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Tech Explainer: Green Computing, Part 3 – Why you should reduce, reuse & recycle

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Tech Explainer: Green Computing, Part 3 – Why you should reduce, reuse & recycle

The new 3Rs of green computing are reduce, reuse and recycle. 

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To help your customers meet their environmental, social and governance (ESG) goals, it pays to focus on the 3 Rs of green computing—reduce, reuse and recycle.

Sure, pursuing these goals can require some additional R&D and reorganization. But tech titans such as AMD and Supermicro are helping.

AMD, Supermicro and their vast supply chains are working to create a new virtuous circle. More efficient tech is being created using recycled materials, reused where possible, and then once again turned into recycled material.

For you and your customers, the path to green computing can lead to better corporate citizenship as well as higher efficiencies and lower costs.

Green server design

New disaggregated server technology is now available from manufacturers like Supermicro. This tech makes it possible for organizations of every size to increase their energy efficiency, better utilize data-center space, and reduce capital expenditures.

Supermicro’s SuperBlade, BigTwin and EDSFF SuperStorage are exemplars of disaggregated server design. The SuperBlade multi-node server, for instance, can house up to 20 server blades and 40 CPUs. And it’s available in 4U, 6U and 8U rack enclosures.

These efficient designs allow for larger, more efficient shared fans and power supplies. And along with the chassis itself, many elements can remain in service long past the lifespans of the silicon components they facilitate. In some cases, an updated server blade can be used in an existing chassis.

Remote reprogramming

Innovative technologies like adaptive computing enable organizations to adopt a holistic approach to green computing at the core, the edge and in end-user devices.

For instance, AMD’s adaptive computing initiative offers the ability to optimize hardware based on applications. Then your customers can get continuous updates after production deployment, adapting to new requirements without needing new hardware.

The key to adaptive computing is the Field Programmable Gate Array (FPGA). It’s essentially a blank canvas of hardware, capable of being configured into a multitude of different functions. Even after an FPGA has been deployed, engineers can remotely access the component to reprogram various hardware elements.

The FPGA reprogramming process can be as simple as applying security patches and bug fixes—or as complex as a wholesale change in core functionality. Either way, the green computing bona fides of adaptive computing are the same.

What’s more, adaptive tech like FPGAs significantly reduces e-waste. This helps to lower an organization’s overall carbon footprint by obviating the manufacturing and transportation necessary to replace hardware already deployed.

Adaptive computing also enables organizations to increase energy efficiency. Deploying cutting-edge tech like the AMD Instinct MI250X Accelerator to complete AI training or inferencing can significantly reduce the overall electricity needed to complete a task.

Radical recycling

Even in organizations with the best green computing initiatives, elements of the hardware infrastructure will eventually be ready for retirement. When the time comes, these organizations have yet another opportunity to go green—by properly recycling.

Some servers can be repurposed for other, less-demanding tasks, extending their lifespan. For example, a system that had been used for HPC applications that may no longer have the required FP64 performance could be repurposed to host a database or email application.

Quite a lot of today’s computer hardware can be recycled. This includes glass from monitors; plastic and aluminum from cases; copper in power supplies; precious metals used in circuitry; even the cardboard, wood and other materials used in packaging.

If that seems like too much work, there are now third-party organizations that will oversee your customers’ recycling efforts for a fee. Later, if all goes according to plan, these recycled materials will find their way back into the manufacturing supply chain.

Tech suppliers are working to make recycling even easier. For example, AMD is one of the many tech leaders whose commitment to environmental sustainability extends across its entire value chain. For AMD, that includes using environmentally preferable packing materials, such as recycled materials and non-toxic dyes.

Are you 3R?

Your customers understand that establishing and adhering to ESG goals is more than just a good idea. In fact, it’s vital to the survival of humanity.

Efforts like those of AMD and Supermicro are helping to establish a green computing revolution—and not a moment too soon.

