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AMD and Supermicro: Pioneering AI Solutions

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AMD and Supermicro: Pioneering AI Solutions

In the constantly evolving landscape of AI and machine learning, the synergy between hardware and software is paramount. Enter AMD and Supermicro, two industry titans who have joined forces to empower organizations in the new world of AI with cutting-edge solutions.

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Bringing AMD Instinct to the Forefront

In the constantly evolving landscape of AI and machine learning, the synergy between hardware and software is paramount. Enter AMD and Supermicro, two industry titans who have joined forces to empower organizations in the new world of AI with cutting-edge solutions. Their shared vision? To enable organizations to unlock the full potential of AI workloads, from training massive language models to accelerating complex simulations.

The AMD Instinct MI300 Series: Changing The AI Acceleration Paradigm

At the heart of this collaboration lies the AMD Instinct MI300 Series—a family of accelerators designed to redefine performance boundaries. These accelerators combine high-performance AMD EPYC™ 9004 series CPUs with the powerful AMD InstinctTM MI300X GPU accelerators and 192GB of HBM3 memory, creating a formidable force for AI, HPC, and technical computing.

Supermicro’s H13 Generation of GPU Servers

Supermicro’s H13 generation of GPU Servers serves as the canvas for this technological masterpiece. Optimized for leading-edge performance and efficiency, these servers integrate seamlessly with the AMD Instinct MI300 Series. Let’s explore the highlights:

8-GPU Systems for Large-Scale AI Training:

  • Supermicro’s 8-GPU servers, equipped with the AMD Instinct MI300X OAM accelerator, offer raw acceleration power. The AMD Infinity Fabric™ Links enable up to 896GB/s of peak theoretical P2P I/O bandwidth, while the 1.5TB HBM3 GPU memory fuels large-scale AI models.
  • These servers are ideal for LLM Inference and training language models with trillions of parameters, minimizing training time and inference latency, lowering the TCO and maximizing throughput.

Benchmarking Excellence

But what about real-world performance? Fear not! Supermicro’s ongoing testing and benchmarking efforts have yielded remarkable results. The continued engagement between AMD and Supermicro performance teams enabled Supermicro to test pre-release ROCm versions with the latest performance optimizations and publicly released optimization like Flash Attention 2 and vLLM. The Supermicro AMD-based system AS -8125GS-TNMR2 showcases AI inference prowess, especially on models like Llama-2 70B, Llama-2 13B, and Bloom 176B. The performance? Equal to or better than AMD’s published results from the Dec. 6 Advancing AI event.

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Charles Liang’s Vision

In the words of Charles Liang, President and CEO of Supermicro:

“We are very excited to expand our rack scale Total IT Solutions for AI training with the latest generation of AMD Instinct accelerators. Our proven architecture allows for fully integrated liquid cooling solutions, giving customers a competitive advantage.”

Conclusion

The AMD-Supermicro partnership isn’t just about hardware and software stacks; it’s about pushing boundaries, accelerating breakthroughs, and shaping the future of AI. So, as we raise our virtual glasses, let’s toast to innovation, collaboration, and the relentless pursuit of performance and excellence.

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Supermicro Adds AI-Focused Systems to H13 JumpStart Program

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Supermicro Adds AI-Focused Systems to H13 JumpStart Program

Supermicro is now letting you validate, test and benchmark AI workloads on its AMD-based H13 systems right from your browser. 

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Supermicro has added new AI-workload-optimized GPU systems to its popular H13 JumpStart program. This means you and your customers can validate, test and benchmark AI workloads on a Supermicro H13 system right from your PC’s browser.

The JumpStart program offers remote sessions to fully configured Supermicro systems with SSH, VNC, and web IPMI. These systems feature the latest AMD EPYC 9004 Series Processors with up to 128 ‘Zen 4c’ cores per socket, DDR5 memory, PCIe 5.0, and CXL 1.1 peripherals support.

In addition to previously available models, Supermicro has added the H13 4U GPU System with dual AMD EPYC 9334 processors and Nvidia L40S AI-focused universal GPUs. This H13 configuration is designed for heavy AI workloads, including applications that leverage machine learning (ML) and deep learning (DL).

3 simple steps

The engineers at Supermicro know the value of your customer’s time. So, they made it easy to initiate a session and get down to business. The process is as simple as 1, 2, 3:

  • Select a system: Go to the main H13 JumpStart page, then scroll down and click one of the red “Get Access” buttons to browse available systems. Then click “Select Access” to pick a date and time slot. On the next page, select the configuration and press “Schedule” and then “Confirm.”
  • Sign In: log in with a Supermicro SSO account to access the JumpStart program. If you or your customers don’t already have an account, creating a new account is both free and easy.
  • Initiate secure access: When the scheduled time arrives, begin the session by visiting the JumpStart page. Each server will include documentation and instructions to help you get started quickly.

So very secure

Security is built into the program. For instance, the server is not on a public IP address. Nor is it directly addressable to the Internet. Supermicro sets up the jump server as a proxy, and this provides access to only the server you or your customer are authorized to test.

And there’s more. After your JumpStart session ends, the server is manually secure-erased, the BIOS and firmware are re-flashed, and the OS is reinstalled with new credentials. That way, you can be sure any data you’ve sent to the H13 system will disappear once the session ends.

