In the ever-evolving landscape of data centers and IT infrastructure, meeting the demands of scale-out workloads is a continuous challenge. Organizations seek solutions that not only provide superior performance but also optimize Total Cost of Ownership (TCO). 

Enter the new Dell PowerEdge C6615, a modular node designed to address these challenges with innovative solutions. Let's delve into the key features and benefits of this groundbreaking addition to the Dell PowerEdge portfolio.

Industry challenges

1. Maximizing Rack utilization: One of the primary challenges in the data center world is maximizing rack utilization. The Dell PowerEdge C6615 addresses this by offering dense compute options.
2. Cutting-edge processors: High-performance processors are crucial for scalability and security. The C6615 is powered by a 4th Generation AMD EPYC 8004 series processor, ensuring top-tier performance.
3. Total Cost of Ownership (TCO): TCO is a critical consideration that encompasses power and cooling efficiency, licensing costs, and seamless integration with existing data center infrastructure. The C6615 is designed to reduce TCO significantly.

Introducing the Dell PowerEdge C6615

The Dell PowerEdge C6615 is a modular node designed to revolutionize data center infrastructure. Here are some key highlights:

• Price-performance ratio: The C6615 offers outstanding price per watt for scale-out workloads, with up to a 315% improvement compared to a one-socket (1S) server with AMD EPYC 9004 Series server processor.
• Optimized thermal solution: It features an optimized thermal solution that allows for air-cooling configurations with up to 53% improved cooling performance compared to the previous generation chassis.
• Density-optimized compute: The C6615's architecture is tailored for scale-out WebTech workloads, offering exceptional performance with reduced TCO.
• High-speed NVMe storage: It provides high-speed NVMe storage for applications with intensive IOPS requirements, ensuring efficient performance.
• Efficient scalability: With 40% more cores per rack compared to the AMD EPYC 9004 Series server processors, the C6615 allows for quicker and more efficient scalability.
• SmartNIC: It includes a SmartNIC with hardware-accelerated networking and storage, saving CPU cycles and enhancing security.

Key features

To maximize efficiency and reduce environmental impact, the PowerEdge C6615 incorporates several key features:

• Power and thermal efficiency: The 2U chassis with four nodes enhances power and thermal efficiency, eliminating the need for liquid cooling.
• Flexible I/O options: It supports up to two PCIe Gen5 slots and one 16 PCIe Gen5 OCP 3.0 slot for network cards, ensuring versatile connectivity.
• Security: Security is integrated at every phase of the PowerEdge lifecycle, from supply chain protection to Multi-Factor Authentication (MFA) and role-based access controls.

Accelerating performance

In benchmark testing, the C6615 outperforms the competition:

• HPL Benchmark: It showcases up to a 335% improvement in performance per watt per dollar and a 210% increase in performance per CPU dollar compared to other 1S systems with the AMD EPYC 9004 Series server processor.  

Figure 1.  HPL benchmark performance

• SPEC_CPU2017 Benchmark: Results demonstrate up to a 205% improvement in performance per watt per dollar and a remarkable 128% increase in performance per CPU dollar compared to similar systems.  

Figure 2.  SPEC_CPU2017 benchmark performance

Final thoughts

The seamless integration of the Dell PowerEdge C6615 into existing processes and toolsets is facilitated by comprehensive iDRAC9 support for all components. This ensures a smooth transition while leveraging the full potential of your server infrastructure.

Dell's commitment to environmental sustainability is evident in its use of recycled materials and energy-efficient options, helping to reduce carbon footprints and operational costs.

In conclusion, the Dell PowerEdge C6615 emerges as a leading dense compute solution, delivering exceptional value and unmatched performance. For more information, visit the PowerEdge Servers Powered by AMD site and explore how this innovative solution can transform your data center operations.

Note: Performance results may vary based on specific configurations and workloads. It's recommended to consult with Dell or an authorized partner for tailored solutions.

Author: David Dam

Overview

On 06-13-2023, Dell Technologies published a TPCx-AI SF1000 result that was based on an 11 x Dell PowerEdge R6625 hardware platform powered by AMD Genoa processors. As of the publication date, Dell results held number one slots on the Top Performance and Price/Performance tables for TPCx-AI on SF3, SF100, SF300, and SF1000. These results reinforce Dell Technologies’ TPCx-AI benchmark leadership position; a statement to the great performance provided by its AI, ML, and DL solutions.

This blog presents the hardware platform that was tested, what was measured and what the results mean.

What TPCx-AI tests measure

TPCx-AI measures the end-to-end machine learning or data science platform using a diverse representative dataset scaling from 1 GB to 10 TB. The TPCx-AI benchmark assesses various aspects of AI training and inference performance, including data generation, model training, serving, scoring, and system scalability. The benchmark can be used across a wide range of different systems from edge to data center. It aims to provide a standardized and objective measure of AI performance across different platforms and configurations.

By using TPCx-AI, organizations and vendors can make informed decisions about the AI infrastructure that best suits their needs. The benchmark helps in understanding the system's capability to handle large-scale AI training workloads and can help optimize performance and resource allocation for AI tasks.