In other words, pursuing green computing’s 3 Rs will be well worth the effort.

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Meet Supermicro’s Petascale Storage, a compact rackmount system powered by the latest AMD EPYC processors

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Meet Supermicro’s Petascale Storage, a compact rackmount system powered by the latest AMD EPYC processors

Supermicro’s H13 Petascale Storage Systems is a compact 1U rackmount system powered by the AMD EPYC 97X4 processor (formerly codenamed Bergamo) with up to 128 cores.

 

 

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Your customers can now implement Supermicro Petascale Storage, an all-Flash NVMe storage system powered by the latest 4th gen AMD EPYC 9004 series processors.

The Supermicro system has been specifically designed for AI, HPC, private and hybrid cloud, in-memory computing and software-defined storage.

Now Supermicro is offering the first of these systems. It's the Supermicro H13 Petascale Storage System. This compact 1U rackmount system is powered by an AMD EPYC 97X4 processor (formerly codenamed Bergamo) with up to 128 cores.

For organizations with data-storage requirements approaching petascale capacity, the Supermicro system was designed with a new chassis and motherboard that support a single AMD EPYC processor, 24 DIMM slots for up to 6TB of main memory, and 16 hot-swap ES.3 slots. That's the Enterprise and Datacenter Standard Form Factor (EDSFF), part of the E3 family of SSD form factors designed for specific use cases. ES.3 is short and thin. It uses 25W and 7.5mm-wide storage media designed with a PCIe 5.0 interface.

The Supermicro Petascale Storage system can deliver more than 200 GB/sec. bandwidth and over 25 million input-output operations per second (IOPS) from a half-petabyte of storage.

Here's why 

Why might your customers need such a storage system? Several reasons, depending on what sorts of workloads they run:

  •  Training AI/ML applications requires massive amounts of data for creating reliable models.
  • HPC projects use and generate immense amounts of data, too. That's needed for real-world simulations, such as predicting the weather or simulating a car crash.
  • Big-data environments need susbstantial datasets. These gain intelligence from real-world observations ranging from sensor inputs to business transactions.
  • Enterprise applications need to locate large amounts of data close to computing over NVMe-over-Fabrics (NVMeoF) speeds.

Also, the Supermicro H13 Petascale Storage System offers significant performance, capacity, throughput and endurance--all while keeping excellent power efficiencies.

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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 2 — Holistic strategies

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Tech Explainer: Green Computing, Part 2 — Holistic strategies

Holistic green computing strategies can help both corporate and individual users make changes for the better.

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Green computing allows us to align the technology that powers our lives with the sustainability goals necessary to battle the climate crisis.

In Part 1 of our Tech Explainer on green computing, we looked at data-center architecture best practices and component-level green engineering. Now we’ll investigate holistic green computing strategies that can help both corporate and individual users change for the better.

Green manufacturing and supply chain

The manufacturing process can account for up to 70% of the natural resources used in the lifecycle of a PC, server or other digital device. And an estimated 76% of all global trade passes through a supply chain. So it’s more important than ever to reform processes that could harm the environment.

AMD’s efforts to advance environmental sustainability in partnership with its suppliers is a step in the right direction. The AMD Supply Chain is currently on track to ensure two important goals: that 80% of its suppliers source renewable energy, and that 100% make public their emissions-reduction goals, both by 2025.

To reduce the environmental impact of IT manufacturing, tech providers are replacing the toxic chemicals used in computer manufacturing with alternatives that are more environmentally friendly.

Materials such as the brominated flame retardants found in plastic casings are giving way to eco-friendly, non-toxic silicone compounds. Traditional non-recyclable plastic parts are being replaced by parts made from both bamboo and recyclable plastics, such as polycarbonate resins. And green manufacturers are working to eliminate other toxic chemicals, including lead in solder and cadmium and selenium in circuit boards.

Innovation in green manufacturing can identify and improve hundreds, if not thousands, of industry-standard practices. No matter how small an improvement is when employed to create millions of devices, it can make a big difference.