Supermicro is serious about its security policies. However, the company still warns users to keep sensitive data to themselves. The JumpStart program is meant for benchmarking, testing and validation only. In their words, “processing sensitive data on the demo server is expressly prohibited.”

Keep up with the times

Supermicro’s expertly designed H13 systems are at the core of the JumpStart program, with new models added regularly to address typical workloads.

In addition to the latest GPU systems, the program also features hardware focused on evolving data center roles. This includes the Supermicro H13 CloudDC system, an all-in-one rackmount platform for cloud data centers. Supermicro CloudDC systems include single AMD EPYC 9004 series processors and up to 10 hot-swap NVMe/SATA/SAS drives.

You can also initiate JumpStart sessions on Supermicro Hyper Servers. These multi-use machines are optimized for tasks including cloud, 5G core, edge, telecom and hyperconverged storage.

Supermicro Hyper Servers included in the company’s JumpStart program offer single or dual processor configurations featuring AMD EPYC 9004 processors and up to 8TB of DDR5 memory in a 1U or 2U form factor.

Helping your customers test and validate a Supermicro H13 system for AI is now easy. Just get a JumpStart.

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Supermicro debuts 3 GPU servers with AMD Instinct MI300 Series APUs

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Supermicro debuts 3 GPU servers with AMD Instinct MI300 Series APUs

The same day that AMD introduced its new AMD Instinct MI300 series accelerators, Supermicro debuted three GPU rackmount servers that use the new AMD accelerated processing units (APUs). One of the three new systems also offers energy-efficient liquid cooling.

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Supermicro didn’t waste any time.

The same day that AMD introduced its new AMD Instinct MI300 series accelerators, Supermicro debuted three GPU rackmount servers that use the new AMD accelerated processing units (APUs). One of the three new systems also offers energy-efficient liquid cooling.

Here’s a quick look, plus links for more technical details:

Supermicro 8-GPU server with AMD Instinct MI300X: AS -8125GS-TNMR2

This big 8U rackmount system is powered by a pair of AMD EPYC 9004 Series CPUs and 8 AMD Instinct MI300X accelerator GPUs. It’s designed for training and inference on massive AI models with a total of 1.5TB of HBM3 memory per server node.

The system also supports 8 high-speed 400G networking cards, which provide direct connectivity for each GPU; 128 PCIe 5.0 lanes; and up to 16 hot-swap NVMe drives.

It’s an air-cooled system with 5 fans up front and 5 more in the rear.

Quad-APU systems with AMD Instinct MI300A accelerators: AS -2145GH-TNMR and AS -4145GH-TNMR

These two rackmount systems are aimed at converged HPC-AI and scientific computing workloads.

They’re available in the user’s choice of liquid or air cooling. The liquid-cooled version comes in a 2U rack format, while the air-cooled version is packaged as a 4U.

Either way, these servers are powered by four AMD Instinct MI300A accelerators, which combine CPUs and GPUs in an APU. That gives each server a total of 96 AMD ‘Zen 4’ cores, 912 compute units, and 512GB of HBM3 memory. Also, PCIe 5.0 expansion slots allow for high-speed networking, including RDMA to APU memory.

Supermicro says the liquid-cooled 2U system provides a 50%+ cost savings on data-center energy. Another difference: The air-cooled 4U server provides more storage and an extra 8 to 16 PCIe acceleration cards.

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Tech Explainer: How does design simulation work? Part 1

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Tech Explainer: How does design simulation work? Part 1

Design simulation lets designers and engineers create, test and improve designs of real-world airplanes, cars, medical devices and more while working safely and quickly in virtual environments. This workflow also reduces the need for physical tests and allows designers to investigate more alternatives and optimize their products.

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Design simulation is a type of computer-aided engineering used to create new products, reducing the need for physical prototypes. The result is a faster, more efficient design process in which complex physics and math do much of the heavy lifting.

Rapid advances in CPUs and GPUs that are used to perform simulation and software have made it possible to shift product design from the physical world to a virtual one.

In this virtual space, engineers can create and test new designs as quickly as their servers can calculate the results and then render them with visualization software.

Getting better all the time

Designing via AI-powered virtual simulation offers significant improvements over older methods.

Back in the day, it might have taken a small army of automotive engineers years to produce a single new model. Prototypes were often sculpted from clay and carted into a wind tunnel to test aerodynamics.

Each new model went through a seemingly endless series of time-consuming physical simulations. The feedback from those tests would literally send designers back to the drawing board.

It was an arduous and expensive process. And the resources necessary to accomplish these feats of engineering often came at the expense of competition. Companies whose pockets weren’t deep enough might fail to keep up.

Fast-forward to the present. Now, we’ve got smaller design teams aided by increasingly powerful clusters of high-performance systems.

These engineers can tweak a car’s crumple zone in the morning … run the new version through a virtual crash test while eating lunch … and send revised instructions to the design team before day’s end.

Changing designs, saving lives

Faster access to this year’s Ford Mustang is one thing. But if you really want to know how design simulation is changing the world, talk to someone whose life was saved by a mechanical heart valve.

Using the latest tech, designers can simulate new prosthetics in relation to the physiology they’ll inhabit. Many factors come into play here, including size, shape, materials, fluid dynamics, failure models and structural integrity over time.

