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govindhtech · 2 months

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AMD EPYC 9754 Benchmark Beats NVIDIA Grace Superchip

AMD EPYC 9754 Benchmark

AMD EPYC 4th Gen Outperforms NVIDIA Grace Superchip in Performance and Efficiency Industry benchmarks indicate that single- and dual-socket 4th Gen AMD EPYC systems outperform 2P NVIDIA Grace CPU Superchip systems by ~2.00x to ~3.70x and have more than double the energy efficiency.

My previous blogs showed that 4th Gen AMD EPYC processors beat 5th Gen Intel Xeon Platinum and Ampere Altra Max M128-30 CPUs for critical applications. They’ll evaluate 4th Gen AMD EPYC and NVIDIA Grace CPU Superchip CPUs’ performance and energy efficiency.

Continuous innovation drives 4th Gen AMD EPYC CPUs to create new standards in datacenter performance, power efficiency, security, and TCO. The 4th Gen AMD EPYC processor range provides cutting-edge on-premises and cloud-based solutions for today’s demanding, diverse workloads.

Over 250 server architectures and 800 cloud instances are supported by AMD EPYC. AMD EPYC processors have over 300 world records in commercial applications, technical computing, data management, data analytics, digital services, media and entertainment, and infrastructure solutions.

The Grace CPU Superchip from NVIDIA was released with eye-opening performance comparisons. These claims must be carefully assessed because their benchmark results are minimal and lack key system setup data. With comprehensive industry-standard benchmark releases, AMD continually shows superior performance and power efficiency.

AMD EPYC processors have 5927 official SPEC CPU 2017 publications, while NVIDIA Grace has none. As you will see, 4th Gen AMD EPYC processors outperform NVIDIA CPUs in energy efficiency and performance.

Please note that this blog covers only a portion of the tested workloads. ARM-based NVIDIA Grace can run a limited number of workloads due to compatibility concerns with x86 processing architecture, which enables enterprise, cloud-native, and HPC applications.

AMD tested several systems with single-socket and dual-socket AMD EPYC 9754 (code name “Bergamo,” with 128 cores and 256 threads/vCPUs), dual-socket AMD EPYC 9654 (code name “Genoa,” with 96 cores and 192 threads/vCPUs), and NVIDIA Grace processors. Unless otherwise stated, each AMD EPYC system had 12 × 64GB DDR5-4800 memory per socket. NVIDIA used the highest server-supported LPDDR5-8532 memory of 480 GB.

Efficiency of Power

Modern data centres must handle rising demand while optimizing electricity use to cut costs and promote sustainability. By assessing the System Under Test’s power and performance, the Standard Performance Evaluation Corporation (SPEC) power ssj 2008 benchmark compares volume server class computers’ energy efficiency.

General-purpose computing

For computer system performance testing, SPEC created the SPEC CPU 2017 benchmark set. A leading industry standard for evaluating general-purpose computing infrastructure, SPECrate 2017_int_base ratings evaluate integer performance.

Single- and dual-socket AMD EPYC 9754 systems outscored NVIDIA Grace systems by approx. 1.33x and 2.64x, respectively. Additionally, a dual-socket AMD EPYC 9654 system outperformed the same NVIDIA system by ~2.43x in the same tests.

Java server-side

4th Gen AMD EPYC CPUs provide cloud native workloads without sacrifice or costly architectural upgrades. Java is used everywhere in enterprise and cloud contexts. The SPE jbb 2015 benchmark models an e-commerce corporation with an IT infrastructure that handles point-of-sale requests, online purchases, and data-mining operations to evaluate Java-based application performance on server-class hardware.

Single- and dual-socket AMD EPYC 9754 computers outscored NVIDIA Grace by ~1.81x and ~3.58x, respectively. Additionally, the dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~3.36x in SPECjbb2015-MultiJVM max-jOPS tests. AMD EPYC computers use SUSE Linux Enterprise Server 15 SP4 v15.14.21 with Java SE 21.0 for 9654 and 17.0 LTS for 9754. NVIDIA Grace system running Ubuntu 22.04.4 (kernel v15.15.0-105-generic) and Java SE 22.0.

