerEdge R750xa Server for Inference on AI Applications
Wed, 01 Dec 2021 13:05:19 -0000
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Introduction to the PowerEdge R750xa server for inference on AI applications
Dell Technologies HPC & AI Innovation Lab has submitted results for the Dell EMC PowerEdge R750xa server to MLPerf™ Inference v1.1, the latest round from MLCommons™, for data center on-premises benchmarks. Based on these results submitted for data center on-premises inference, the PowerEdge R750xa server performs well across many application areas and consistently provides high-performance results for machine-learning inference benchmarks. In this blog, we showcase the PowerEdge R750xa server results as a benchmark for high performance.
The results show that the PowerEdge R750xa server is flexible and can support challenges across many AI applications. Also, the results are reproducible for inference performance in problem areas that are addressed by vision, speech, language, and commerce applications.
PowerEdge R750xa server technical specifications
The PowerEdge R750xa server base configuration provides enterprise solutions for customers in the field of artificial intelligence. It is a 2U, dual-socket server with dual 3rd Gen Intel Xeon Scalable processors with 40 cores and 32 DDR4 RDIMM slots for up to 1 TB of memory at powerful data rates.
With state-of-the-art hardware, the PowerEdge R750xa server is well suited for heavy workloads. It is especially well suited for artificial intelligence, machine learning, and deep learning applications and their heavy computational requirements. Also, the PowerEdge R750xa server is designed for flexibility, capable of adding more processors and PCIe cards, and adequate HDD or SSD/NVMe storage capacity to scale to meet workload needs. With scalability at its foundation, the server can be expanded to manage visualization, streaming, and other types of workloads to address AI processing requirements.
The following figures show the PowerEdge R750xa server:
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 without the cover
Overview of MLPerf Inference
The MLPerf Inference benchmark is an industry-standard benchmark suite that accepts submission of inference results for a system under test (SUT) to various divisions. Each division is governed by policies that define the conditions for generating the results and the acceptable configurations for the SUTs. This blog provides details about the divisions and policies governing MLPerf Inference Benchmarking. For more information, see the MLCommons Inference Benchmarking website.
The focus of the PowerEdge R750xa server for inference was on the Closed Division Datacenter suite. There are six tasks spanning four areas for which benchmarking results were submitted. Within each task, the closed division defines a set of constraints that the inference benchmark must follow.
Systems submitting inference benchmark results in each of these tasks are required to meet each of the constraints shown in the following table:
Table 1: Benchmark tasks for MLPerf v1.1 Inference
Area | Task | Model | QSL size | Quality | Server latency constraint |
Vision | Image classification | Resnet50-v1.5 | 1024 | 99% of FP32 (76.46%) | 15 ms |
Vision | Object detection (large) | SSD-ResNet34 | 64 | 99% of FP32 (0.20 mAP) | 100 ms |
Vision | Medical image segmentation | 3D UNet | 16 | 99% of FP32 and 99.9% of FP32 (0.85300 mean DICE score) | N/A |
Speech | Speech-to-text | RNNT | 2513 | 99% of FP32 (1 - WER, where WER=7.452253714852645%) | 1000 ms |
Language | Language processing | BERT | 10833 | 99% of FP32 and 99.9% of FP32 (f1_score=90.874%) | 130 ms |
Commerce | Recommendation | DLRM | 204800 | 99% of FP32 and 99.9% of FP32 (AUC=80.25%) | 30 ms |
PowerEdge R750xa server performance for inference
We submitted PowerEdge R750xa server benchmarking results for each task listed in the preceding table. For each task, we provided two submissions. The first submission was for the system operating in the Offline scenario, in which the SUT receives all samples in a single query and processes them continuously until completed. In this mode, system latency is not a primary issue. The second submission addressed the system operating in a server scenario, in which the model and data are processed through a network connection and depend on the latency of the SUT.
The PowerEdge R750xa server generated results for each task, in both modes, across three different configurations. The following table lists the three configurations:
Table 2: PowerEdge R750xa server configurations submitted for benchmarking
Configuration | 1 | 2 | 3 |
System | PowerEdge R750xa server | ||
Accelerator | 4x A100-PCIe (80 GB) | ||
CPU | Intel Xeon Gold 6338 | ||
Software stack | CUDA 11.3 cuDNN 8.2.1 Driver 470.42.01 DALI 0.31.0 | CUDA 11.3 cuDNN 8.2.1 Driver 470.42.01 DALI 0.31.0 Triton 21.07 | CUDA 11.3 cuDNN 8.2.1 Driver 470.42.01 DALI 0.31.0 Triton 21.07 Multi-Instance GPU (MIG) |
The table shows that all three configurations are similar, using a 4 x A100-PCIe (80 GB) GPU and the Intel Xeon Gold 6338 CPU. The primary difference is in the software stack. All three configurations use TensorRT. Configuration 2 adds a layer by using the NVIDIA Triton Inference Server as the inference engine. Configuration 3 adds two layers by using the NVIDIA Triton Inference Server and the NVIDIA Multi-Instance GPU (MIG).
The following figures show the results of each of these systems for the Offline and Server scenarios.