What’s more, it’s far better to theorize how a part will interact with the human body before the doctor installs it. Simulations can warn medical pros about potential infections, rejections and physical mismatches. AI can play a big part in these types of simulations and manufacturing.

Sure, perfection may be unattainable. But the closer doctors get to a perfect match between a prosthetic and its host body, the better the patient will fair after the procedure.

Making the business case

Every business wants to cut costs, increase efficiency and get an edge over the competition. Here, too, design simulation offers a variety of ways to achieve those lofty goals.

As mentioned above, simulation can drastically reduce the need for expensive physical prototypes. Creating and testing a new airplane design virtually means not having to come within 100 miles of a runway until the first physical prototype is ready to take flight. 

Aerospace and automotive industries rely heavily on both the structural integrity of an assembly but also on computational fluid dynamics. In this way, simulation can potentially save an aerospace company billions of dollars over the long run.

What’s more, virtual airplanes don’t crash. They can’t be struck by lightning. And in a virtual passenger jet, test pilots don’t need to worry about their safety.

By the time a new aircraft design rolls onto the tarmac, it’s already been proven air-worthy—at least to the extent that a virtual simulation can make those kinds of guarantees.

Greater efficiency

Simulation makes every aspect of design more efficient. For instance, iteration, a vital element of the design process, becomes infinitely more manageable in a simulated environment.

Want to find out how a convertible top will affect your new supercar’s 0-to-60 time? Simulation allows engineers to quickly replace the hard-top with some virtual canvas and then create a virtual drag race against the original model.

Simulation can take a product to the manufacturing phase, too. Once a design is finished, engineers can simulate its journey through a factory environment.

This virtual factory, or digital twin, can help determine how long it will take to build a product and how it will react to various materials and environmental conditions. It can even determine how many moves a robot arm will need to make and when human intervention might become necessary. This process helps engineers optmize the manufacturing process.

In countless ways, simulation has never been more real.

In Part 2 of this 2-part blog, we’ll explore the digital technology behind design simulation. This cutting-edge technology is made possible by the latest silicon, vast swaths of high-speed storage, and sophisticated blade servers that bring it all together.

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Tech Explainer: What’s the difference between Machine Learning and Deep Learning? Part 2

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Tech Explainer: What’s the difference between Machine Learning and Deep Learning? Part 2

In Part 1 of this 2-part Tech Explainer, we explored the difference between how machine learning and deep learning models are trained and deployed. Now, in Part 2, we’ll get deeper into deep learning to discover how this advanced form of AI is changing the way we work, learn and create.

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Where Machine Learning is designed to reduce the need for human intervention, Deep Learning—an extension of ML—removes much of the human element altogether.

If ML were a driver-assistance feature that helped you parallel park and avoid collisions, DL would be an autonomous, self-driving car.

The human intervention we’re talking about has much to do with categorizing and labeling the data used by ML models. Producing this structured data is both time-consuming and expensive.

DL shortens the time and lowers the cost by learning from unstructured data. This elimnates much of the data pre-processing performed by humans for ML.

That’s good news for modern businesses. Market watcher IDC estimates that as much as 90% of corporate data is associated with unstructured data.

DL is particularly good at processing unstructured data. That includes information coming from the edge, the core and millions of both personal and IoT devices.

Like a brain, but digital

Deep Learning systems “think” with a neural network—multiple layers of interconnected nodes designed to mimic the way the human brain works. A DL system processes data inputs in an attempt to recognize, classify and accurately describe objects within data.

The layers of a neural network are stacked vertically. Each layer builds on the work performed by the one below it. By pushing data through each successive layer, the overall system improves its predictions and categorizations.

For instance, imagine you’ve tasked a DL system to identify pictures of junk food. The system would quickly learn—on its own—how to differentiate Pringles from Doritos.

It might do this by learning to recognize Pringles’ iconic tubular packaging. Then the system would categorize Pringles differently than the family-size sack of Doritos.

What if you fed this hypothetical DL system with more pictures of chips? Then it could begin to identify varying angles of packaging, as well as colors, logos, shapes and granular aspects of the chips themselves.

As this example illustrates, the longer a DL system operates, the more intelligent and accurate it becomes.

Things we used to do

DL tends to be deployed when it’s time to pull out the big guns. This isn’t tech you throw at a mere spam filter or recommendation engine.

Instead, it’s the tech that powers the world’s finance, biomedical advances and law enforcement. For these verticals, failure is simply not an option.

For these verticals, here are some of the ways DL operates behind the scenes:

  • BioMed: DL helps healthcare staff analyze medical imaging such as X-rays and CT scans. In many cases, the technology is more accurate than well-trained physicians with decades of experience.
  • Finance: For those seeking a market edge (read: everyone), DL employs powerful, algorithmic-based predictive analytics. This helps modern-day robber barons manage their portfolios based on insights from data so vast, they couldn’t leverage it themselves. DL also helps financial institutions assess loans, detect fraud and manage credit.
  • Law Enforcement: In the 2002 movie “Minority Report,” Tom Cruise played a police officer who could arrest people before they committed a crime. With DL, this fiction could turn into an unsettling reality. DL can be used to analyze millions of data points, then predict who is most likely to break the law. It might even give authorities an idea of where, when and how it could happen.

The future…?