Green enterprise

Today’s enterprise data-center managers are working to maximize server performance while also minimizing their environmental impact. Leading-edge green methodologies include two important moves: reducing power usage at the server level and extending hardware lifecycles to create less waste.

Supermicro, an authority on energy-efficient data center design, is empowering this movement by creating new servers engineered for green computing.

One such server is Supermicro’s 4-node BigTwin. The BigTwin features disaggregated server architecture that reduces e-waste by enabling subsystem upgrades.

As technology improves, IT managers can replace components like the CPU, GPU and memory. This extends the life of the chassis, power supplies and cooling systems that might otherwise end up in a landfill.

Twin and Blade server architectures are more efficient because they share power supplies and fans. This can significantly lower their power usage, making them a better choice for green data centers.

The upgraded components that go into these servers now include high-efficiency processors like the AMD EPYC 9654. The infographic below, courtesy of AMD, shows how 4th Gen AMD EPYC processors can power 2,000 virtual machines using up to 35% fewer servers than the competition:

EPYC green infographic

As shown, the potential result is up to 29% less energy consumed annually. That kind of efficiency can save an estimated 35 tons of carbon dioxide—the equivalent of 38 acres of U.S. forest carbon sequestration every year.

Green data centers also employ advanced cooling systems. For instance, Supermicro’s servers include optional liquid cooling. Using fluid to carry heat away from critical components allows IT managers to lower fan speeds inside each server and reduce HVAC usage in data centers.

Deploying efficient cooling systems like these lowers a data center’s Power Usage Effectiveness (PUE), thus reducing carbon emissions from power generation.

Changing for the better, together

No single person, corporation or government can stave off the worst effects of climate crisis. If we are to win this battle, we must work together.

Engineers, industrial designers and data scientists have their work cut out for them. By fueling the evolution of green computing, they—and their corporate managers—can provide us with the tools we need to go green and safeguard our environment for generations to come.

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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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Bergamo: a deeper dive into AMD’s new EPYC processor for cloud-native workloads

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Bergamo: a deeper dive into AMD’s new EPYC processor for cloud-native workloads

Bergamo is AMD’s first-ever server processor designed specifically for cloud-native workloads. Learn how it works.  

 

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Bergamo is the former codename for AMD’s new 4th gen EPYC 97X4 processors optimized for cloud-native workloads, which the company introduced earlier this month.

AMD is responding to the increasingly specialized nature of data center workloads by optimizing its server processors for specific workloads. This month AMD introduced two examples: Bergamo (97X4) for cloud and Genoa-X (9XX4X) for technical computing.

The AMD EPYC 97X4 processors are AMD’s first-ever designed specifically for cloud-native workloads. And they’re shipping now in volume to AMD’s hyperscale customers that include Facebook parent company Meta and partners including Supermicro.

Speaking of Supermicro, that company this week announced that the new AMD EPYC 97X4 processors can now be included in its entire line of Supermicro H13 AMD-based systems.

Zen mastery

The main difference between the AMD EPYC 97X4 and AMD’s general-purpose Genoa series processors comes down to the core chiplet. The 97X4 CPUs use a new design called “Zen 4c.” It’s an update on the AMD Zen 4 core used in the company’s Genoa processors.

Where AMD’s original Zen 4 was designed for the highest performance per core, the new Zen 4c has been designed for a sweet spot of both density and power efficiency.

As AMD CEO Lisa Su explained during the company’s recent Data Center and AI Technology Premier event, AMD achieved this by starting with the same RTL design as Zen 4. AMD engineers then optimized this physical layout for power and area. They also redesigned the L3 cache hierarchy for greater throughput.

The result: a design that takes up about 35% less area yet offers substantially better performance per watt.

Because the start from the Zen 4’s design, the new 97X4 processors are both software- and platform-compatible with Genoa. The idea is that end users can mix and match 97X4- and Genoa-based servers, depending on their specific workloads and computing needs.

