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Data-center service providers: ready for transformation?

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Data-center service providers: ready for transformation?

An IDC researcher argues that providers of data-center hosting services face new customer demands that require them to create new infrastructure stacks. Key elements will include rack-scale integration, accelerators and new CPU cores. 

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If your organization provides data-center hosting services, brace yourself. Due to changing customer demands, you’re about to need an entirely new infrastructure stack.

So argues Chris Drake, a senior research director at market watcher IDC, in a recently published white paper sponsored by Supermicro and AMD, The Power of Now: Accelerate the Datacenter.

In his white paper, Drake asserts that this new data center infrastructure stack will include new CPU cores, accelerated computing, rack-scale integration, a software-defined architecture, and the use of a micro-services application environment.

Key drivers

That’s a challenging list. So what’s driving the need for this new infrastructure stack? According to Drake, changing customer requirements.

More specifically, a growing need for hosted IT requirements. For reasons related to cost, security and performance, many IT shops are choosing to retain proprietary workloads on premises and in private-cloud environments.

While some of these IT customers have sufficient capacity in their data centers to host these workloads on prem, many don’t. They’ll rely instead on service providers for a range of hosted IT requirements. To meet this demand, Drake says, service providers will need to modernize.

Another driver: growing customer demand for raw compute power, a direct result of their adoption of new, advanced computing tools. These include analytics, media streaming, and of course the various flavors of artificial intelligence, including machine learning, deep learning and generative AI.

IDC predicts that spending on servers ranging in price from $10K to $250K will rise from a global total of $50.9 billion in 2022 to $97.4 billion in 2027. That would mark a 5-year compound annual growth rate of nearly 14%.

Under the hood

What will building this new infrastructure stack entail? Drake points to 5 key elements:

  • Higher-performing CPU cores: These include chiplet-based CPU architectures that enable the deployment of composable hardware architectures. Along with distributed and composable hardware architectures, these can enable more efficient use of shared resources and more scalable compute performance.
  • Accelerated computing: Core CPU processing will increasingly be supplemented by hardware accelerators, including those for AI. They’ll be needed to support today’s—and tomorrow’s—increasingly diverse range of high-performance and data-intensive workloads.
  • Rack-scale integration: Pre-tested racks can facilitate faster deployment, integration and expansion. They can also enable a converged-infrastructure approach to building and scaling a data center.
  • Software-defined data center technology: In this approach, virtualization concepts such as abstraction and pooling are extended to a data center’s compute, storage, networking and other resources. The benefits include increased efficiency, better management and more flexibility.
  • A microservices application architecture: This approach divides large applications into smaller, independently functional units. In so doing, it enables a highly modular and agile way for applications to be developed, maintained and upgraded.

Plan for change

Rome wasn’t built in a day. Modernizing a data center will take time, too.

To help service providers implement a successful modernization, Drake of IDC offers this 6-point action plan:

1. Develop a transformation road map: Aim to strike a balance between harnessing new technology opportunities on the one hand and being realistic about your time frames, costs and priorities on the other.

2. Work with a full-stack portfolio vendor: You want a solution that’s tailored for your needs, not just an off-the-rack package. “Full stack” here means a complete offering of servers, hardware accelerators, storage and networking equipment—as well as support services for all of the above.

3. Match accelerators to your workloads: You don’t need a Formula 1 race car to take the kids to school. Same with your accelerators. Sure, you may have workloads that require super-low latency and equally high thruput. But you’re also likely to be supporting workloads that can take advantage of more affordable CPU-GPU combos. Work with your vendors to match their hardware with your workloads.

4. Seek suppliers with the right experience: Work with tech vendors that know what you need. Look for those with proven track records of helping service providers to transform and scale their infrastructures.

5. Select providers with supply-chain ownership: Ideally, your tech vendors will fully own their supply chains for boards, systems and rack designs such as liquid-cooling systems. That includes managing the vertical integration needed to combine these elements. The right supplier could help you save costs and get to market faster.

6. Create a long-term plan: Plan for the short term, but also look ahead into the future. Technology isn’t sitting still, and neither should you. Plan for technology refreshes. Ask your vendors for their road maps, and review them. Decide what you can support in-house versus what you’ll probably need to hand off to partners.