Looking into a crystal ball—which these days probably uses DL—we can see a long succession of similar technologies coming. Just as ML begat DL, so too will DL beget the next form of AI—and the one after that.

The future of DL isn’t a question of if, but when. Clearly, DL will be used to advance a growing number of industries. But just when each sector will come to be ruled by our new smarty-pants robots is less clear.

Keep in mind: Even as you read this, DL systems are working tirelessly to help data scientists make AI more accurate and able to provide more useful assessments of datasets for specific outcomes. And as the science progresses, neural networks will continue to become more complex—and more like human brains.

That means the next generation of DL will likely be far more capable than the current one. Future AI systems could figure out how to reverse the aging process, map distant galaxies, even produce bespoke food based on biometric feedback from hungry diners.

For example, the upcoming AMD Instinct MI300 accelerators promise to usher in a new era of computing capabilities. That includes the ability to handle large language models (LLMs), the key approach behind generative AI systems such as ChatGPT.

Yes, the robots are here, and they want to feed you custom Pringles. Bon appétit!

 

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Can liquid-cooled servers help your customers?

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Can liquid-cooled servers help your customers?

Liquid cooling can offer big advantages over air cooling. According to a new Supermicro solution guide, these benefits include up to 92% lower electricity costs for a server’s cooling infrastructure, and up to 51% lower electricity costs for an entire data center.

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The previous thinking was that liquid cooling was only for supercomputers and high-end gaming PCs. No more.

Today, many large-scale cloud, HPC, analytics and AI servers combine CPUs and GPUs in a single enclosure, generating a lot of heat. Liquid cooling can carry away the heat that’s generated, often with less overall cost and more efficiently than air.

According to a new Supermicro solution guide, liquid’s advantages over air cooling include:

  • Up to 92% lower electricity costs for a server’s cooling infrastructure
  • Up to 51% lower electricity costs for the entire data center
  • Up to 55% less data center server noise

What’s more, the latest liquid cooling systems are turnkey solutions that support the highest GPU and CPU densities. They’re also fully validated and tested by Supermicro under demanding workloads that stress the server. And unlike some other components, they’re ready to ship to you and your customers quickly, often in mere weeks.

What are the liquid-cooling components?

Liquid cooling starts with a cooling distribution unit (CDU). It incorporates two modules: a pump that circulates the liquid coolant, and a power supply.

Liquid coolant travels from the CDU through flexible hoses to the cooling system’s next major component, the coolant distribution manifold (CDM). It’s a unit with distribution hoses to each of the servers.

There are 2 types of CDMs. A vertical manifold is placed on the rear of the rack, is directly connected via hoses to the CDU, and delivers coolant to another important component, the cold plates. The second type, a horizontal manifold, is placed on the front of the rack, between two servers; it’s used with systems that have inlet hoses on the front.

The cold plates, mentioned above, are placed on top of the CPUs and GPUs in place of their typical heat sinks. With coolant flowing through their channels, they keep these components cool.

Two valuable CDU features are offered by Supermicro. First, the company’s CDU has a cooling capacity of 100kW, which enables very high rack compute densities. Second, Supermicro’s CDU features a touchscreen for monitoring and controlling the rack operation via a web interface. It’s also integrated with the company’s Super Cloud Composer data-center management software.

What does it work on?

Supermicro offers several liquid-cooling configurations to support different numbers of servers in different size racks.

Among the Supermicro servers available for liquid cooling is the company’s GPU systems, which can combine up to eight Nvidia GPUs and AMD EPYC 9004 series CPUs. Direct-to-chip (D2C) coolers are mounted on each processor, then routed through the manifolds to the CDU. 

D2C cooling is also a feature of the Supermicro SuperBlade. This system supports up to 20 blade servers, which can be powered by the latest AMD EPYC CPUs in an 8U chassis. In addition, the Supermicro Liquid Cooling solution is ideal for high-end AI servers such as the company’s 8-GPU 8125GS-TNHR.

To manage it all, Supermicro also offers its SuperCloud Composer’s Liquid Cooling Consult Module (LCCM). This tool collects information on the physical assets and sensor data from the CDU, including pressure, humidity, and pump and valve status.

This data is presented in real time, enabling users to monitor the operating efficiency of their liquid-cooled racks. Users can also employ SuperCloud Composer to set up alerts, manage firmware updates, and more.

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Interview: How NEC Germany keeps up with the changing HPC market

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Interview: How NEC Germany keeps up with the changing HPC market

In an interview, Oliver Tennert, director of HPC marketing and post-sales at NEC Germany, explains how the company keeps pace with a fast-developing market.

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  • NEC Germany

The market for high performance computing (HPC) is changing, meaning system integrators that serve HPC customers need to change too.

To learn more, PIC managing editor Peter Krass spoke recently with Oliver Tennert, NEC Germany’s director of HPC marketing and post-sales. NEC Germany works with hardware vendors that include AMD processors and Supermicro servers. This interview has been lightly edited for clarity.

First, please tell me about NEC Germany and its relationship with parent company NEC Corp.?

I work for NEC Germany, which is a subsidary of NEC Europe. Our parent company, NEC Corp., is a Japanese company with a focus on telecommunications, which is still a major part of our business. Today NEC has about 100,000 employees around the world.

