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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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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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Mercedes-AMG F1 Racing Team Gains an Edge with AMD’s EPYC™ Processors

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Mercedes-AMG F1 Racing Team Gains an Edge with AMD’s EPYC™ Processors

In F1, fast cars and fast computers go hand in hand. Computational performance became more important when F1 IT authorities added rules that dictate how much computing and wind tunnel time each team can use. Mercedes was the top finisher in 2021 giving it the biggest compute/wind tunnel handicap. So, when it selected a new computer system, it opted for AMD EPYC™ processors, gaining 20% performance improvement to get more modeling done in less time.

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In the high-stakes world of Formula One racing, finding that slight edge to build a better performing car often means using the most powerful computers to model aerodynamics. The Mercedes-AMG Petronas F1 racing team found that using AMD EPYC™ processors helps gain that edge. Since 2010, the team has brought home 124 race wins and nine driver’s championships across the F1 racing circuit.

 

Thanks to the increased performance of these AMD EPYC™ CPUs, the team is able to run twice the number of daily simulations. The key is having the best computational fluid dynamics models available. And time is of the essence because the racing association’s IT authorities have added rules that dictate how much computing and wind tunnel time each team can use, along with a dollar limit on computing resources to level the playing field despite resource differences.

 

Teams that traditionally have been top finishers of the race are allowed a third less computing time, and since the Mercedes team was the top 2021 finisher, it has the least computing allocation. The 2022 race limited computing expenditures to $140M, and for 2023, the number will be further cut to $135M. The result is that teams are focused on finding the highest performing computers at the lowest cost. In F1, fast cars and fast computers go hand in hand.

 

“Performance was the key driver of the decision making,” said Simon Williams, Head of Aero Development Software for the team. “We looked at AMD and the competitors. We needed to get this right, because we're going to be using this hardware for the next three years.” Mercedes replaced its existing three-year old computers with AMD EPYC™-based systems and gained 20% performance improvements, letting it run many more simulations in parallel. “I can't stress enough how important the fast turnaround is,” Williams said. “It's been great having AMD help us achieve that."

 

Servers such as the Supermicro A+ series can bring home big wins as well.

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Eliovp Increases Blockchain-Based App Performance with Supermicro Servers

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Eliovp Increases Blockchain-Based App Performance with Supermicro Servers

Eliovp, which brings together computing and storage solutions for blockchain workloads, rewrote its code to take full advantage of AMD’s Instinct™ MI100 and MI250 GPUs. As a result, Eliovp’s blockchain calculations run up to 35% faster than what it saw on previous generations of its servers.

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When you’re building blockchain-based applications, you typically need a lot of computing and storage horsepower. This is the niche that Belgium-based Eliovp fills. They have developed a line of extremely fast cloud-based servers designed to run demanding blockchain workloads.

 

Eliovp has been recognized as the top Filecoin storage provider in Europe. This refers to a decentralized blockchain-based protocol that lets anyone rent spare local storage and is a key Web3 component.

 

To satisfy the compute  and storage needs, Eliiovp employs Supermicro’s A+ AS-1124US® and AS-4124GS® servers, running quad-core AMD EPYC™ 7543 and 7313 CPUs and as many as 8 AMD Instinct™ MI100 and MI250 GPUs to further boost performance.

 

What makes these servers especially potent is that Eliovp rewrote its code to run on this specific AMD Instinct™ GPU family. As a result, Eliovp’s blockchain calculations run up to 35% faster than what it saw on previous generations of its servers.

 

One of the attractions of the Supermicro servers is the capability to leverage the high-density core count and higher clock speeds as well as the 32 memory slots. And it comes packaged in a relatively small form factor.

 

“By working with Supermicro, we get new generations of servers with AMD technology earlier in our development cycle, enabling us to bring our products to market faster," said Elio Van Puyvelde, CEO of Eliovp. The company was able to take advantage of new CPU and GPU instructions and memory management to make its code more efficient and effective. Eliovp was also able to reduce overall server power consumption, which is always important in blockchain applications that span dozens of machines.