Transactional Databases

MySQL is a popular open-source relational database technology in enterprise and cloud environments. AMD assessed online transaction processing using HammerDB TPROC-C. The HammerDB TPROC-C workload, generated from the TPC-C Benchmark Standard, does not match published TPC-C results as it does not meet the standard.

Single- and dual-socket AMD EPYC 9754 computers outscored NVIDIA Grace by ~1.58x and ~2.16x, respectively. Additionally, the dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~2.17x in the same tests.

Ubuntu 22.04, MySQL 8.0.37, and HammerDB 4.4 were installed on the test systems. Multiple 16-core VMs were on each system. Three test runs’ medians were compared.

System Supporting Decisions

Decision Support System deployments use MySQL extensively. AMD evaluated Design Support System performance with HammerDB TPROC-H. HammerDB TPROC-H workload results do not meet with the TPC-H Benchmark Standard, hence they cannot be compared to published TPC-H results.

As shown in single- and dual-socket AMD EPYC 9754 systems outscored NVIDIA Grace systems by ~1.42x and ~2.98x, respectively. Additionally, the dual-socket AMD EPYC 9654 system outperformed the same system by ~2.62x in the same tests.

Ubuntu 22.04, MySQL 8.0.37, and HammerDB 4.4 were installed on the test systems. Multiple 16-core VMs were on each system. Three test runs’ medians were compared.

The Web Server

In Figure 6, single- and dual-socket AMD EPYC 9754 computers outscored NVIDIA Grace by ~1.27x and ~2.56x, respectively. Additionally, the dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~1.89x in the same tests.

The server and client were on the same system in this benchmark test to reduce network delay and estimate CPU processing power. The systems ran Ubuntu 22.04 and NGINX 1.18.0. Multiple 8-core instances ran on each system. Each run assessed the workload for 90 seconds, and the median requests per second (rps) from 3 runs per platform were averaged to compare performance.

In-memory analytics

A powerful in-memory distributed key-value database, cache, and message broker with optional persistence, Redis. AMD used redis-benchmark to test Redis servers.

Single- and dual-socket AMD EPYC 9754 beat NVIDIA Grace by ~1.15x and ~2.29x, respectively (Figure 7). Additionally, the dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~1.54x in the same tests.

Ubuntu 22.04, Redis 7.0.11, and redis-benchmark 7.2.3 were installed. Each client established 512 GET/SET connections with 1000-byte keys to its Redis server. Multiple 8-core instances ran on each system. Three runs of the workload test ran 10 million requests on each system, and the median requests per second (rps) statistics were aggregated to compare performance.

Cache Tier

The high-performance, distributed in-memory caching system Memcached stores key-value pairs for short amounts of arbitrary data like strings or objects. It caches rendered pages and database or API calls. AMD used the popular memtier benchmarking tool to evaluate latency and throughput improvements.

Single and dual-socket AMD EPYC 9754 computers outscored NVIDIA Grace by ~1.16x and ~2.26x, respectively. Additionally, the dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~1.97x in the same tests.

The computers ran Ubuntu v22.04, Memcached v1.6.14, and memtier v1.4.0. Each memtier client had 10 connections, 8 pipelines, and a 1:10 SET/GET ratio with its Memcached server. Multiple instances with 8 cores ran on each system. To compare performance, the workload test processed 10 million requests on each system and averaged the median requests per second (rps) from three runs per platform.

High-performance computing

HPC impacts every sector of our lives where performance is critical, from manufacturing to life sciences. ARM processors excel at lighter workloads but struggle with HPC and crucial data-centric tasks. Some apps have been ported to ARM, however they lack the advanced features of x86 processors that increase HPC performance. Another factor is memory capacity: 4th Gen AMD EPYC CPUs can support 3 TB, whereas ARM-based NVIDIA Grace can only support 480 GB.

Compiling HPC apps for ARM processors and diagnosing runtime errors is difficult. When testing common open-source HPC workloads for fast turnaround times, AMD engineers encountered the following issues:

Runtime issues occur despite source code updates in NAMD.

GROMACS compiles incorrectly and requires source adjustment.