HPC as a business within NEC is done primarily by NEC Germany and our counterparts at NEC Corp. in Japan. The Japanese operation covers HPC in Asia, and we cover EMEA, mainly Europe.

What kinds of HPC workloads and applications do your customers run?

It’s probably 60:40 — that is, about 60% of our customers are in academia, including universities, research facilities, and even DWD, Germany’s weather-forecasting service. The remaining 40% are industrial, including automotive and engineering companies. 

The typical HPC use cases of our customers come in two categories. The most important HPC category of course is simulation. That can mean simulating physical processes. For example, what does a car crash look like under certain parameters? These simulations are done in great detail.

Our other important HPC category is data analytics. For example, that could mean genomic analysis.

How do you work with AMD and Supermicro?

To understand this, you first have to understand how NEC’s HPC business works. For us, there are two aspects to the business.

One, we’ve got our own vector technology. Our NEC vector engine is a PCIe card designed and produced in Japan. The latest incarnation of our vector supercomputer is the NEC SX-Aurora TSUBASA. It was designed to run applications that are both vectorizable and profit from high bandwidth to main memory. One of our big customers in this area is the German weather service, DWD.

The other part of the business is what we call “pizza boxes,” the x86 architecture. For this, we need industry-standard servers, including processors from AMD and servers from Supermicro.

For that second part of the business, what is NEC’s role?

The answer has to do with how the HPC business works operationally. If a customer intends to purchase a new HPC cluster, typically they need expert advice on designing an optimized HPC environment. What they do know is the application they run. And what they want to know is, ‘How do we get the best, most optimized system for this application?’

This implies doing a lot of configuration. Essentially, we optimize the design based on many different components. Even if we know that an AMD processor is the best for a particular task, still, there are dozens of combinations of processor SKUs and server model types which offer different price/performance ratios. The same applies to certain data-storage solutions. For HPC, storage is more than just picking an SSD. What’s needed is a completely different kind of technology.

Configuring and setting up such a complex solution takes a lot of expertise. We’re being asked to run benchmarks. That means the customer says, ‘Here’s my application, please run it on some specific configurations, and tell me which one offers the best price/performance ratio.’ This takes a lot of time and resources. For example, you need the systems on hand to just try it out. And the complete tender process—from pre-sales discussions to actual ordering and delivery—can take anywhere from weeks to months.

And this is just to bid, right? After all this work, you still might not get the order?

Yes, that can happen. There are lots of factors that influence your chances. In general, if you have a good working relationship with a private customer, it’s easier. They have more discretion than academic or public customers. For public bids, everything must be more transparent, because it’s more strictly regulated. Normally, that means you have more work, because you have to test more setups. Your competition will be doing the same.

When working with the second group, the private industry customers, do customer specify parts from specific vendors, such as AMD and Supermicro?

It depends on the factors that will influence the customer’s final selection. Price and performance, that’s one thing. Power consumption is another. Then, sometimes, it’s the vendors. Also, certain projects are more attractive to certain vendors because of market visibility—so-called lighthouse projects. That can have an influence on the conditions we get from vendors. Vendors also honor the amount of effort we have put in to getting the customer in the first place. So there are all sorts of external factors that can influence the final system design.

Also, today, the majority of HPC solutions are similar from an architectural point of view. So the difference between competing vendors is to take all the standard components and optimize from these, instead of providing a competing architecture. As a result, the soft skills—such as the ability to implement HPC solutions in an efficient and professional way—also have a large influence on the final order.

How about power consumption and cooling? Are these important considerations for your HPC customers?

It’s become absolutely vital. As a rule of thumb, we can say that the larger an HPC project is going to be, the more likely that it is going to be cooled by liquid.

In the past, you had a server room that you cooled with air conditioning. But those times are nearly gone. Today, when you think of a larger HPC installation—say, 1,000 or 2,000 nodes—you’re talking about a megawatt of power being consumed, or even more. And that also needs to be cooled.

The challenge in cooling a large environment is to get the heat away from the server and out of the room to somewhere else, whether outside or to a larger cooling system. This cannot be done by traditional cooling with air. Air is too inefficient for transporting heat. Water is much better. It’s a more efficient means for moving heat from Point A to Point B.

How are you cooling HPC systems with liquid?

There are a few ways to do this. There’s cold-water cooling, mainly indirect. You bring in water with what’s known as an “inlet temperature” of about 10 C and it cools down the air inside the server racks, with the heat getting carried away with the water now at about 15 or 20 C. The issue is, first you need energy just to cool the water down to 10 C. Also, there’s not much you can do with water at 15 or 20 C. It’s too warm for cooling anything else, but too cool for heating a room.

That’s why the new approach is to use hot-water cooling, mainly direct. It sounds like a paradox. But what might seem hot to a human being is in fact pretty cool for a CPU. For a CPU, an ambient temperature of 50 or 60 C is fine; it would be absolutely not fine for a human being. So if you have an inlet temperature for water of, say, 40 or 45 C, that will cool the CPU, which runs at an internal temperature of 80 or 90 C. The outbound temperature of the water is then maybe 50 C. Then it becomes interesting. At that temperature, you can heat a building. You can reuse the heat, rather than just throwing it away. So this kind of infrastructure is becoming more important and more interesting.

Looking ahead, what are some of your top projects for the future?