 

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Microsoft Azure’s More Capable Compute Instances Take Advantage of the Latest AMD EPYC™ Processors

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Microsoft Azure’s More Capable Compute Instances Take Advantage of the Latest AMD EPYC™ Processors

Azure HBv3 series virtual machines (VMs) are optimized for HPC applications, such as fluid dynamics, explicit and implicit finite element analysis, weather modeling, seismic processing, and various simulation tasks. HBv3 VMs feature up to 120 Third-Generation AMD EPYC™ 7v73X-series CPU cores with more than 450 GB of RAM.

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Increasing demands for higher-performance computing mean that the cloud-based computing needs to ratchet up its performance too. Microsoft Azure has introduced more capable compute virtual machines (VMs) that take advantage of the latest from AMD EPYC™ processors. This means that developers can easily spin up VMs that normally cost thousands of dollars if they were to purchase their physical equivalents.

 

This story's focus is on two of Azure's series: HBv3 and NVv4. In most cases, a single virtual machine is used to take advantage of all its resources. High-performance examples of Azure HBv3 series VMs are optimized for HPC applications, such as fluid dynamics, explicit and implicit finite element analysis, weather modeling, seismic processing, and various simulation tasks. HBv3 VMs feature up to 120 Third-Generation AMD EPYC™ 7v73X-series CPU cores with more than 450 GB of RAM. This series of VMs has processor clock frequencies up to 3.5GHz. All HBv3-series VMs feature 200Gb/sec HDR InfiniBand switches to enable supercomputer-scale HPC workloads. The VMs are connected and optimized to deliver the most consistent performance. Get more information about AMD EPYC™ and Microsoft Azure virtual machines.

 

A Dutch construction company, TBI, is using the Azure NVv4 to run computer-aided design and building modeling tasks on a series of virtual Windows desktops. The NVv4 VMs are only available running Windows powered by from four to 32 AMD EPYC™ vCPUs and offering a partial to full AMD Instinct™ M125 GPU with memory ranging from 2GB to 17GB. Previous generations of NV instances used Intel CPUs and NVIDIA GPUs that offer less performance.

 

TBI chose this solution because it was cheaper, easier to support and keep its software collection updated. Using virtual desktops meant that no client data was stored on any laptops, making things more secure. Also, these instances delivered equivalent performance, taking advantage of the SR-IOV technology.

 

Supermicro offers a wide range of servers that incorporate the AMD EPYC™ CPU and a number of servers optimized for applications that use GPUs. These servers range from 1U rackmount servers to high end 4U GPU optimized systems. Whether you’re using it on-prem or you’re building your own cloud, Supermicro’s Aplus servers are optimized for performance and technical computing applications and they run Azure and other systems well. Get more information about Supermicro servers with AMD’s EPYC™ CPUs.

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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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Supermicro and Qumulo Deliver High-Performance File Data Management Solution

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Supermicro and Qumulo Deliver High-Performance File Data Management Solution

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One of the issues that’s key to delivering higher-performing computing solutions is something that predates the PC itself: managing distributed file systems. The challenge becomes more acute when the applications involve manipulating large quantities of data. The tricky part is in how they scale to support these data collections, which might consist of video security footage, life sciences data collections and other research projects.

 

Storage systems from Qumulo integrate well into a variety of existing environments, such as those involving multiple storage protocols and file systems. The company supports a wide variety of use cases that allow for scaling up and out to handle Petabyte data quantities. Qumulo can run at both the network edge, in the data center and on various cloud environments. Their systems run on Supermicro’s all non-volatile memory express (NVMe) platform, the highest performing protocol designed for manipulating data stored on SSD drives. The servers are built on 24-core 2.8 GHz AMD EPYC™ processors.


 

Qumulo provides built-in near real-time data analytics that let IT administrators predict storage trends and better manage storage capacity so that they can proactively plan and optimize workflows.

 

The product handles seamless file and object data storage, is hardware agnostic, and supports single data namespace and burstable computing running on the three major cloud providers (AWS, Google and Azure) with nearly instant data replication. Its distributed file system is designed to handle billions of files and works equally well on both small and large file sizes.

 

Qumulo also works on storage clusters, such as those created with Supermicro AS-1114S servers, which can accommodate up to 150TB per storage node. Qumulo Shift for Amazon S3 is a feature that lets users copy data to the Amazon S3 native format for easy access to AWS services if the required services are not available in an on-prem data center. 