Basic math

Another difference is that where Genoa processors offer up to 96 cores per socket, the new 97X4 processors offer up to 128.

Here’s how it’s done: Each AMD 97X4 system-on-chip (SoC) contains 8 core complex dies (CCDs). In turn, each CCD contains 16 Zen 4c cores. So 8 CCDs x 16 cores = a total of 128 cores.

As the table below shows, courtesy of AMD, there are three SKUs for the new EPYC 97X5 series processors:

For security, all 3 SKUs support AMD Infinity Guard, a suite of hardware-level security features, and AMD Infinity Architecture, which lets system builders and cloud architects get maximum power while still ensuring security.

Are your customers looking for servers to handle their cloud-native applications? Tell them to look into the new AMD EPYC 97X4 processors.

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AMD intros CPUs, cache, AI accelerators for cloud, enterprise data centers

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AMD intros CPUs, cache, AI accelerators for cloud, enterprise data centers

AMD strengthens its commitment to the cloud and enterprise data centers with new "Bergamo" CPUs, "Genoa-X" cache, Instinct accelerators.

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This week AMD strengthened its already strong commitment to the cloud and enterprise markets. The company announced several new products and partnerships at its Data Center and AI Technology Premier event, which was held in San Francisco and simultaneously broadcast online.

“We’re focused on pushing the envelope in high-performance and adaptive computing,” AMD CEO Lisa Su told the audience, “creating solutions to the world’s most important challenges.”

Here’s what’s new:

Bergamo: That’s the former codename for the new 4th gen AMD EPYC 97X4 processors. AMD’s first processor designed specifically for cloud-native workloads, it packs up to 128 cores per socket using AMD’s new Zen 4c design to deliver lots of power/watt. Each socket contains 8 chiplets, each with up to 16 Zen 4c cores; that’s twice as many cores as AMD’s earlier Genoa processors (yet the two lines are compatible). The entire lineup is available now.

Genoa-X: Another codename, this one is for AMD’s new generation of AMD 3D V-Cache technology. This new product, designed specifically for technical computing such as engineering simulation, now supports over 1GB of L3 cache on a 96-core CPU. It’s paired with the new 4th gen AMD EPYC processor, including the high-performing Zen4 core, to deliver high performance/core.

“A larger cache feeds the CPU faster with complex data sets, and enables a new dimension of processor and workload optimization,” said Dan McNamara, an AMD senior VP and GM of its server business.

In all, there are 4 new Genoa-X SKUs, ranging from 16 to 96 cores, and all socket-compatible with AMD’s Genoa processors.

Genoa: Technically, not new, as this family of data-center CPUs was introduced last November. But what is new is AMD’s new focus for the processors on AI, data-center consolidation and energy efficiency.

AMD Instinct: Though AMD had already introduced its Instinct MI300 Series accelerator family, the company is now revealing more details.

This includes the introduction of the AMD Instinct MI300X, an advanced accelerator for generative AI based on AMD’s CDNA 3 accelerator architecture. It will support up to 192GB of HBM3 memory to provide the compute and memory efficiency needed for large language model (LLM) training and inference for generative AI workloads.

AMD also introduced the AMD Instinct Platform, which brings together eight MI300X accelerators into an industry-standard design for the ultimate solution for AI inference and training. The MI300X is sampling to key customers starting in Q3.

Finally, AMD also announced that the AMD Instinct MI300A, an APU accelerator for HPC and AI workloads, is now sampling to customers.

Partner news: Mark your calendar for June 20. That’s when Supermicro plans to explore key features and use cases for its Supermicro 13 systems based on AMD EPYC 9004 series processors. These Supermicro systems will feature AMD’s new Zen 4c architecture and 3D V-Cache tech.

This week Supermicro announced that its entire line of H13 AMD-based systems are now available with support for the 4th gen AMD EPYC processors with Zen 4c architecture and V-Cache technology.