Public customers such as research universities have to replace their HPC systems every three to five years. That’s the normal cycle. In that time the hardware becomes obsolete, especially as the vendors optimize their power consumption to performance ratio more and more. So it’s a steady flow of new projects. For our industrial customers, the same applies, though the procurement cycle may vary.

We’re also starting to see the use of computational HPC capacity from the cloud. Normally, when people think of cloud, they think of public clouds from Amazon, Microsoft, etc. But for HPC, there are interim approaches as well. A decade ago, there was the idea of a dedicated public cloud. Essentially, this meant a dedicated capacity that was for the customer’s exclusive use, but was owned by someone other than the customer. Now, between the dedicated cloud and public cloud, there are all these shades of grey. In the past two years, we’ve implemented several larger installations of this “grey-shaded” cloud approach. So more and more, we’re entering the service-oriented market.

There is a larger trend away from customers wanting to own a system, and toward customers just wanting to utilize capacity. For vendors with expertise in HPC, they have to change as well. Which means a change in the business and the way they have to work with customers. It boils down to, Who owns the hardware? And what does the customer buy, hardware or just services? That doesn’t make you a public-cloud provider. It just means you take over responsibility for this particular customer environment. You have a different business model, contract type, and set of responsibilities.

 

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Interview: How German system integrator SVA serves high performance computing with AMD and Supermicro

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Interview: How German system integrator SVA serves high performance computing with AMD and Supermicro

In an interview, Bernhard Homoelle, head of the HPC competence center at German system integrator SVA, explains how his company serves customers with help from AMD and Supermicro. 

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  • SVA System Vertrieb Alexander GmbH

SVA System Vertrieb Alexander GmbH, better known as SVA, is among the leading IT system integrators of Germany. Headquartered in Wiesbaden, the company employs more than 2,700 people in 27 branch offices. SVA’s customers include organizations in automotive, financial services and healthcare.

To learn more about how SVA works jointly with Supermicro and AMD on advanced technologies, PIC managing editor Peter Krass spoke recently with Bernhard Homoelle, head of SVA’s high performance computing (HPC) competence center (pictured above). Their interview has been lightly edited.

For readers outside of Germany, please tell us about SVA?

First of all, SVA is an owner-operated system integrator. We offer high-quality products, we sell infrastructure, we support certain types of implementations, and we offer operational support to help our customers achieve optimum solutions.

We work with partners to figure out what might be the best solution for our customers, rather than just picking one vendor and trying to convince the customer they should use them. Instead, we figure out what is really needed. Then we go in the direction where the customer can really have their requirements met. The result is a good relationship with the customer, even after a particular deal has been closed.

Does SVA focus on specific industries?

While we do support almost all the big industries—automotive, transportation, public sector, healthcare and more—we are not restricted to any specific vertical. Our main business is helping customers solve their daily IT problems, deal with the complexity of new IT systems, and implement new things like AI and even quantum computing. So we’re open to new solutions. We also offer training with some of our partners.

Germany has a robust auto industry. How do you work with these clients?

In general, they need huge HPC clusters and machine learning. For example, autonomous driving demands not only more computing power, but also more storage. We’re talking about petabytes of data, rather than terabytes. And this huge amount of data needs to be stored somewhere and finally processed. That puts pressure on the infrastructure—not just on storage, but also on the network infrastructure as well as on the compute side. For their way into cloud, some these customers are saying, “Okay, offer me HPC as a Service.”

How do you work with AMD and Supermicro?

It’s a really good relationship. We like working with them because Supermicro has all these various types of servers for individual needs. Customers are different, and therefore they have their own requirements. Figuring out what might be the best server for them is difficult if you have limited types of servers available. But with Supermicro, you can get what you have in mind. You don’t have to look for special implementations because they have these already at hand.

We’re also partnering with AMD, and we have access to their benchmark labs, so we can get very helpful information. We start with discussions with the customer to figure out their needs. Typically, we pick up an application from the customer and then use it as a kind of benchmark. Next, we put it on a cluster with different memory, different CPUs, and look for the best solution in terms of performance for their particular application. Based on the findings, we can recommend a specific CPU, number of cores, memory type and size, and more.

With HPC applications, core memory bandwidth is almost as important as the number of cores. AMD’s new Genoa-X processors should help to overcome some of these limitations. And looking ahead, I’m keen to see what AMD will offer with the Instinct MI300.

Are there special customer challenges you’re solving with Supermicro and AMD solutions?

With HPC workloads, our academic customers say, “This is the amount of money available, so how many servers can you really give us for this budget?” Supermicro and AMD really help here with reasonable prices. They’re a good choice for price/performance.

With AI and machine learning, the real issue is software tools. It really depends what kinds of models you can use and how easy it is to use the hardware with those models.

This discussion is not easy, because for many of our customers today, AI means Nvidia. But I really recommend alternatives, and AMD is bringing some alternatives that are great. They offer a fast time to solution, but they also need to be easy to switch to.

How about "green" computing? Is this an important issue for your customers now?

Yes, more and more we’re seeing customers ask for this green computing approach. Typically, a customer has a thermal budget and a power-price budget. They may say, “In five years, the expenses paid for power should not exceed a certain limit.”

In Europe, we also have a supply-chain discussion. Vendors must increasingly provide proof that they’re taking care in their supply chain with issues including child labor and working conditions. This is almost mandatory, especially in government calls. If you’re unable to answer these questions, you’re out of the bid.

