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Supermicro H13 Servers Maximize Your High-Performance Data Center

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Supermicro H13 Servers Maximize Your High-Performance Data Center

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The modern data center must be both highly performant and energy efficient. Massive amounts of data are generated at the edge and then analyzed in the data center. New CPU technologies are constantly being developed that can analyze data, determine the best course of action, and speed up the time to understand the world around us and make better decisions.

With the digital transformation continuing, a wide range of data acquisition, storage and computing systems continue to evolve with each generation of  a CPU. The latest CPU generations continue to innovate within their core computational units and in the technology to communicate with memory, storage devices, networking and accelerators.

Servers and, by default, the CPUs within those servers, form a continuum of computing and I/O power. The combination of cores, clock rates, memory access, path width and performance contribute to specific servers for workloads. In addition, the server that houses the CPUs may take different form factors and be used when the environment where the server is placed has airflow or power restrictions. The key for a server manufacturer to be able to address a wide range of applications is to use a building block approach to designing new systems. In this way, a range of systems can be simultaneously released in many form factors, each tailored to the operating environment.

The new H13 Supermicro product line, based on 4th Generation AMD EPYC™ CPUs, supports a broad spectrum of workloads and excels at helping a business achieve its goals.

Get speeds, feeds and other specs on Supermicro’s latest line-up of servers

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Manage Your HPC Resources with Supermicro's SuperCloud Composer

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Manage Your HPC Resources with Supermicro's SuperCloud Composer

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  • GigaIO

Today’s data center has numerous challenges: provisioning hardware and cloud workloads, balancing the needs of performance-intensive applications across compute, storage and network resources, and having a consistent monitoring and analytics framework to feed intelligent systems management. Plus, you may have the need to deploy or re-deploy all these resources as needs shift, moment to moment.

Supermicro has created its own tool to assist with these decisions to monitor and manage this broad IT portfolio, called the SuperCloud Composer (SCC). It combines a standardized web-based interface using an Open Distributed Infrastructure Management interface with a unified dashboard based on the RedFish message bus and service agents.

SCC can track the various resources and assign them to different pools with its own predictive analytics and telemetry. It delivers a single intelligent management solution that covers both existing on-premises IT equipment as well as a more software-defined cloud collection. Additional details can be found in this SuperCloud Composer white paper.

SuperCloud Composer makes the use of a cluster-level PCIe network using the FabreX software from GigaIO Networks. It has the capability to flexibly scale up and out storage systems while using the lowest latency paths available.

It also supports Weka.IO cluster members, which can be deployed across multiple systems simultaneously. See our story The Perfect Combination: The Weka Next-Gen File System, Supermicro A+ Servers and AMD EPYC™ CPUs.

SCC can create automated installation playbooks in Ansible, including a software boot image repository that can quickly deploy new images across the server infrastructure. It has a fast-deploy feature that allows a new image to be deployed within seconds.

SuperCloud Composer offers a robust analytics engine that collects historical and up-to-date analytics stored in an indexed database within its framework. This data can produce a variety of charts, graphs and tables so that users can better visualize what is happening with their server resources. Each end-user is provided with analytic capable charting represented by IOPS, network, telemetry, thermal, power, composed node status, storage allocation and system status.

Last but not least, SCC also has both network provisioning and storage fabric provisioning features where build plans are pushed to data or fabric switches either as single-threaded or multithreaded operations, such that multiple switches can be updated simultaneously by shared or unique build plan templates.

For more information, watch this short SCC explainer video. Or schedule an online demo of SCC and request a free 90-day trial of the software.

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Supermicro Debuts New H13 Server Solutions Using AMD’s 4th-Gen EPYC™ CPUs

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Supermicro Debuts New H13 Server Solutions Using AMD’s 4th-Gen EPYC™ CPUs

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Last week, Supermicro announced its new H13 A+ server solutions, featuring the latest fourth-generation AMD EPYC™ processors. The new AMD “Genoa”-class Supermicro A+ configurations will be able to handle up to 96 Zen4 CPU cores running up to 6TB of 12-channel DDR5 memory, using a separate channel for each stick of memory.

The various systems are designed to support the highest performance-intensive computing workloads over a wide range of storage, networking and I/O configuration options. They also feature tool-less chassis and hot-swappable modules for easier access to internal parts as well as I/O drive trays on both front and rear panels. All the new equipment can handle a range of power conditions, including 120 to 480 AC volt operation and 48 DC power attachments.