For more information, see the white paper on the Supermicro and Qumulo High-Performance File Data Management and Distributed Storage solution, powered by AMD EPYC™ processors.

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Red Hat’s OpenShift Runs More Efficiently with Supermicro’s SuperBlade® Servers

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Red Hat’s OpenShift Runs More Efficiently with Supermicro’s SuperBlade® Servers

The Supermicro SuperBlade's advantage for the Red Hat OCP environment is that it supports a higher-density infrastructure and lower-latency network configuration, along with benefits from reduced cabling, power and shared cooling features. SuperBlades feature multiple AMD EPYC™ processors using fast DDR4 3200MHz memory modules.

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  • Red Hat

Red Hat’s OpenShift Container Platform (OCP) provides enterprise Kubernetes-bundled devops pipelines. It automates builds and container deployments and lets developers focus on application logic while leveraging best-of-class enterprise infrastructure.

 

OpenShift supports a broad range of programming languages, web frameworks, databases, connectors to mobile devices and external back ends. OCP supports cloud-native, stateless applications and traditional applications. Because of its flexibility and utility in running advanced applications, OCP has become one of the go-to places that support high-performance computing.

 

Red Hat’s OCP comes in several deployment packages, including as a managed service running on the major cloud platforms, as virtual machines, and on “bare metal” servers, meaning a user installs all the software needed for the platform and is the sole tenant of the server.

 

It’s that last use case in which Supermicro’s SuperBlade servers are especially useful. Their advantage is that they support a higher-density infrastructure and lower-latency network configuration, along with benefits from reduced cabling, power and shared cooling features.

 

The SuperBlade comes in an 8U chassis with room to accommodate up to 20 hot-pluggable nodes (processor, network and storage) in a variety of more than a dozen models that support serial-attached SCSI, ordinary SATA drives, and GPU processor modules. It sports multiple AMD EPYC™ processors using fast DDR4 3200MHz memory modules.

A chief advantage of the SuperBlade is that it can support a variety of higher-capacity OCP workload configurations and do so within a single server chassis. This is critical because OCP requires a variety of server roles to deliver its overall functionality, and having these roles working inside of a chassis means performance  and latency benefits. For example, you could partition a SuperBlade’s 20 nodes into various OCP components such as administrative, management, storage, worker, infrastructure and load balancer nodes, all operating within a single chassis. For deeper detail about running OCP on the SuperBlade, check out this Supermicro white paper.

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Single-Root I/O Virtualization Delivers a Big Boost for Performance-Intensive Environments

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Single-Root I/O Virtualization Delivers a Big Boost for Performance-Intensive Environments

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Single-root I/O virtualization (SR-IOV) is an interesting standard for performance-intensive computing because it lets a network adapter access resources across a PCIe bus, making it even higher performing. It lets data traffic be routed directly to a particular virtual machine (VM) without interrupting the flow of other traffic across the bus. It does that by bypassing the software switching layer of the virtualization stack, thereby reducing the input/output overhead and improving network performance, stability and reliability. (Get more information about SR-IOV in VMware and Microsoft contexts, for example.)

 

What this means, especially in GPU-based computing, is that each VM has its own dedicated share of the GPU and isn’t forced to compete with other VMs for its share of resources. The feature also helps isolate each VM and is the basic building block for modern VM hyperscale technologies.

 

Tests of SR-IOV have found big benefits, such as lowering processor utilization by 50% and boosting network throughput by up to 30%. This allows for more VMs per host and being able to run heavier workloads on each VM.
 

An excellent server for any virtualization platform is the Supermicro BigTwin® server. With up to 4 servers in just 2U, the Supermicro BigTwin is a versatile and powerful multi-node system that is environmentally friendly due to its shared components. Plus it can handle a wide range of workloads. Learn more about the Supermicro BigTwin model AS -2124BT-HTR.

 

Not a New Idea
 

The technology isn’t new: Scott Lowe wrote about it back in 2009 and SR-IOV was initially supported by Microsoft Windows Server 2012 and with AMD chipsets in 2016. This support has been extended with Azure NVv4 and AWS EC2 G4ad virtual machine instances, which are based on the AMD EPYC™ 7002 CPU and Radeon Pro™ GPU processor families.