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AMD CTO: ‘AI across our entire portfolio’

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AMD CTO: ‘AI across our entire portfolio’

In a presentation for industry analysts, AMD chief technology officer Mark Papermaster laid out the company’s vision for artificial intelligence everywhere — from PC and edge endpoints to the largest hypervisor servers.

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The current buildout of the artificial intelligence infrastructure is an event as big as the original launch of the internet.

AI, now mainly an expense, will soon be monetized. Thousands of AI applications are coming.

And AMD plans to embed AI across its entire product portfolio. That will include components and software on everything from PCs and edge sensors to the largest servers used by the big cloud hypervisors.

These were among the comments of Mark Papermaster, AMD’s executive VP and CTO, during a recent fireside chat hosted by stock research firm Arete Research. During the hour-long virtual presentation, Papermaster answered questions from moderator Brett Simpson of Arete and attending stock analysts. Here are the highlights.

The overall AI market

AMD has said it believes the total addressable market (TAM) for AI through 2027 is $400 billion. “That surprised a lot of people,” Papermaster said, but AMD believes a huge AI infrastructure is needed.

That will begin with the major hyperscalers. AWS, Google Cloud and Microsoft Azure are among those looking at massive AI buildouts.

But there’s more. AI is not only in the domain of these massive clusters. Individual businesses will be looking for AI applications that can drive productivity and enhance the customer experience.

The models for these kinds of AI systems are typically smaller. They can be run on smaller clusters, too, whether on-premises or in the cloud.

AI will also make its way into endpoint devices. They’ll include PCs, embedded devices, and edge sensors.

Also, AI is more than just compute. AI systems also require robust memory, storage and networking.

“We’re thrilled to bring AI across our entire product portfolio,” Papermaster said.

Looking at the overall AI market, AMD expects to see a compound annual growth rate of 70%. “I know that seems huge,” Papermaster said. “But we are investing to capture that growth.”

AI pricing

Pricing considerations need to take into account more than just the price of a GPU, Papermaster argued. You really have to look at the total cost of ownership (TCO).

The market is operating with an underlying premise: Demand for AI compute is insatiable. That will drive more and more compute into a smaller area, delivering more efficient power per FLOP, the most common measure of AI compute performance.

Right now, the AI compute model is dominated by a single player. But AMD is now bringing the competition. That includes the recently announced MI300 accelerator. But as Papermaster pointed out, there’s more, too. “We have the right technology for the right purpose,” he said.

That includes using not only GPUs, but also (where appropriate) CPUs. These workloads can include AI inference, edge computing, and PCs. In this way, user organizations can better manage their overall CapEx spend.

As moderator Simpson reminded him, Papermaster is fond of saying that customers buy road maps. So naturally he was asked about AMD’s plans for the AI future. Papermaster mainly deferred, saying more details will be forthcoming. But he also reminded attendees that AMD’s investments in AI go back several years and include its ROCm software enablement stack.

Training vs. inference

Training and inference are currently the two biggest AI workloads. Papermaster believes we’ll see the AI market bifurcate along their two lines.

Training depends on raw computational power in a vast cluster. For example, the popular ChatGPT generative AI tool uses a model with over a trillion parameters. That’s where AMD’s MI300 comes into play, Papermaster said, “because it scales up.”

This trend will continue, because for large language models (LLMs), the issue is latency. How quickly can you get a response? That requires not only fast processors, but also equally fast memory.

More specific inferencing applications, typically run after training is completed, are a different story, Papermaster said, adding: “Essentially, it’s ‘I’ve trained my model; now I want to organize it.’” These workloads are more concise and less demanding of both power and compute, meaning they can run on more affordable GPU-CPU combinations.

Power needs for AI

User organizations face a challenge: While running an AI system requires a lot of power, many data centers are what Papermaster called “power-gated.” In other words, they’re unable to drive up compute capacity to AI levels using current technology.

AMD is on the case. In 2020, the company committed itself to driving a 30x improvement in power efficiency for its products by 2025. Papermaster said the company is still on track to deliver that.

To do so, he added, AMD is thinking in terms of “holistic design.” That means not just hardware, but all the way through an application to include the entire stack.

One promising area involves AI workloads that can use AI approximation. These are applications that, unlike HPC workloads, do not need incredible levels of accuracy. As a result, performance is better for lower-precision arithmetic than it is for high-precision. “Not all AI models are created equally,” Papermaster said. “You’ll need smaller models, too.”