The TPCx-AI standard defines 10 use cases based on data science pipelines modeled on a retail business data center to evaluate the performance of artificial intelligence systems. The workload trains deep neural networks on large datasets using prominent machine learning frameworks such as TensorFlow. The benchmark measures:

1. The total time taken to train a model for each use case to a specific level of accuracy
2. The time taken for that model to be used for inference or serving

The blog, Interpreting the results of the TPCx-AI Benchmark, outlines the ten use cases, their data science models, and the benchmark phases.

 System under test (SUT)

Figure 1 System Under Test (SUT).

Software versions

 Table 1 Software versions

The result

Primary metrics

 Table 2 Primary metric scores

 The three primary metrics in Table 2 are required for all TPC results. The top ten results, based on performance or price/performance at a particular SF category, are displayed in the tables of the respective benchmark standard categorized by the metric and SF. To compare any results, all three metrics must be disclosed in the body of the message. The TPC does not allow comparing TPCx-AI results from different SF categories. The blog, Interpreting the results of the TPCx-AI Benchmark, goes into the details of how the performance and price/performance metrics are calculated. The availability date is the date all the priced line items (SKUs) are available to customers and must be within 185 days of the submission date. For the performance metric, the higher the score the better. For price/performance, the lower the better.

Other metrics

 Table 3 Other metrics

 The metrics in Table 3 are required to be reported and disclosed in the Full Disclosure Report (FDR) and Executive Summary (ES). Except for the total system cost, these other metrics are not used in the calculation of the primary metrics but provide additional information about the system that was tested. For instance, the total system cost is the total cost of ownership (TCO) for one year. The redundancy modes provide the data protection mechanisms that were used in the configuration as required by the benchmark standard. The number of streams refers to the number of concurrent serving tests during the Throughput phase.

Numerical quantities

Benchmark run times

 Table 4 Benchmark run times

Benchmark phase times

 Table 5 Benchmark phase metrics

 The seven benchmark phases and their metrics are explained in Interpreting the results of the TPCx-AI Benchmark, and are performed sequentially from data generation to throughput tests. In power training, models are generated and trained for each use case sequentially from UC1 to UC10. In power serving, the models obtained during the training phase are used to conduct the serving phase sequentially, one use case at a time. There are two power serving tests. The test that registers the longer time provides the TPST metric. The throughput phase runs multiple streams of serving tests concurrently. The more the number of streams, the more the system resources are taxed. Typically, the number of streams are increased until TTT_n+1 > TTT_n (where n+1 refers to the next throughput test). The duration of the longest running stream (T_TPUT) is used to calculate the throughput test metric TTT.

Use case times and accuracy

 Table 6 Use case times and accuracy

Table 6 shows the use case run times (in seconds) for each benchmark phase and the accuracy of the model that was used. For instance, the RNN model that was generated and trained for UC2 had a word_error rate of 0.4383 which was less (better) than the threshold error_rate of 0.5. The XGBoost model trained for UC8 was 74.99% accurate which was above and better than the 65% minimum accuracy threshold requirement.

Figure 2 Use case time by benchmark phase

TPCx-AI SF1000 results tables

Table 7 displays the top TPCx-AI SF1000 tables as of the publication of this blog.

Table 7 SF1000 top performance table

Table 8 Top price/performance table

Table 7 and Table 8 are similar. Of the four published results at SF1000, Dell Technologies’ hardware platforms hold the number 1, number 2, and number 3 positions on both the performance and price/performance tables. The main difference between the three top results is the processor generations:

• The number 1 result used 4^th generation AMD Genoa processors
• The number 2 result used 3^rd generation Intel Ice Lake processors
• The number 3 result used 2^nd generation Intel Cascade Lake processors

Key takeaways

1. Dell dominates TPCx-AI top performance and price/performance tables at SF3, SF100, SF300, and SF1000.
2. TPCx-AI performance improved greatly on newer generation Dell hardware platforms that have newer generation processors:
   1. There was a 60.71% performance improvement between hardware platforms powered by (14G) 2^nd generation and (15G) 3^rd generation processors.
   2. There was a 37.13% improvement between 3^rd generation and (16G) 4^th generation processors.
3. TPCx-AI price/performance improved greatly between processor generations of the Dell 14G, 15G, and 16G hardware platforms:
   1. There was a 14.80% price/performance drop from hardware platforms powered by 2^nd generation to 3^rd generation processors.
   2. There was a 27.08% price/performance drop from 3^rd generation to 4^th generation processors.
4. The form factor of the hardware platforms has reduced:
   1. The Dell 14G TPCx-AI SF1000 result used 2U servers
   2. The 15G and 16G results used 1U servers and scored better performance and price/performance
5. Using NVMe data storage scored better price/performance metrics:
   1. The 14G result used hard drives
   2. The 15G and 16G results used more expensive NVMe data drives, and yet scored better price/performance metrics

Conclusion

This blog examined in detail the TPCx-AI performance result of the Dell 16G PE R6625 hardware platform. The result cemented Dell Technologies’ leadership positions on TPCx-AI performance and price/performance tables at SF1000, in addition to the leadership positions at SF3, SF100, and SF300. These results prove Dell Technologies’ leadership as a provider of high-performance AI, ML, and DL solutions based on verifiable performance data backed by a reputable, industry-standards performance consortium.

References

Nicholas Wakou, Nirmala Sundararajan; Interpreting the results of the TPCx-AI Benchmark; infohub.delltechnologies.com (February 2023).