Dell PowerEdge HS5610 Performance
Download PDFThu, 29 Jun 2023 21:55:49 -0000
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Summary
Dell delivers technology optimization without the financial and operational burden of supporting extreme configurations. Dell PowerEdge cloud-scale servers are designed and optimized to give you the ability to scale with server configurations built for CSPs. The servers scale up to two sockets, 32 cores each, 1 TB of memory, and various SAS, SATA, and NVMe storage options. Cloud-scale servers also offer Dell Open Server Manager built on open-source OpenBMC systems management software.
Test configuration
Server | PowerEdge R650xs | PowerEdge HS5610 |
CPU | 2 x Intel® Xeon® Gold 5318Y | 2 x Intel® Xeon® Gold 5418Y |
Memory | 16 x 32 GB at 2933 MT/s | 16 x 32 GB at 4400 MT/s |
Storage | 4 x 960 GB SATA drives | |
RAID controller | H755 Front RAID 5 | |
Operating system | Ubuntu 22.04 TLS |
Database benchmark—Redis
Redis is an open-source (BSD licensed), in-memory data structure store used as a database, cache, message broker, and streaming engine. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, HyperLogLogs, geospatial indexes, and streams. Redis has integrated replication, Lua scripting, LRU eviction, transactions, and different levels of on-disk persistence, and provides high availability through Redis Sentinel and automatic partitioning with Redis Cluster.
To achieve top performance, Redis works with an in-memory dataset. Depending on your use case, Redis can persist your data either by periodically dumping the dataset to disk or by appending each command to a disk-based log. You can also disable persistence if you just need a feature-rich, networked, in-memory cache.
Redis supports asynchronous replication, with fast nonblocking synchronization and auto-reconnection with partial resynchronization on net split.
Results:
- The data provided highlights the performance of each system running a typical SET command to modify data in the schema in memory. This test leverages AVX512 extensions on both 15G and 16G systems. Relative performance uplift on our 16G configuration was strongly influenced by the increased memory bandwidth provided by DDR5.
- Dell PowerEdge HS5610 database performance improved by 35 percent compared to the previous generation. Veeva claim ID: CLM-007679
- The Dell PowerEdge HS5610 offers a 33 percent increase in price performance per CPU dollar when compared to the previous generation. Veeva claim ID: CLM‑007681
- Dell PowerEdge HS5610 performance has increased by 28 percent per watt compared to the previous generation with Redis database benchmark. Veeva claim ID: CLM-007680
CPU benchmark—V-Ray 5
V-Ray Benchmark is a free, stand-alone application that can be used to test how fast your system renders. It’s simple and fast, and includes three render engine tests:
- V-Ray—CPU compatible
- V-Ray GPU CUDA—GPU and CPU compatible
- V-Ray GPU RTX—RTX GPU compatible
Three custom-built test scenarios are also included to put each V-Ray 5 render engine through its paces.
Discover how your computer ranks alongside others and learn how different hardware combinations can boost your rendering speeds.
Results:
- The data provided highlights the performance of each system running a CPU benchmark with V-Ray using the number of vsamples. Higher is better.
- PowerEdge HS5610 has 15 percent more CPU rendering compared to the previous generation. Veeva claim ID: CLM-007677
Memory benchmark—STREAM
The STREAM benchmark is a simple synthetic benchmark program that measures sustainable memory bandwidth (in MB/s) and the corresponding computation rate for simple vector kernels.
Computer CPUs are getting faster much more quickly than computer memory systems. As these gains progress, an increasing number of programs will be limited in performance by the memory bandwidth of the system, rather than by the computational performance of the CPU.
As an extreme example, several current high-end machines run simple arithmetic kernels for out-of-cache operands at 4 to 5 percent of their rated peak speeds. That means that they are spending 95 to 96 percent of their time idle and waiting for cache misses to be satisfied.
The STREAM benchmark is specifically designed to work with datasets much larger than the available cache on any given system, so that the results are (presumably) more indicative of the performance of very large, vector-style applications.
Results:
- The Copy benchmark measures the transfer rate in the absence of arithmetic. This should be one of the fastest memory operations, but it also represents a common one—fetching two values from memory, a(i) and b(i), and updating one operation.
- PowerEdge HS5610 has 25 percent more memory bandwidth than the similarly configured previous-generation system. Veeva claim ID: CLM-007678
Conclusion
Our engineers select the appropriate benchmarks in coordination with your team. Then, using the benchmarks, we perform iterative testing in a Dell Technologies performance lab to analyze the effects of specific server settings and hardware configurations on a benchmark. This data-driven approach with engineers specializing in PowerEdge system performance allows Dell to identify the optimal system configuration for a given workload and provide guidance that delivers rapid time to value for our cloud customers.