AMD is among those who have been surprised by the speed of AI adoption. In response, AMD has increased its projection of AI sales this year from $2 billion to $3.5 billion, what Papermaster called the fastest ramp AMD has ever seen.

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AMD Instinct MI300 Series: Take a deeper dive in this advanced technology

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AMD Instinct MI300 Series: Take a deeper dive in this advanced technology

Take a look at the innovative technology behind the new AMD Instinct MI300 Series accelerators.

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Earlier this month, AMD took the wraps off its highly anticipated AMD Instinct MI300 Series of generative AI accelerators and data-center acceleration processing units (APUs). During the announcement event, AMD president Victor Peng said the new components had been “designed with our most advanced technologies.”

Advanced technologies indeed. With the AMD Instinct MI300 Series, AMD is writing a brand-new chapter in the story of AI-adjacent technology.

Early AI developments relied on the equivalent of a hastily thrown-together stock car constructed of whichever spare parts happened to be available at the time. But those days are over.

Now the future of computing has its very own Formula 1 race car. It’s extraordinarily powerful and fine-tuned to nanometer tolerances.

A new paradigm

At the heart of this new accelerator series is AMD’s CDNA 3 architecture. This third generation employs advanced packaging that tightly couples CPUs and GPUs to bring high-performance processing to AI workloads.

AMD’s new architecture also uses 3D packaging technologies that integrate up to 8 vertically stacked accelerator complex dies (XCDs) and four I/O dies (IODs) that contain system infrastructure. The various systems are linked via AMD Infinity Fabric technology and are connected to 8 stacks of high-bandwidth memory (HBM).

High-bandwidth memory can provide far more bandwidth and yet much lower power consumption compared with the GDDR memory found in standard GPUs. Like many of AMD’s notable innovations, its HBM employs a 3D design.

In this case, the memory modules are stacked vertically to shorten the distance the data needs to travel. This also allows for smaller form factors.

AMD has implemented the HMB using a unified memory architecture. This is an increasingly popular design in which a single array of main-memory modules supports both the CPU and GPU simultaneously, speeding tasks and applications.

Unified memory is more efficient than traditional memory architecture. It offers the advantage of faster speeds along with lower power consumption and ambient temperatures. Also, data need not be copied from one set of memory to another.

Greater than the sum of its parts

What really makes AMD CDNA 3 unique is its chiplet-based architecture. The design employs a single logical processor that contains a dozen chiplets.

Each chiplet, in turn, is fabricated for either compute or memory. To communicate, all the chiplets are connected via the AMD Infinity Fabric network-on-chip.

The primary 5nm XCDs contain the computational elements of the processor along with the lowest levels of the cache hierarchy. Each XCD includes a shared set of global resources, including the scheduler, hardware queues and 4 asynchronous compute engines (ACE).

The 6nm IODs are dedicated to the memory hierarchy. These chiplets carry a newly redesigned AMD Infinity Cache and an HBM3 interface to the on-package memory. The AMD Infinity Cache boosts generational performance and efficiency by increasing cache bandwidth and reducing the number of off-chip memory accesses.

Scaling ever upward

System architects are constantly in the process of designing and building the world’s largest exascale-class supercomputers and AI systems. As such, they are forever reaching for more powerful processors capable of astonishing feats.

The AMD CDNA 3 architecture is an obvious step in the right direction. The new platform takes communication and scaling to the next level.

In particular, the advent of AMD’s 4th Gen Infinity Architecture Fabric offers architects a new level of connectivity that could help produce a supercomputer far more powerful than anything we have access to today.

It’s reasonable to expect that AMD will continue to iterate its new line of accelerators as time passes. AI research is moving at a breakneck pace, and enterprises are hungry for more processing power to fuel their R&D.

What will researchers think of next? We won’t have to wait long to find out.

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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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Research Roundup: GenAI, 10 IT trends, cybersecurity, CEOs, and privacy

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Research Roundup: GenAI, 10 IT trends, cybersecurity, CEOs, and privacy

Catch up on the latest IT research and analysis from leading market watchers.

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Generative AI is booming. Ten trends will soon rock your customers’ world. While cybersecurity spending is up, CEOs lack cyber confidence. And Americans worry about their privacy.

That’s some of the latest from leading IT market watchers. And here’s your Performance Intensive Computing roundup.

GenAI market to hit $143B by 2027

Generative AI is quickly becoming a big business.