With green computing, we see that the power needed for CPUs and GPUs is going up and up. Five years ago, the maximum a CPU could burn was 200W, but now even 400W might not be enough. Some GPUs are as high as 700W, and there are super-chips beyond even that.

All this makes it difficult to use air-cooled systems. Customers can use air conditioning to a certain extent, but there’s only so much air you can press through the rack. Then you need either on-chip water cooling or some kind of immersion cooling. This can help in two dimensions: saving energy and getting density — you can put the components closer together, and you don’t need the big heat sink anymore.

One issue now is that each vendor offers a different cooling infrastructure. Some of our customers run multi-vendor data centers, so this could create a compatibility issue. That’s one reason we’re looking into immersion cooling. We think we could do some of our first customer implementations in 2024.

Looking ahead, what do you see as a big challenge?

One area is that we want to help customers get easier access to their HPC clusters. That’s done on the software side.

In contrast to classic HPC users, machine learning and AI engineers are not that interested in Linux stuff, compiler options or any other infrastructure details. Instead, they’d like to work on their frameworks. The challenge is getting them to their work as easily as possible—so that they can just log in, and they’re in their development environment. That way, they won’t have to care about what sort of operating system is underneath or what kind of scheduler, etc., is running.

 

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How AMD and Supermicro are working together to help you deliver AI

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How AMD and Supermicro are working together to help you deliver AI

AMD and Supermicro are jointly offering high-performance AI alternatives with superior price and performance.

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When it comes to building AI systems for your customers, a certain GPU provider with a trillion-dollar valuation isn’t the only game in town. You should also consider the dynamic duo of AMD and Supermicro, which are jointly offering high-performance AI alternatives with superior price and performance.

Supermicro’s Universal GPU systems are designed specifically for large-scale AI and high-performance computing (HPC) applications. Some of these modular designs come equipped with AMD’s Instinct MI250 Accelerator and have the option of being powered by dual AMD EPYC processors.

AMD, with a newly formed AI group led by Victor Peng, is working hard to enable AI across many environments. The company has developed an open software stack for AI, and it has also expanded its partnerships with AI software and framework suppliers that now include the PyTorch Foundation and Hugging Face.

AI accelerators

In addition, AMD’s Instinct MI300A data-center accelerator is due to ship in this year’s fourth quarter. It’s the successor to AMD’s MI200 series, based on the company’s CDNA 2 architecture and first multi-die CPU, which powers some of today’s fastest supercomputers.

The forthcoming Instinct MI300A is based on AMD’s CDNA 3 architecture for AI and HPC workloads, which uses 5nm and 6nm process tech and advanced chiplet packaging. Under the MI300A’s hood, you’ll find 24 processor cores with Zen 4 tech, as well as 128GB of HBM3 memory that’s shared by the CPU and GPU. And it supports AMD ROCm 5, a production-ready, open source HPC and AI software stack.

Earlier this month, AMD introduced another member of the series, the AMD Instinct MI300X. It replaces three Zen 4 CPU chiplets with two CDNA 3 chiplets to create a GPU-only system. Announced at AMD’s recent Data Center and AI Technology Premier event, the MI300X is optimized for large language models (LLMs) and other forms of AI.

To accommodate the demanding memory needs of generative AI workloads, the new AMD Instinct MI300X also adds 64GB of HBM3 memory, for a new total of 192GB. This means the system can run large models directly in memory, reducing the number of GPUs needed, speeding performance, and reducing the user’s total cost of ownership (TCO).

AMD also recently introduced the AMD Instinct Platform, which puts eight MI300X systems and 1.5TB of memory in a standard Open Compute Project (OCP) infrastructure. It’s designed to drop into an end user’s current IT infrastructure with only minimal changes.

All this is coming soon. The AMD MI300A started sampling with select customers earlier this quarter. The MI300X and Instinct Platform are both set to begin sampling in the third quarter. Production of the hardware products is expected to ramp in the fourth quarter.

KT’s cloud

All that may sound good in theory, but how does the AMD + Supermicro combination work in the real world of AI?

Just ask KT Cloud, a South Korea-based provider of cloud services that include infrastructure, platform and software as a service (IaaS, PaaS, SaaS). With the rise of customer interest in AI, KT Cloud set out to develop new XaaS customer offerings around AI, while also developing its own in-house AI models.

However, as KT embarked on this AI journey, the company quickly encountered three major challenges:

  • The high cost of AI GPU accelerators: KT Cloud would need hundreds of thousands of new GPU servers.
  • Inefficient use of GPU resources in the cloud: Few cloud providers offer GPU virtualization due to overhead. As a result, most cloud-based GPUs are visible to only 1 virtual machine, meaning they cannot be shared by multiple users.
  • Difficulty using large GPU clusters: KT is training Korean-language models using literally billions of parameters, requiring more than 1,000 GPUs. But this is complex: Users would need to manually apply parallelization strategies and optimizations techniques.

The solution: KT worked with Moreh Inc., a South Korean developer of AI software, and AMD to design a novel platform architecture powered by AMD’s Instinct MI250 Accelerators and Moreh’s software.

The entire AI software stack was developed by Moreh from PyTorch and TensorFlow APIs to GPU-accelerated primitive operations. This overcomes the limitations of cloud services and large AI model training.