Legal disclosures
- Testing conducted by Dell Server TME Lab March of 2023. Server performance benchmarks were performed on similarly configured Dell PowerEdge HS5610 vs Dell PowerEdge R650xs. See documentation for test and configuration specifics. Actual results will vary by use.
- Testing conducted by Dell Server TME Lab March of 2023. Server performance benchmarks were performed on similarly configured Dell PowerEdge HS5610 vs Dell PowerEdge R650xs. See documentation for test and configuration specifics. The CPU price was based on Intel.com site per March 29, 2023, for Gold 5318Y and 5418Y. Actual results will vary by use.
Related Documents
Spark Machine Learning on Dell HS5610 Platform with Cloudera
Mon, 29 Jan 2024 22:49:51 -0000
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Executive Summary
To establish a thorough solution collateral for the Dell PowerEdge HS5610 platform integrated with Cloudera software, we are commencing benchmarking initiatives this year. These benchmarks will form the foundational baseline for our future testing endeavors, and it's essential to emphasize that we will not be making comparisons with previous generations.
This initiative holds great significance, prompted directly by Dell's explicit request to craft this reference solution. Intel has taken charge of executing the benchmark tests and generously shared their Best-Known Methods (BKMs), providing invaluable guidance for this critical undertaking.
What are the key takeaways?
Cloudera Data Platform built on Dell’s 16G PowerEdge servers with Intel® 4th Generation Xeon processor architecture can accommodate growing enterprise data workloads and efficiently manage increasing demands for analytics and machine learning in a smaller footprint. Cloudera Data Platform delivers easier data management and scalability for data anywhere with optimal performance, scalability, and security.
As organizations create more diverse and more user-focused data products and services, there is a growing need for machine learning, which can be used to develop personalization, recommendations, and predictive insights. But as organizations amass greater volumes and greater varieties of data, data scientists are spending most of their time supporting their infrastructure instead of building the models to solve their data problems. To help solve this problem, as an integrated part of Cloudera’s platform, Spark provides a general machine learning library that is designed for simplicity, scalability, and easy integration with other tools. With the scalability, language compatibility, simple administration and compliance-ready security and governance provided through cloudera, data scientists can solve and iterate through their data problems faster.
Spark MLlib
Spark MLlib is a distributed machine learning framework built on top of Spark Core. The key benefit of MLlib is that it allows data scientists to focus on their data problems and models instead of solving the complexities surrounding distributed data. MLlib leverages the advantages of in-memory computation and is optimized for matrix and vector operations, aligning its capabilities with specific algorithmic requirements for the given use case.
K-means Overview
Clustering stands as a fundamental exploratory data analysis technique, providing valuable insights into the inherent structure of data. One prominent algorithm for this purpose is K-means, widely recognized for partitioning data points into a predefined number of clusters. This technique finds extensive applications in diverse domains including market segmentation, document clustering, image segmentation, search engines, real estate, anomaly detection and image compression, highlighting it’s versatility and importance in data analysis.
- K-Means Overview
K-means clustering performance
We achieved remarkable results by clustering 10 million samples with 1,000 dimensions in just 283 seconds. This accomplishment was made possible through the application of the K-Means algorithm from Spark's ML library, which was provided by Cloudera 7.1.8 and deployed on Dell PowerEdge HS5610 platform.
We conducted a performance evaluation of Spark's MLlib K-Means algorithm using the HiBench Benchmark.
For detailed information on our benchmarking process, you can refer to Intel GitHub repository: https://github.com/Intel-bigdata/HiBench
Note - This result is not compared against any other platform hardware or software. We will use this as baseline for future products.
Configuration Details
Workload Configuration | |
Platform | Dell PowerEdge HS5610 |
CPU | 6448Y |
Memory | 512 GB (16 x 32GB DDR5-4800) |
Boot Device | Dell EMC™ Boot Optimized Server Storage (BOSS-N1) with 2 x 480 GB M.2 NVMe SSDs (RAID1) |
HDFS Data Disk | 2 x Dell Ent NVMe P5500 RI U.2 3.84TB |
HDFS Namenode Disk | 1 x Dell Ent NVMe P5500 RI U.2 3.84TB |
Yarn Cache Disk | 1 x Dell Ent NVMe P5500 RI U.2 3.84TB |
Network Interface Controller | NetXtreme BCM5720 Gigabit Ethernet PCIe |
Cluster size | 1 |
Cloudera Distribution | Cloudera Data Platform 7.1.8 |
Compute Engine | Spark 3.2.0 |
Workload | Hibench 7.1.1 – Kmeans Algorithm |
Iterations and result choice | 3 iterations, average |
Spark Configuration | |
spark.deploy.mode | yarn |
Executor Numbers | 16 |
Executor cores | 8 |
spark.executor.memory | 24g |
spark.executor.memoryOverhead | 4g |
spark.driver.memory | 20g |
spark default parallelism | 128 |
spark.driver.maxResultSize | 20g |
spark.serializer | org.apache.spark.serializer.KryoSerializer |
spark.kryoserializer.buffer.max | 1g |
spark.network.timeout | 1200s |
K-means Configuration | |
Number of clusters | 5 |
Dimensions | 1,000 |
Number of Samples | 10,000,000 |
Samples per inputfile | 10,000 |
Number of Iterations | 40 |
k | 300 |
Extract Insights on a Scalable and Security-Enabled Data Platform from Cloudera
Mon, 29 Jan 2024 22:48:44 -0000
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Summary
This joint paper outlines the key hardware considerations when configuring a data platform based on the most recent Dell’s 16th Generation PowerEdge Server portfolio offerings.