Market watcher IDC expects that spending on GenAI software, related hardware and services will this year reach nearly $16 billion worldwide.

Looking ahead, IDC predicts GenAI spending will reach $143 billion by 2027. That would represent a compound annual growth rate (CAGR) over the years 2023 to 2027 of 73%—more than twice the growth rate in overall AI spending.

“GenAI is more than a fleeting trend or mere hype,” says IDC group VP Ritu Jyoti.

Initially, IDC expects, the largest GenAI investments will go to infrastructure, including hardware, infrastructure as a service (IaaS), and system infrastructure software. Then, once the foundation has been laid, spending is expected to shift to AI services.

Top 10 IT trends

What will be top-of-mind for your customers next year and beyond? Researchers at Gartner recently made 10 predictions:

1. AI productivity will be a primary economic indicator of national power.

2. Generative AI tools will reduce modernization costs by 70%.

3. Enterprises will collectively spend over $30 billion fighting “malinformation.”

4. Nearly half of all CISOs will expand their responsibilities beyond cybersecurity, driven by regulatory pressure and expanding attack surfaces.

5. Unionization among knowledge workers will increase by 1,000%, motivated by fears of job loss due to the adoption of GenAI.

6. About one in three workers will leverage “digital charisma” to advance their careers.

7. One in four large corporations will actively recruit neurodivergent talent—including people with conditions such as autism and ADHD—to improve business performance.

8. Nearly a third of large companies will create dedicated business units or sales channels for machine customers.

9. Due to labor shortages, robots will soon outnumber human workers in three industries: manufacturing, retail and logistics.

10. Monthly electricity rationing will affect fully half the G20 nations. One result: Energy efficiency will become a serious competitive advantage.

Cybersecurity spending in Q2 rose nearly 12%

Heightened threat levels are leading to heightened cybersecurity spending.

In the second quarter of this year, global spending on cybersecurity products and services rose 11.6% year-on-year, reaching a total of $19 billion worldwide, according to Canalys.

A mere 12 vendors received nearly half that spending, Canalys says. They include Palo Alto Networks, Fortinet, Cisco and Microsoft.

One factor driving the spending is fear, the result of a 50% increase in the number of publicly reported ransomware attacks. Also, the number of breached data records more than doubled in the first 8 months of this year, Canalys says.

All this increased spending should be good for channel sellers. Canalys finds that nearly 92% of all cybersecurity spending worldwide goes through the IT channel.

CEOs lack cyber confidence

Here’s another reason why cybersecurity spending should be rising: Roughly three-quarters of CEOs (74%) say they’re concerned about their organizations’ ability to avert or minimize damage from a cyberattack.

That’s according to a new survey, conducted by Accenture, of 1,000 CEOs from large organizations worldwide.

Two findings from the Accenture survey really stand out:

  • Nearly two-thirds of CEOs (60%) say their organizations do not incorporate cybersecurity into their business strategies, products or services
  • Nearly half (44%) the CEOs believe cybersecurity can be handled with episodic interventions rather than with ongoing, continuous attention.

Despite those weaknesses, nearly all the surveyed CEOs (96%) say they believe cybersecurity is critical to their organizations’ growth and stability. Mind the gap!

How do Americans view data privacy?

Fully eight in 10 Americans (81%) are concerned about how companies use their personal data. And seven in 10 (71%) are concerned about how their personal data is used by the government.

So finds a new Pew Research Center survey of 5,100 U.S. adults. The study, conducted in May and published this month, sought to discover how Americans think about privacy and personal data.

Pew also found that Americans don’t understand how their personal data is used. In the survey, nearly eight in 10 respondents (77%) said they have little to no understanding of how the government uses their personal data. And two-thirds (67%) said the same thing about businesses, up from 59% a year ago.

Another key finding: Americans don’t trust social media CEOs. Over three-quarters of Pew’s respondents (77%) say they have very little or no trust that leaders of social-medica companies will publicly admit mistakes and take responsibility.

And about the same number (76%) believe social-media companies would sell their personal data without their consent.

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Tech Explainer: How does design simulation work? Part 2

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Tech Explainer: How does design simulation work? Part 2

Cutting-edge technology powers the virtual design process.

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The market for simulation software is hot, growing at a compound annual growth rate (CAGR) of 13.2%, according to Markets and Markets. The research firm predicts that the global market for simulation software, worth an estimated $18.1 billion this year, will rise to $33.5 billion by 2027.