Users do not need to insert or modify even a single line of existing source code for the MoAI platform. They also do not need to change the method of running a PyTorch/TensorFlow program.

Did it work?

In a word, yes. To test the setup, KT developed a Korean language model with 11 billion parameters. Training was then done on two machines: one using Nvidia GPUs, the other being the AMD/Moreh cluster equipped with AMD Instinct MI250 accelerators, Supermicro Universal GPU systems, and the Moreh AI platform software.

Compared with the Nvidia system, the Moreh solution with AMD Instinct accelerators showed 116% throughput (as measured by tokens trained per second), and 2.05x higher cost-effectiveness (measured as throughput per dollar).

Other gains are expected, too. “With cost-effective AMD Instinct accelerators and a pay-as-you-go pricing model, KT Cloud expects to be able to reduce the effective price of its GPU cloud service by 70%,” says JooSung Kim, VP of KT Cloud.

Based on this test, KT built a larger AMD/Moreh cluster of 300 nodes—with a total of 1,200 AMD MI250 GPUs—to train the next version of the Korean language model with 200 billion parameters.

It delivers a theoretical peak performance of 434.5 petaflops for fp16/bf16 (a native 16-bit format for mixed-precision training) matrix operations. That should make it one of the top-tier GPU supercomputers in the world.

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Tech Explainer: Green Computing, Part 1 - What does the data center demand?

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Tech Explainer: Green Computing, Part 1 - What does the data center demand?

The ultimate goal of Green Computing is net-zero emissions. To get there, organizations can and must innovate, conducting an ongoing campaign to increase efficiency and reduce waste.

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The Green Computing movement has begun in earnest and not a moment too soon. As humanity faces the existential threat of climate crisis, technology needs to be part of the solution. Green computing is a big step in the right direction.

The ultimate goal of Green Computing is net-zero emissions. It’s a symbiotic relationship between technology and nature in which both SMBs and enterprises can offset carbon emissions, drastically reduce pollution, and reuse/recycle the materials that make up their products and services.

To get there, the tech industry will need to first take a long, hard look at the energy it uses and the waste it produces. Using that information, individual organizations can and must innovate, conducting an ongoing campaign to increase efficiency and reduce waste.

It’s a lofty goal, sure. But after all the self-inflicted damage we’ve done since the dawn of the Industrial Revolution, we simply have no choice.

The data-center conundrum

All digital technology requires electricity to operate. But data centers use more than their share.

Here’s a startling fact: Each year, the world’s data centers gobble up at least 200 terawatts of energy. That’s roughly 2% of all the electricity used on this planet annually.

What’s more, that figure is likely to increase as new, power-hungry systems are brought online and new data centers are opened. And the number of global data centers could grow from 700 in 2021 to as many as 1,200 by 2026, predicts Supermicro.

At that rate, data-center energy consumption could account for up to 8% of global energy usage by 2030. That’s why tech leaders including AMD and Supermicro are rewriting the book on green computing best practices.

A Supermicro white paper, Green Computing: Top 10 Best Practices For A Green Data Center, suggests specific actions you and your customers can take now to reduce the environmental impact of your data centers:

  • Right-size systems to match workload requirements
  • Share common scalable infrastructure
  • Operate at higher ambient temperature
  • Capture heat at the source via aisle containment and liquid cooling
  • Optimize key components (i.e., CPU, GPU, SSD, etc.) for workload performance per watt
  • Optimize hardware refresh cycle to maintain efficiency
  • Optimize power delivery
  • Utilize virtualization and power management
  • Source renewable energy and green manufacturing
  • Consider climate impact when making site selection

Green components

Rethinking data-center architectures is an excellent way to leverage green computing from a macro perspective. But to truly make a difference, the industry needs to consider green computing at the component level.

This is one area where AMD is leading the charge. Its mission: increase the energy efficiency of its CPUs and hardware accelerators. The rest of the industry should follow suit.

In 2021 AMD announced its goal to deliver a 30x increase in energy efficiency for both AMD EPYC CPUs and AMD Instinct accelerators for AI and HPC applications running on accelerated compute nodes—and to do so by 2025.

Taming AI energy usage

The golden age of AI has begun. New machine learning algorithms will give life to a population of hyper-intelligent robots that will forever alter the nature of humanity. If AI’s most beneficent promises come to fruition, it could help us live, eat, travel, learn and heal far better than ever before.

But the news isn’t all good. AI has a dark side, too. Part of that dark side is its potential impact on our climate crisis.

Researchers at the University of Massachusetts, Amherst, illustrated this point by performing a life-cycle assessment for training several large AI models. Their findings, published by Supermicro, concluded that training a single AI model can emit more than 626,000 pounds of carbon dioxide. That’s approximately 5 times the lifetime emissions of your average American car.

A comparison like that helps put AMD’s environmental sustainability goals in perspective. Affecting a 30x energy efficiency increase in the components that power AI could bring some much-needed light to AI’s dark side.

In fact, if the whole technology sector produces practical innovations similar to those from AMD and Supermicro, we might have a fighting chance in the battle against climate crisis.

Continued…

Part 2 of this 3-part series will take a closer look at the technology behind green computing—and the world-saving innovations we could see soon.

 

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