OpenRadioss fails to compile and needs Pull Requests and adjustments for ARM instructions.

WRF and OpenFOAM dependencies do not compile.

AMD engineers easily compiled and tested these open-source HPC workloads:

HPL needs modest cmake adjustments to compile and run with the ARM performance math library.

Scalapack is not included in the ARM performance math library, yet Quantum ESPRESSO compiles and runs.

These issues highlight the importance of AMD EPYC chips’ x86 CPU architecture compatibility between generations. Compare the performance of these two workloads.

High-performance Linpack

Figure 9 shows that the dual-socket AMD EPYC 9754 machine outperformed NVIDIA Grace by ~2.34x. Additionally, a dual-socket AMD EPYC 9654 system outperformed the NVIDIA system by ~1.97x in the same tests.

NVIDIA Grace runs Red Hat Enterprise Linux 9.4 with kernel 5.14.0-427.18.1.el9_4.aarch64+64k and has 480GB of LPDDR5X-8532 memory, while AMD EPYC 9754 and 9654 have 1.5 TB of DDR5-4800.

Quantum ESPRESSO

Open-source Quantum ESPRESSO calculates nanoscale electronic structures and materials using density-functional theory, plane waves, and pseudo potentials. Comparing system performance with Quantum ESPRESSO 7.0 ausurf benchmark.

The dual-socket AMD EPYC 9754 computer outscored NVIDIA Grace by ~4.08x. Additionally, a dual-socket AMD EPYC 9654 machine outperformed the NVIDIA system by ~3.46x in the same tests.

NVIDIA Grace runs Red Hat Enterprise Linux 9.4 with kernel 5.14.0-427.18.1.el9_4.aarch64+64k and has 480GB of LPDDR5X-8532 memory, while AMD EPYC 9754 and 9654 have 1.5 TB of DDR5-4800.

Video Encoding

From classic to modern, FFmpeg can encode, decode, convert, stream, filter, and play most video formats. Its format conversion, video resizing, editing, and seamless streaming make it popular.

The systems ran Ubuntu 22.04 and FFmpeg 4.4.2. Each FFmpeg instance transcoded a 4K input file into raw video using the VP9 codec. Multiple 8-core instances ran on each system. Each system was evaluated by its median total frames processed per hour over three test runs.

AMD EPYC 9754 Price

The AMD EPYC 9754 processor costs around $11,900 at launch. Its high-end server capabilities and top-tier EPYC lineup position impact its pricing (CPU / processor comparison) (Club386).

Conclusion

General-purpose AMD EPYC 9654 and, most critically, cloud-native AMD EPYC 9764 processors outperformed NVIDIA Grace in eleven fundamental, SQL, and HPC applications. This and my June blog statistics show that 4th Gen AMD EPYC processors remain market leaders in performance and power efficiency.

With the upcoming release of 5th Gen AMD EPYC processors (codenamed “Turin”) with up to 192 cores per processor and a fully compatible x86 architecture that runs your current and future workloads, AMD will extend our performance and energy efficiency leads. Let’s keep this a secret until then.

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govindhtech · 3 months

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IBM Cloud Bare Metal Servers for VPCs Use 4th Gen Intel Xeon

The range of IBM Cloud Bare Metal Servers for Virtual Private Clouds is being shaken up by new 4th Gen Intel Xeon processors and dynamic network bandwidth.

With great pleasure, IBM is thrilled to announce that the fourth generation of Intel Xeon CPUs are now available on IBM Cloud Bare Metal Servers for Virtual Private Clouds. IBM customers now have the ability to provision Intel’s most recent micro architecture within their very own virtual private cloud. This allows them to get access to a variety of performance benefits, such as increased core-to-memory ratios (21 new server profiles) and dynamic network bandwidth that is only available through IBM Cloud VPC. For those who are following track, that is three times as many provisioning options as their present Intel Xeon CPUs, which are of the second generation. Take a look around.

Are these servers made of bare metal suitable for my needs?