No surprise, then, that tech titans AMD and Supermicro would design an advanced hardware platform to meet the demands of this burgeoning software market.

AMD and Supermicro have teamed up with Ansys Inc., a U.S.-based designer of engineering simulation software. One result of this three-way collaboration is the Supermicro SuperBlade.

Shanthi Adloori, senior director of product management at Supermicro, calls the SuperBlade “one of the fastest simulation-in-a-box solutions.”

Adloori adds: “With a high core count, large memory capacity and faster memory bandwidth, you can reduce the time it takes to complete a simulation .”

One very super blade

Adloori isn’t overstating the case.

Supermicro’s SuperBlade can house up to 20 hot-swappable nodes in its 8U chassis. Each of those blades can be equipped with AMD EPYC CPUs and AMD Instinct GPUs. In fact, SuperBlade is the only platform of its kind designed to support both GPU and non-GPU nodes in the same enclosure.

Supermicro SuperBlade’s other tech specs may be less glamorous, but they’re no less impressive. When it comes to memory, each blade can address a maximum of either 8TB or 16TB of DDR5-4800 memory.

Each node can also house 2 NVMe/SAS/SATA drives and as many as eight 3000W Titanium Level power supplies.

Because networking is an essential element of enterprise-grade design simulation, SuperBlade includes redundant 25Gb/10Gb/1Gb Ethernet switches and up to 200Gbps/100Gbps InfiniBand networking for HPC applications.

For smaller operations, the Supermicro SuperBlade is also available in smaller configurations, including  6U and 4U. These versions pack fewer nodes, which ultimately means they’re able to bring less power to bear. But, hey, not every design team makes passenger jets for a living.

It’s all about the silicon

If Supermicro’s SuperBlade is the tractor-trailer of design simulation technology, then AMD CPUs and GPUs are the engines under the hood.

The differing designs of these chips lend themselves to specific core competencies. CPUs can focus tremendous power on a few tasks at a time. Sure, they can multitask. But there’s a limit to how many simultaneous operations they can address.

AMD bills its EPYC 7003 Series CPUs as the world’s highest-performing server processors for technical computing. The addition of AMD 3D V-Cache technology delivers an expanded L3 cache to help accelerate simulations.

GPUs, on the other hand, are required when running simulations where certain tasks require simultaneous operations to be performed. The AMD Instinct MI250X Accelerator contains 220 compute units with 14,080 stream processors.

Instead of throwing a ton of processing power at a small number of operations, the AMD Instinct can address thousands of less resource-intensive operations simultaneously. It’s that capability that makes GPUs ideal for HPC and AI-enabled operations, an increasingly essential element of modern design simulation.

The future of design simulation

The development of advanced hardware like SuperBlade and the AMD CPUs and GPUs that power it will continue to progress as more organizations adopt design simulation as their go-to product development platform.

That progression will continue to manifest in global companies like Boeing and Volkswagen. But it will also find its way into small startups and single users.

Also, as the required hardware becomes more accessible, simulation software should become more efficient.

This confluence of market trends could empower millions of independent designers with the ability to perform complex design, testing and validation functions.

The result could be nothing short of a design revolution.

Part 1 of this two-part Tech Explainer explores the many ways design simulation is used to create new products, from tiny heart valves to massive passenger aircraft. Read Part 1 now.

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Why M&E content creators need high-end VDI, rendering & storage

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Why M&E content creators need high-end VDI, rendering & storage

Content creators in media and entertainment need lots of compute, storage and networking. Supermicro servers with AMD EPYC processors are enhancing the creativity of these content creators by offering improved rendering and high-speed storage. These systems empower the production of creative ideas.

 

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When content creators at media and entertainment (M&E) organizations create videos and films, they’re also competing for attention. And today that requires a lot of technology.

Making a full-length animated film involves no fewer than 14 complex steps, including 3D modeling, texturing, animating, visual effects and rendering. The whole process can take years. And it requires a serious quantity of high-end compute, storage and software.

From an IT perspective, three of the most compute-intensive activities for M&E content creators are VDI, rendering and storage. Let’s take a look at each.

* Virtual desktop infrastructure (VDI): While content creators work on personal workstations, they need the kind of processing power and storage capacity available from a rackmount server. That’s what they get with VDI.