The standard is supported by both VMware and Microsoft’s Hyper-V hosts and in various AMD EPYC™ CPU chipsets with MxGPU technology that is built into the actual silicon. This enables sharing a GPU’s power across multiple users or VMs but providing a similar performance level of a discrete processor.

The SR-IOV technology is a big benefit for immersive cloud-based gaming, desktop-as-a-service, machine learning models and 3D rendering applications.

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Build an Accelerated Data Center with AMD's Third-Gen EPYC™ CPUs

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Build an Accelerated Data Center with AMD's Third-Gen EPYC™ CPUs

“AMD EPYC™ processors are now a part of the world’s hyperscale data centers,” said Lisa Su, AMD’s CEO. Meta/Facebook is now building its servers with powerful third-generation AMD EPYC™ CPUs.

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If you're making plans to build a high-performance data center, be sure to take a close look at the latest version of AMD's EPYC™ CPU chipsets, which were code-named “Milan X.”

 

Servers that employ AMD’s third-generation EPYC™ CPUs are so powerful that Meta/Facebook is now building its servers with them, using the new single-socket cloud-scale design, which is a part of their Open Compute Project. “AMD EPYC™ processors are now a part of the world’s hyperscale data centers,” said Lisa Su, AMD’s CEO, in the presentation at which she debuted the processors.

 

This latest generation of AMD EPYC™ CPUs uses an innovative packaging option of 3D stacking of chiplets for high-performance computing applications. Higher density cached memory is stacked on top of the processor to deliver more than 200 times the interconnected density of prior chiplet packaging designs. “It is the most flexible active-on-active silicon technology available in the world,” Su said. “It consumes much less energy and fits into existing CPU sockets, too.” AMD's latest chipsets satisfy the higher demands of cloud computing and electronic circuit design applications.

 

Jason Zander, EVP Microsoft Azure, said that Microsoft's partnership with AMD has let the cloud computing company deliver cloud instances that can run up to 12 times the speed of earlier offerings. “That rivals some supercomputers,” he said. Azure has configured some of the most powerful virtual instances, which are running on the latest AMD EPYC™ processors. They are available from 16 cores up to 120 cores and can share 448 GB of memory and 480 MB of L3 cache among the processors. For deeper information, see this Microsoft blog.

 

Circuit design demands the fastest processors. “The next step for AMD is to deliver more differentiation in value with a focus on performance per core,” said Dan McNamara, general manager of AMD’s Server Business Unit. “In our tests comparing Synopsys VCS chip-design simulation software running on older and newer AMD EPYC™ CPUs, engineers were able to complete 66% more jobs in the same elapsed time, thanks to having a larger L3 cache. This means that more data can be kept closer to the processor for better performance.” These faster product design lifecycles mean faster times to market since designers can save time in the testing process.

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Fast Supermicro A+ Servers with Dual AMD EPYC™ CPUs Support Scientific Research in Hungary

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Fast Supermicro A+ Servers with Dual AMD EPYC™ CPUs Support Scientific Research in Hungary

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The Budapest Institute for Computer Science and Control (known as SZTAKI) conducts a wide range of scientific research spanning the fields of physics, computer science, industrial controls and intelligent systems. The work involves medical image processing, autonomous vehicles, robotics and natural language processing, all areas that place heavy demands on computing equipment and a natural use case for performance-intensive computing.


SZTAKI has been in operation since 1964 and has more than 300 full-time staff, with more than 70 of them holding science-related degrees. It works with both government and other academic institutions jointly on research projects as well as contract research and development of custom computer-based applications.

The institute also coordinates similar types of work done at Hungary’s AI national lab. For example, there are several projects underway to develop AI-based solutions to process the Hungarian language and build computational-based models that can be more effective and not require as much training as earlier models. They are also working on creating more transparent and explainable machine learning models to make them more reliable and more resilient in preserving data privacy.

SZTAKI has been using Supermicro’s A+ 4124GO-NART servers with GPUs that are configured with two AMD EPYC™ 7F72 CPUs. “Our researchers are now able to advance our use of AI and focus on more advanced research," said Andras Benczur, scientific director at the AI lab. One challenges they face is keeping up with the advanced algorithms that its researchers have developed. Having the Supermicro servers, which operate at 20x the speed of previous servers, means that researchers can execute coding and modeling decisions far more quickly.

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