The new H13 systems have been optimized for AI, machine learning and complex calculation tasks for data analytics and other kinds of HPC applications. Supermicro’s 4th-Gen AMD EPYC™ systems employ the latest PCIe 5.0 connectivity throughout their layouts to speed data flows and provide high network and cluster internetworking performance. At the heart of these systems is the AMD EPYC™ 9004 series CPUs, which were also announced last week.

The Supermicro H13 GrandTwin® systems can handle up to six SATA3 or NVMe drive bays, which are hot-pluggable. The H13 CloudDC systems come in 1U and 2U chassis that are designed for cloud-based workloads and data centers that can handle up to 12 hot-swappable drive bays and support the Open Compute Platform I/O modules. Supermicro has also announced its H13 Hyper configuration for dual-socketed systems. All of the twin-socket server configurations support 160 PCIe 5.0 data lanes.

There are several GPU-intensive configurations for another series of both 4U and 8U sized servers that can support up to 10 GPU PCIe accelerator cards, including the latest graphic processors from AMD and Nvidia. The 4U family of servers support both AMD Infinity Fabric Link and NVIDIA NVLink Bridge technologies so users can choose the right balance of computation, acceleration, I/O and local storage specifications.

To get a deep dive on H13 products, including speeds, feeds and specs, download this whitepaper from the Supermicro site: Supermicro H13 Servers Enable High-Performance Data Centers.

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AMD’s Infinity Guard Selected by Google Cloud for Confidential Computing

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AMD’s Infinity Guard Selected by Google Cloud for Confidential Computing

Google Cloud has been working over the past several years with AMD on developing new on-chip security protocols. More on the release of the AMD EPYC™ 9004 series processors in this part three of a four-part series..

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Google Cloud has been working over the past several years with AMD on developing new on-chip security protocols that have seen further innovation with the release of the AMD EPYC™ 9004 series processors. These have a direct benefit for performance-intensive computing applications, particularly for supporting higher-density virtual machines (VMs) and using technologies that can protect data flows from leaving the confines of what Google calls confidential VMs as well as further isolating VM hypervisors. They offer a collection of N2D and C2D instances that support these confidential VMs.
 
“Product security is always our top focus,” said AMD CTO Mark Papermaster. “We are continuously investing and collaborating in the security of these technologies.” 
 
Royal Hansen, VP of engineering for Google Cloud said: “Our customers expect the most trustworthy computing experience on the planet. Google and AMD have a long history and a variety of relationships with the deepest experts on security and chip development. This was at the core of our going to market with AMD’s security solutions for datacenters.”
 
The two companies also worked together on this security analysis.
 
Called Infinity Guard collectively, the security technologies theyv'e been working on involve four initiatives:
 
1. Secure encrypted virtualization provides each VM with its own unique encryption key known only to the processor.
 
2. Secure nested paging complements this virtualization to protect each VM from any malicious hypervisor attacks and provide for an isolated and trusted environment.
 
3. AMD’s secure boot along with the Trusted Platform Module attestation of the confidential VMs happen every time a VM boots, ensuring its integrity and to mitigate any persistent threats.
 
4. AMD’s secure memory encryption and integration into the memory channels speed performance.
 
These technologies are combined and communicate using the AMD Infinity Fabric pathways to deliver breakthrough performance along with better secure communications.
 

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Are Your App Workloads Running in Parallel?

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Are Your App Workloads Running in Parallel?

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To be effective at delivering performance-intensive applications, it pays to split up your workloads and run them simultaneously, a.k.a., in parallel. In the past, we didn’t really think about the resources required to run workloads, because many business computers were all-purpose machines. There was also a tendency to run loads serially to avoid bogging down due to heavy CPU utilization, heavy I/0 and so on.

 

But computers have become much more capable of late. What were once thought of as “desktop” computers have approached the arena once occupied by minicomputers and mainframes. Like the larger systems, they serve multiple concurrent users and higher-demanding applications. As a result, we need to think more carefully about how their various components – processor, memory, storage and network connections – interact, find and eliminate the bottlenecks between these components to make them useful for higher-end workloads.
 

Straighten out Bottlenecks


One way to eliminate bottlenecks is to break your apps into smaller, more digestible pieces that can run concurrently. As the new processors employ more cores and more sophisticated components, this means that more of your code can be consumed by the entire CPU package. This is the inherent nature of parallel processing, and why the world’s fastest supercomputers now routinely span thousands (and some in the millions) of cores.