Market positioning
Cloudera® Data Platform (CDP) Private Cloud is a scalable data platform that allows data to be managed across its life cycle—from ingestion to analysis—without leaving the data center. It consists of two products: Cloudera Private Cloud Base (the on-premises portion built on Dell PowerEdge™ servers[RAK1] [DD2] [DD3] ) and Cloudera Private Cloud Data Services. The Data Services provide containerized compute analytic applications that scale dynamically and can be upgraded independently. This platform simplifies managing the growing volume and variety of data in your enterprise, unleashing the business value of that data. CDP Private Cloud helps enhance business agility and flexibility by disaggregating compute and storage and supporting a container-based environment. The platform also includes secure user access and data governance features.
Key Considerations
- Scalability and Performance: The CDP Platform is built on Dell’s 16th Generation PowerEdge servers with Intel® 4th Generation Xeon processor architecture. It can accommodate growing enterprise data workloads and efficiently handle increasing demands for analytics and machine learning in a smaller footprint.
- Compatibility and Integration: Ensuring compatibility and seamless integration between CDP Private Cloud and the hardware components is essential for a successful deployment in a Cloud environment. Delivering faster time-to-market and minimizing the total cost of ownership are ensured with Intel architecture-based Dell PowerEdge servers that are well suited to work with the CDP Platform running on a private cloud
- Availability and Resilience: The reliability and resilience features of the 16th Generation PowerEdge servers, (such as redundant power supplies, hardware monitoring, and failover capabilities, so on), are critical for maintaining[RAK4] [RAK5] the reliability and availability of the CDP Platform.
Available Configurations
The new Dell PowerEdge HS5610 is a 1U, two-socket rack server purpose-built for Cloud Service Providers’ most popular IT applications, this also lends itself well for Hybrid Cloud Edge deployments. Vi This scalable server optimizes technology without the financial and operational burden of supporting extreme configurations. With tailored performance, I/O flexibility, and open ecosystem system management, you gain simplicity for large-scale, heterogeneous SaaS, PaaS, and IaaS data centers.
Some of the benefits include –
- Faster performance by using 4th generation Intel® Xeon® Scalable processors with up to 32 cores per socket
- Accelerated in-memory applications with up to 16 DDR5 RDIMMS with speeds up to 4800 MT/sec
- Designed to take up less space than traditional servers, which makes them a good option for data centers with limited space and for cloud service providers
- Designed to be cooled efficiently, which can help to prevent overheating and ensure the longevity of the servers at cloud and on-premises
- Power efficient, which can help to reduce the overall operating costs of a data center
- Configurations that can easily scale to meet changing demand, which can help to optimize the cost of a data center
- Long living instances for space and cost reductions
- Validated workloads that reduce data center costs and overhead
- Resilient Architecture for Zero Trust IT environment and operations
| Cloudera® Data Platform (CDP) Private Cloud Base Cluster |
| |||
| Edge Node (1 Node) + Master Nodes (Minimum of Three Nodes Required)
| Worker Nodes for Use with External Storage System (Minimum of Three Nodes Required) | Worker Nodes with Local All-Flash Storage (Minimum of Three Nodes Required) | Worker Nodes with Local HDDs (Minimum of Three Nodes Required) |
|
Functions | Edge node: Apache Hadoop® clients, NameNode, Resource Manager, Apache ZooKeeper | DataNode, NodeManager, CDP DC (YARN) workloads |
| ||