The following figure shows the first inference benchmark for image classification inferences with the PowerEdge R750xa server:
Figure. 1: PowerEdge R750xa server performance on inference for image classification using ResNet
Three different configurations of the PowerEdge R750xa server were benchmarked. Each configuration used ResNet-50 as the base model and we observed performance in both the Offline and Server scenarios. The first configuration with the Triton Inference Server performed slightly faster in the Offline scenario with 147,327 samples per second compared to the other two configurations. The configuration without the Triton Inference Server ran 146,878 samples per second while the configuration with the Triton Inference Server and MIG ran 136,656 samples per second. In the Offline scenario, these results suggest that the Triton Inference Server performs slightly faster, handling batches of samples quicker without regard to latency. These results give the first configuration a performance edge in the Offline scenario. In the Server scenario, the configuration without the Triton Inference Server performed the fastest at 135,025 samples per second. The configuration with the Triton Inference Server ran 126,018 samples per second, while the configuration with the Triton Inference Server and MIG ran 40,003 samples per second. These results show that the configurations including MIG all performed comparably, ranking highly for 4 x A100-PCIe (80 GB) GPU configurations on the image classification task. The results demonstrate that the PowerEdge R750xa server is a high-performance computing platform for image classification, especially when high-performance acceleration is installed on the system.
The following figure shows the second inference benchmark for Natural Language Processing (NLP) inferences with the PowerEdge R750xa server:
Figure. 2: PowerEdge R750xa server performance on inference for language processing using BERT
The same three configurations of the PowerEdge R750xa server were benchmarked. Each configuration used two versions of BERT as the base model and we observed performance in both the Offline and Server scenarios. The first version of the BERT model (BERT-99) is based on a 99 percent accuracy of inference; the second version (BERT-99.9) is based on a 99.9 percent accuracy of inference. In both cases, the PowerEdge R750xa server ran approximately 50 percent more samples per second with the BERT-99 model compared to the BERT-99.9 model. This result is because using the Bert-99.9 model to achieve 99.9 percent accuracy is based on 16-bit floating point data whereas the BERT-99 model is based on 8-bit integer data. The former requires significantly more computations due to the larger number of bits per sample.
As with the first inference benchmark, the configuration with the Triton Inference Server performed slightly faster in the Offline scenario with 12,859 samples per second compared to the other configurations using the BERT-99 model. It follows that the Triton Inference Server is configured to perform slightly better in the Offline scenario. In the Server scenario, the configuration without the Triton Inference Server performed best at 11,701 samples per second. For the BERT-99.9 model, the configuration without the Triton Inference Server ran 6,397 samples per second in the Offline scenario. Both configurations without MIG performed identically at 5,683 samples per second in the Server scenario for the BERT-99.9 model. This marginal difference can be attributed to run-to-run variability. Therefore, both configurations performed nearly identically.
These results show that all configurations performed comparably with or without the Triton Inference Server. For NLP, all configurations ranked highly for 4 x PCIe GPU configurations. The results suggest that the PowerEdge R750xa server is well suited for handling natural language processing samples in an inference configuration.
The following figure shows the third inference benchmark for light-weight object detection in images with the PowerEdge R750xa server:
Figure. 3: PowerEdge R750xa server performance on inference for light-weight object detection.
The same three configurations of the PowerEdge R750xa server were benchmarked. Each configuration used SDD-Large as the base model and we observed performance in both the Offline and Server scenarios. The configuration that relied on the Triton Inference Server performed slightly faster in the Offline scenario with 3,638 samples per second. In the Server scenario, the configuration without the Triton Inference Server performed best at 3,252 samples per second, approximately 14 percent faster than the other configurations. Once again, each configuration performed comparably with or without the Triton Inference Server or MIG. For object detection, all configurations ranked highly for 4 x PCIe GPU configurations.
In addition to image classification, NLP, and object detection, the PowerEdge R750xa server was benchmarked for medical image classification, speech-to-text processing, and recommendation systems. The following table shows the best performance of the PowerEdge R750xa server, which relies on 4 x GPU A100 acceleration without the Triton Inference Server or MIG, and its respective performance in both the Offline and Server scenarios for each of the major tasks identified in Table 1.
Table 3: Performance of the PowerEdge R750xa server 4x A100-PCIe (80 GB) on TensorRT
Area | Task | Model | Mode | Samples per second |
Vision | Image classification | Resnet50-v1.5 | Offline | 146,878 |
Vision | Image classification | Resnet50-v1.5 | Server | 135,025 |
Vision | Object detection (large) | SSD-ResNet34 | Offline | 3,634 |
Vision | Object detection (large) | SSD-ResNet34 | Server | 3,252 |
Vision | Medical image segmentation | 3D UNet 99 | Offline | 231 |
Vision | Medical image segmentation | 3D UNet 99.9 | Offline | 231 |
Speech | Speech-to-text | RNNT | Offline | 53,113 |
Speech | Speech-to-text | RNNT | Server | 48,504 |
Language | Language processing | BERT-99 | Offline | 12,773 |
Language | Language processing | BERT-99 | Server | 11,701 |
Language | Language processing | BERT-99.9 | Offline | 6,397 |
Language | Language processing | BERT-99.9 | Server | 5,683 |
Commerce | Recommendation | DLRM-99 | Offline | 1,136,410 |
Commerce | Recommendation | DLRM-99 | Server | 1,136,670 |
Commerce | Recommendation | DLRM-99.9 | Offline | 1,136,410 |
Commerce | Recommendation | DLRM-99.9 | Server | 1,136,670 |
The results show that the system performed well in all tasks, ranking highly for each. These results show that the PowerEdge R750xa server is a solid system with flexibility for handling most AI problems that you might encounter.
Conclusion
In this blog, we quantified the performance of the PowerEdge R750xa server in the MLCommons Inference v1.1 performance benchmarks. Customers can use the submitted results to evaluate the applicability and flexibility of the PowerEdge R750xa server to address their needs and challenges.
The results in this blog show that the PowerEdge R750xa server is a flexible choice for AI inference problems. It has the flexibility to meet the inference requirements across many different scenarios and workload types.