That includes Supermicro’s new 1U and 2U Hyper-U servers designed for cloud-native workloads. Both are equipped with a single AMD EPYC processor with up to 128 cores.

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Absolute Hosting finds the sweet spot with AMD-powered Supermicro servers

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Absolute Hosting finds the sweet spot with AMD-powered Supermicro servers

Absolute Hosting, a South African provider of hosting services to small and midsize businesses, sought to upgrade its hardware, improve its performance, and lower its costs. The company achieved all three goals with AMD-powered Supermicro servers.

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Some brands are so strong, customers ask for them by name. They ask for a Coke when thirsty, click on Amazon.com when shopping online, visit a Tesla showroom when thinking of buying an electric car.

For Absolute Hosting Ltd., a South Africa-based provider of hosting and other digital services for small and midsize businesses (SMBs), it’s not one brand, but two: Supermicro and AMD. More specifically, the combination of Supermicro servers powered by AMD EPYC processors.

“Clients who have switched over to us have been amazed by the performance of our AMD EPYC-powered servers,” says Jade Benson, the founder of Absolute Hosting and now its managing director.

Benson and his colleagues find the Supermicro-AMD brand so powerful, they offer it by name. Check out Absolute Hosting's website, and you’ll see the AMD and Supermicro brands called out by name.

SMB specialists

It wasn’t always the case. Back in 2011, when Benson founded Absolute Hosting, the company served local South African tech resellers. Five years later, in 2016, the company shifted its focus to offering hosting and virtual server services to local SMBs.

One of its hosting services is virtual private servers. VPS hosting provides dedicated resources to each customer’s website, allowing for more control, customization and scalability than they’d get with shared hosting. That makes VPS hosting ideal for businesses that require lots of resources, enjoy high traffic, or need a great deal of control over their hosting environment.

Today Absolute Hosting owns about 100 physical servers and manages roughly 300 VPS servers for clients. The company also supplies its 5,000 clients with other hosting services, including Linux web, WordPress and email.

‘We kept seeing AMD’

Absolute Hosting’s shift to AMD-powered Supermicro servers was driven by its own efforts to refresh and upgrade its hardware, improve its performance and lower its own costs. Initially, the company rented dedicated servers from a provider that relied exclusively on Supermicro hardware.

“So when we decided to purchase our own hardware, we made it a requirement to use Supermicro,” Benson says. “And we kept seeing AMD as the recommended option.”

The new servers were a quick success. Absolute Hosting tested them with key benchmarks, including Cinebench, a cross-platform test suite, and Passmark, which compares the performance of CPUs. And it found them leading for every test application.

Absolute Hosting advertised the new offering on social media and quickly had enough business for 100 VPS servers. The company ran a public beta for customers and allowed the local IT community to conduct their own stress tests.

“The feedback we received was phenomenal,” Benson says. “Everyone was blown away.”

Packing a punch

Absolute Hosting’s solution is based on Supermicro’s AS-2115GT-HNTF GrandTwin server. It packs four hot-pluggable nodes into a 2U rackmount form factor.

Each node includes an AMD EPYC CPU; 12 memory slots for up to 3TB of DDR5 memory; flexible bays for storage or I/O; and up to four hot-swap 2.5-inch NVMe/SATA storage drives.

Absolute Hosting currently uses the AMD EPYC 7003 Series processors. But the Supermicro server now supports the 4th gen AMD EPYC 9004 Series processors, and Benson plans to move to them soon.

Benson considers the AMD-powered Supermicro servers a serious competitive advantage. “There are only a few people we don’t tell about AMD,” he says. “That’s our competitors.”

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Research roundup: AI edition

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Research roundup: AI edition

AI is busting out all over. AI is getting prioritized over all other digital investments. The AI market is forecast to grow by over 20% a year through 2030. AI worries Americans about the potential impact on hiring. And AI needs to be safeguarded against the risk of misuse.