In addition to having rapid provisioning, excellent network speeds, and the most secure software-defined resources that are accessible within IBM, IBM Cloud Bare Metal Servers for Virtual Private Clouds are hosted on their most recent and developer-friendly platform. Every single one of your central processing units would be based on the 4th gen Intel Xeon processors, which IBM initially introduced on IBM Cloud Bare Metal Servers for traditional infrastructure in conjunction with Intel’s day-one release product.

The traditional IBM Cloud infrastructure is distinct from the IBM Cloud Virtual Private Cloud. More suitable for large, steady-state, predictable activities that call for the highest possible level of customisation is this method. However, IBM Cloud Virtual Private Cloud is an excellent solution for high-availability and maximum elasticity requirements. Take a look at this brief introduction video to get a better understanding of which environment would be most suitable for your workload requirements.

The customisation choices available to you include five pre-set profile families, which contain your number of CPU instances, RAM, and bandwidth, in the event that IBM Cloud Bare Metal Servers for Virtual Private Cloud turns out to be your preferred choice. What sets IBM Cloud apart from other cloud services is the fact that each profile provides you with DDR-5 memory and dynamic network bandwidth ranging from 10 to 200 Gbps. For tasks that require a significant amount of CPU power, such as heavy web traffic operations, production batch processing, and front-end web servers, compute profiles are the most effective solution.

Balanced profiles are designed to provide a combination of performance and scalability, making them a great choice for databases of a moderate size and cloud applications that experience moderate traffic.

Memory profiles are most effective when applied to workloads that require a significant amount of memory, such as large cache and database applications, as well as in-memory analytics.

When it comes to running small to medium in-memory databases and OLAP, such as SAP BW/4 HANA, very high profiles are the most effective solutions.

Large in-memory databases and online transaction processing workloads are both excellent for ultra-high profiles because they offer the most memory per core.

For these bare metal servers, what kinds of workloads do you propose they handle?

Over the course of this year, IBM’s beta programme was exposed to a wide variety of workloads; nonetheless, there were a few noteworthy success stories that particularly stood out:

Building on top of IBM Cloud, VMware Cloud Foundation These workloads required a high core performance, interoperability with VMware, licencing portability, a smaller core count variety, and a Generic operating system, which IBM just recently launched. In a dedicated location, they conducted tests for VMware managed virtual cloud functions (VCF) as well as build-your-own VMware virtual cloud functions (VCF).

They were happy with the customisation freedom and benchmark performance enhancements that backed up their findings. During the second half of the year, these workloads will be accessible on Intel Xeon profiles of the fourth generation within the IBM Cloud Virtual Private Cloud.

With regard to HPCaaS, this workload was one of a kind, and IBM believe that it is a primary use case for this distribution. Terraform and IBM Storage Scale were used in their tests to see whether or not they could get improved performance. They were delighted with the throughput improvement and the agile provisioning experience between platforms and networking.

The task of providing financial services and banking necessitated both powerful and dedicated system performance, as well as the highest possible level of security and compliance. After conducting tests to determine capacity expansion, user interface experience, security controls, and security management, they were thrilled to find that production times had been reduced.

Beginning the process

In the data centres of IBM Cloud Dallas, Texas, bare metal servers powered by 4th gen Intel Xeon processors are currently accessible. Additional sites will be added in the second half of the year 24. The IBM Cloud Bare Metal Servers for Virtual Private Cloud catalogue allows you to view all of the pricing and provisioning options for their new 4th Gen Intel Xeon processors and save a quote to your account. As an alternative, you could start a chat and obtain some answers right now. Within their cloud documents, you can find more information by reading their getting started guides and tutorials.

Spend one thousand dollars in IBM Cloud credits

If you are an existing customer who is interested in provisioning new workloads or if you are inquisitive about deploying your first workload on IBM Cloud VPC, then you should be sure to take advantage of their limited time promotion for IBM Cloud VPC. By entering the promotional code VPC1000 within either the bare metal or virtual server catalogues, you will receive USD 1,000 in credits that may be used towards the purchase of your new virtual private cloud (VPC) resources. These resources include computing, network, and storage components. Only profiles based on the second generation of Intel Xeon processors and profiles from earlier generations are eligible for this promotion, which is only available for a limited period.