A company called Weka has developed a file system designed to provide higher-speed data ingestion and more appropriate for machine learning and advanced mathematical modeling applications. Understanding the particular type of data storage – whether it is a parallel file system such as Weka, more scratch space for computations or better backups – can make a big difference in overall performance.


But it is also important how your apps work across the network. Is there a lot of back-and-forth between clients and servers, or sending a small chunk of data and waiting for a reply? This introduces a lot of downtime for the app, and these “wait states” should be identified and potentially eliminated.
 

Offload Workloads


Does your application do a lot of calculation? As discussed in an earlier story appearing on Performance-Intensive Computing, complementary processors, such as co-processors and GPUs, can be a big performance boost so long the processor can move on to its next task, working in parallel, instead of waiting for data returned from the offloaded computation.

 

Working in parallel can be a challenge when your apps frequently pause to wait for data from another process or are highly monolithic – designed to run in a serial fashion. Such apps may be challenging to rewrite to take advantage cloud native or parallel operations. At some point, you are going to have to make that break and put in the programming effort to modernize your apps, but only you or your company can decide when it’s right to do that.

 

But if you can modify your workloads for this parallel structure and your hardware was designed to support it, you will see big benefits.

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Unlocking the Value of the Cloud for Mid-size Enterprises

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Unlocking the Value of the Cloud for Mid-size Enterprises

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Organizations around the world are requiring new options for their next-generation computing environments. Mid-size organizations, in particular, are facing increasing pressure to deliver cost-effective, high-performance solutions within their hyperconverged infrastructures (HCI). Recent collaboration between Supermicro, Microsoft Azure and AMD, leveraging their collective technologies, has created a fresh approach that lets enterprises maintain performance at a lower operational cost while helping to reduce the organization’s carbon footprint in support of sustainability initiatives. This cost-effective, 1U system (a 2U version is available) offers both power, flexibility and modularity in large-scale GPU deployments.

The results of the collaboration combine the latest technologies, supporting multiple CPU, GPU, storage and networking options optimized to deliver uniquely configured and highly scalable systems. The product can be optimized for SQL and Oracle databases, VDI, productivity applications and database analytics. This white paper explores why this universal GPU architecture is an intriguing and cost-effective option for CTOs and IT administrators who are planning to rapidly implement hybrid cloud, data center modernization, branch office/edge networking or Kubernetes deployments at scale.

Get the 7-page white paper that provides the detail to assess the solution for yourself, including the new Azure Stack HCI certified system, specifications, cost justification and more.

 

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Register to Watch Supermicro's Sweeping A+ Launch Event on Nov. 10

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Register to Watch Supermicro's Sweeping A+ Launch Event on Nov. 10

Join Supermicro online Nov. 10th to watch the unveiling of the company’s new A+ systems -- featuring next-generation AMD EPYC™ processors. They can't tell us any more right now. But you can register for a link to the event by scrolling down and signing-up on this page.
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Energy-Efficient AMD EPYC™ Processors Bring Significant Savings

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Energy-Efficient AMD EPYC™ Processors Bring Significant Savings

Cut electricity consumption by up to half with AMD's power-saviing EPYC™ processors.

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  • Ateme, DBS, Nokia

Nokia was able to target up to a 40% reduction in server power consumption using EPYC. DBS and Ateme each experienced a 50% drop in energy costs. AMD’s EPYC™ processors can provide big energy-saving benefits, so you can meet your most demanding application performance requirements and still provide planetary and environmental efficiencies.

For example: To provide a collection of 1,200 virtual machines, AMD would require 10 servers compared to 15 for those built using equivalent Intel CPUs. This translates into a 41% lower total cost of ownership over a three-year period, with a third less energy consumption, saving on carbon emissions too. For deep detail and links to case studies by the companies mentioned above. Find out how they  saved significantly on energy-costs while reducing their carbon footprints, check out the infographic.

 

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The Perfect Combination: The Weka Next-Gen File System, Supermicro A+ Servers and AMD EPYC™ CPUs

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The Perfect Combination: The Weka Next-Gen File System, Supermicro A+ Servers and AMD EPYC™ CPUs

Weka’s file system, WekaFS, unifies your entire data lake into a shared global namespace where you can more easily access and manage trillions of files stored in multiple locations from one directory.