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AI is busting out all over. AI is getting prioritized over all other digital investments. The AI market is forecast to grow by over 20% a year through 2030. AI worries Americans about the potential impact on hiring. And AI needs to be safeguarded against the risk of misuse.

That’s some of the latest AI research from leading market watchers. And here’s your research roundup.

The AI priority

Nearly three-quarters (73%) of companies are prioritizing AI over all other digital investments, finds a new report from consultants Accenture. For these AI projects, the No. 1 focus area is improving operational resilience; it was cited by 90% of respondents.

Respondents to the Accenture survey also say the business benefits of AI are real. While only 9% of companies have achieved maturity across all 6 areas of AI operations, they averaged 1.4x higher operating margins than others. (Those 6 areas, by the way, are AI, data, processes, talent, collaboration and stakeholder experiences.)

Compared with less-mature AI operations, these companies also drove 42% faster innovation, 34% better sustainability and 30% higher satisfaction scores.

Accenture’s report is based on its recent survey of 1,700 executives in 12 countries and 15 industries. About 7 in 10 respondents held C-suite-level job titles.

The AI market

It’s no surprise that the AI market is big and growing rapidly. But just how big and how rapidly might surprise you.

How big? The global market for all AI products and services, worth some $428 billion last year, is on track to top $515 billion this year, predicts market watcher Fortune Business Insights.

How fast? Looking ahead to 2030, Fortune Insights expects the global AI market that year to hit $2.03 trillion. If so, that would mark a compound annual growth rate (CAGR) of nearly 22%.

What’s driving this big, rapid growth? Several factors, says Fortune, including the surge in the number of applications, increased partnering and collaboration, a rise in small-scale providers, and demand for hyper-personalized services.

The AI impact

What, me worry? About six in 10 Americans (62%) believe AI will have a major impact on workers in general. But only 28% believe AI will have a major effect on them personally.

So finds a recent poll by Pew Research of more than 11,000 U.S. adults.

Digging a bit deeper, Pew found that nearly a third of respondents (32%) believe AI will hurt workers more than help; the same percentage believe AI will equally help and hurt; about 1 in 10 respondents (13%) believe AI will help more than hurt; and roughly 1 in 5 of those answering (22%) aren’t sure.

Respondents also widely oppose the use of AI to augment regular management duties. Nearly three-quarters of Pew’s respondents (71%) oppose the use of AI for making a final hiring decision. Six in 10 (61%) oppose the use of AI for tracking workers’ movements while they work. And nearly as many (56%) oppose the use of AI for monitoring workers at their desks.

Facial-recognition technology fared poorly in the survey, too. Fully 7 in 10 respondents were opposed to using the technology to analyze employees’ facial expressions. And over half (52%) were opposed to using facial recognition to track how often workers take breaks. However, a small majority (45%) favored the use of facial recognition to track worker attendance; about a third (35%) were opposed and one in five (20%) were unsure.

The AI risk

Probably the hottest form of AI right now is generative AI, as exemplified by the ChatGPT chatbot. But given the technology’s risks around security, privacy, bias and misinformation, some experts have called for a pause or even a halt on its use.

Because that’s unlikely to happen, one industry watcher is calling for new safeguards. “Organizations need to act now to formulate an enterprisewide strategy for AI trust, risk and security management,” says Avivah Litan, a VP and analyst at Gartner.

What should you do? Two main things, Litan says.

First, monitor out-of-the-box usage of ChatGPT. Use your existing security controls and dashboards to catch policy violations. Also, use your firewalls to block unauthorized use, your event-management systems to monitor logs for violations, and your secure web gateways to monitor disallowed API calls.

Second, for prompt engineering usage—which uses tools to create, tune and evaluate prompt inputs and outputs—take steps to protect the sensitive data used to engineer prompts. A good start, Litan says, would be to store all engineered prompts as immutable assets.

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