Platform | Dell PowerEdge HS5610 (1RU) Chassis with up to 10x 2.5" SAS/SATA/NVMe Direct Drives | Dell PowerEdge HS5610 (1RU) Chassis with up to 10x 2.5" SAS/SATA/NVMe Direct Drives | Dell PowerEdge HS5620 (2RU) Chassis with up to 16x 2.5" SAS/SATA and 8x 2.5” NVMe | Dell PowerEdge HS5620 (2RU) Chassis with up to 12x 3.5" Drives and 2 x 2.5” rear storage (NVMe) |
|
CPU | 2 x 4th Gen Intel® Xeon® Gold 6426Y processor | 2 x 4th Gen Intel® Xeon® Gold 6448Y processor
|
| ||
DRAM | 256 GB (16 x 16 GB DDR5-4800) | 512 GB (16 x 32 GB DDR5-4800) |
| ||
Boot Device | Dell EMC™ Boot Optimized Server Storage (BOSS-N1) with 2 x 480 GB M.2 NVMe SSDs (RAID 1) |
| |||
Storage Adapter | Dell PERC H755N NVMe RAID adapter | None | Dell HBA355i |
| |
Storage HDFS/Ozone | 2x (up to 4x) 3.84 TB SATA Read Intensive SSD 2.5in AG Drive, 1DWPD | Not Required. Use an external storage system instead | 8x (up to 16x) 3.84 TB SATA Read Intensive SSD 2.5in AG Drive, 1DWPD | 12x 4 TB (or larger) 7.2 K RPM NLSAS 12 Gbps 512n 3.5” hot plug HDD |
|
Storage Fast Cache | 1 x 1.6 TB or 3.2 TB Enterprise NVMe Mixed Use AG Drive U.2 Gen4 | 1 x 3.2 TB Enterprise NVMe Mixed Use AG Drive U.2 Gen4 |
| ||
Network Interface Controller | Intel Ethernet Network Adapter E810-XXVDA2 for OCP3 (dual-port 10/25 GbE) |
| |||
Additional NIC | None | Intel Ethernet Network Adapter E810-XXV (dual-port 10/25 GbE), or | None | None |
|
Note: For storage-only configuration (Hadoop Distributed File System/Ozone), customers can still choose traditional high-density storage nodes with high-capacity rotational HDDs based on the HS5610 platform, however, external storage systems like Dell PowerScale or ECS are recommended. Customers should be aware that using large capacity HDDs increases the time of background scans (bit-rot detection) and block report generation for HDFS and significantly increases recovery time after full node failure. Also, using nodes with more than 100 TB of storage is not recommended by Cloudera. Source: https://blog.cloudera.com/disk-and-datanode-size-in-hdfs/. For more information and specifications, contact a Dell representative.
| CDP Private Cloud Data Services (Red Hat® OpenShift® Kubernetes®)/Embedded Container Service (ECS) Cluster | ||||
| Container Services Administration Host | Master Nodes (Three Nodes Required) | Worker Nodes (10 Nodes or More) | ||
Functions | OpenShift administration services
| OpenShift services, Kubernetes services | Kubernetes operators, Cloudera® Data Platform (CDP) Private Cloud workload pods | ||
Platform | Dell PowerEdge HS5610 (1RU) Chassis with up to 10x 2.5" SAS/SATA/NVMe Direct Drives | ||||
CPU | 2 x 4th Gen Intel® Xeon® Gold 6426Y processor | 2 x 4th Gen Intel® Xeon® Gold 6448Y processor
| |||
DRAM | 128 GB (16 x 8 GB DDR5-4800) | Standard configuration: 512 GB (16 x 32 GB DDR5-4800) Large memory configuration: 1024 GB (16 x 64 GB DDR5-4800) | |||
Boot device | Dell EMC™ Boot Optimized Server Storage (BOSS-N1) with 2 x 480 GB M.2 NVMe SSDs (RAID 1) | ||||
Storage adapter | Not required for all-NVMe configuration. | ||||
Storage (NVMe) | 1 x 1.6 TB Enterprise NVMe Mixed Use AG Drive U.2 Gen4 | 1 x 3.2 TB Enterprise NVMe Mixed Use AG Drive U.2 Gen4 | 1 x 6.4 TB Enterprise NVMe Mixed Use AG Drive U.2 Gen4
| ||
NOTHING |
| Intel Ethernet Network Adapter E810-XXVDA2 for OCP3 (dual-port 10/25 GbE) |
| ||
Additional NIC | Intel Ethernet Network Adapter E810-XXV (dual-port 10/25 GbE) | ||||
Learn More
Contact your Dell account team for a customized quote on 1-877-289+-3355 or go to the Intel and Cloudera solutions page.
- For workloads requiring high network bandwidth, customers might use an Intel Ethernet Network Adapter E810-CQDA2 with PCIe (dual-port 100 GbE) and 100 GbE top-of-rack (ToR) switches.
- Additional NIC is recommended for connectivity to an external storage system using a dedicated storage network. we [repeat endnote 2]
[RAK1]Dell to confirm the legal name for this platform
[DD2]“Dell PowerEdge HS5610 cloud scale server” is the correct name.
[RAK4]Can add more based on dells feedback
[RAK5]Added benefit section in available configs