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  • Weka.io

One of the challenges of building machine learning (ML) models is managing data. Your infrastructure must be able to process very large data sets rapidly as well as ingest both structured and unstructured data from a wide variety of sources.

 

That kind of data is typically generated in performance-intensive computing areas like GPU-accelerated applications, structural biology and digital simulations. Such applications typically have three problems: how to efficiently fill a data pipeline, how to easily integrate data across systems and how to manage rapid changes in data storage requirements. That’s where Weka.io comes into play, providing higher-speed data ingestion and avoiding unnecessary copies of your data while making it available across the entire ML modeling space.

 

Weka’s file system, WekaFS, has been developed just for this purpose. It unifies your entire data lake into a shared global namespace where you can more easily access and manage trillions of files stored in multiple locations from one directory. It works across both on-premises and cloud storage repositories and is optimized for cloud-intensive storage so that it will provide the lowest possible network latencies and highest performance.

 

This next-generation data storage file system has several other advantages: it is easy to deploy, entirely software-based, plus it is a storage solution that provides all-flash level performance, NAS simplicity and manageability, cloud scalability and breakthrough economics. It was designed to run on any standard x86-based server hardware and commodity SSDs or run natively in the public cloud, such as AWS.

 

Weka’s file system is designed to scale to hundreds of petabytes, thousands of compute instances and billions of files. Read and write latency for file operations against active data is as low as 200 microseconds in some instances.

 

Supermicro has produced its own NVMe Reference Architecture that supports WekaFS on some of its servers, including the Supermicro A+ AS-1114S-WN10RT and AS-2114S-WN24RT using the AMD EPYC™ 7402P processors with at least 2TB of memory, expandable to 4TB. Both servers support hot-swappable NVMe storage modules for ultimate performance. Also check out the Supermicro WekaFS A/I and HPC Solution Bundle.

 

 

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Supermicro SuperBlades®: Designed to Power Through Distributed AI/ML Training Models

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Supermicro SuperBlades®: Designed to Power Through Distributed AI/ML Training Models

Running heavy AI/ML workloads can be a challenge for any server, but the SuperBlade has extremely fast networking options, upgradability, the ability to run two AMD EPYC™ 7000-series 64-core processors and the Horovod open-source framework for scaling deep-learning training across multiple GPUs.

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Running the largest artificial intelligence (AI) and machine learning (ML) workloads is a job for the higher-performing systems. Such loads are often tough for even more capable machines. Supermicro’s SuperBlade combines blades using AMD EPYC™ CPUs with competing GPUs into a single rack-mounted enclosure (such as the Supermicro SBE-820H-822). That leverages an extremely fast networking architecture for these demanding applications that need to communicate with other servers to complete a task.

 

The Supermicro SuperBlade fits everything into an 8U chassis that can host up to 20 individual servers. This means a single chassis can be divided into separate training and model processing jobs. The components are key: servers can take advantage of the 200G HDR InfiniBand network switch without losing any performance. Think of this as delivering a cloud-in-a-box, providing both easier management of the cluster along with higher performance and lower latencies.

 

The Supermicro SuperBlade is also designed as a disaggregated server, meaning that components can be upgraded with newer and more efficient CPUs or memory as technology progresses. This feature significantly reduces E-waste.


The SuperBlade line supports a wide selection of various configurations, including both CPU-only and mixed CPU/GPU models, such as the SBA-4119SG, which comes with up to two AMD EPYC™ 7000-series 64-core CPUs. These components are delivered on blades that can easily slide right in. Plus, they slide out as easily when you need to replace the blades or the enclosure. The SuperBlade servers support a wide network selection as well, ranging from 10G to 200G Ethernet connections.

 

The SuperBlade employs the Horovod distributed model-training, message-passing interface to let multiple ML sessions run in parallel, maximizing performance. In a sample test of two SuperBlade nodes, the solution was able to process 3,622 GoogleNet images/second, and eight nodes were able to scale up to 13,475 GoogleNet images/second.


As you can see, Supermicro’s SuperBlade improves performance-intensive computing and boosts AI and ML use cases, enabling larger models and data workloads. The combined solution enables higher operational efficiency to automatically streamline processes, monitor for potential breakdowns, apply fixes, more efficiently facilitate the flow of accurate and actionable data and scale up training across multiple nodes.

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