VDI separates the desktop and associated software from the physical client device by hosting the desktop environment and applications on a central server. These assets are then delivered to the desktop workstation over a network.

To power VDI setups, Supermicro offers a 4U GPU server with up to 8 PCIe GPUs. The Supermicro AS -4125GS-TNRT server packs a pair of AMD EPYC 9004 processors, Nvidia RTX 6000 GPUs, and 6TB of DDR5 memory.

* Rendering: The last stage of film production, rendering is where the individual 3D images created on a computer are transformed into the stream of 2D images ready to be shown to audiences. This process, conducted pixel by pixel, is time-consuming and resource-hungry. It requires powerful servers, lots of storage capacity and fast networking.

For rendering, Supermicro offers its 2U Hyper system, the AS -2125HS-TNR. It’s configured with dual AMD EPYC 9004 processors, up to 6TB of memory, and your choice of NVMe, SATA or SAS storage.

* Storage: Content creation involves creating, storing and manipulating huge volumes of data. So the first requirement is simply having a great deal of storage capacity. But it’s also important to be able to retrieve and access that data quickly.

For these kinds of storage challenges, Supermicro offers Petascale storage servers based on AMD EPYC processors. They can pack up to 16 hot-swappable E3.S (7.5mm) NVMe drive bays. And they’ve been designed to store, process and move vast amounts of data.

M&E content creators are always looking to attract more attention. They’re getting help from today’s most advanced technology.

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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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Tech Explainer: What’s the difference between Machine Learning and Deep Learning? Part 1

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Tech Explainer: What’s the difference between Machine Learning and Deep Learning? Part 1

What’s the difference between machine learning and deep learning? That’s the subject of this 2-part Tech Explainer. Here, in Part 1, learn more about ML. 

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As the names imply, machine learning and deep learning are types of smart software that can learn. Perhaps not the way a human does. But close enough.

What’s the difference between machine and deep learning? That’s the subject of this 2-part Tech Explainer. Here in Part 1, we’ll look in depth at machine learning. Then in Part 2, we’ll look more closely at deep learning.

Both, of course, are subsets of artificial intelligence (AI). To understand their differences, it helps to first understand something of the AI hierarchy.

At the very top is overarching AI technology. It powers both popular generative AI models such as ChatGPT and less famous but equally helpful systems such as the suggestion engine that tells you which show to watch next on Netflix.

Machine learning is a subset of AI. It can perform specific tasks without first needing explicit instructions.

As for deep learning, it’s actually a subset of machine learning. DL is powered by so-called neural networks, multiple node layers that form a system inspired by the structure of the human brain.

Machine learning for smarties

Machine learning is defined as the use and development of computer systems designed to learn and adapt without following explicit instructions.

Instead of requiring human input, ML systems use algorithms and statistical models to analyze and draw inferences from patterns they find in large data sets.

This form of AI is especially good at identifying patterns from structured data. Then it can analyze those patterns to make predictions, usually reliable.

For example, let’s say an organization wants to predict when a particular customer will unsubscribe from its service. The organization could use ML to make an educated guess based on previous data about customer churn.

The machinery of ML

Like all forms of AI, machine learning uses lots of compute and storage resources. Enterprise-scale ML models are powered by data centers packed to the gills with cutting-edge tech. The most vital of these components are GPUs and AI data-center accelerators.

GPUs, though initially designed to process graphics, have become the preferred tool for AI development. They offer high core counts—sometimes numbering in the thousands—as well as massive parallel processes. That makes them ideally suited to process a vast number of simple calculations simultaneously.

As AI gained acceptance, IT managers sought ever more powerful GPUs. The logical conclusion was the advent of new technologies like AMD’s Instinct MI200 Series accelerators. These purpose-built GPUs have been designed to power discoveries in mainstream servers and supercomputers, including some of the largest exascale systems in use today.

AMD’s forthcoming Instinct MI300X will go one step further, combining a GPU and AMD EPYC CPU in a single component. It’s set to ship later this year.

State-of-the-art CPUs are important for ML-optimized systems. The CPUs need as many cores as possible, running at high frequencies to keep the GPU busy. AMD’s EPYC 9004 Series processors excel at this.

In addition, the CPUs need to run other tasks and threads of the application. When looking at a full system, PCIe 5.0 connectivity and DDR4 memory are important, too.

The GPUs that power AI are often installed in integrated servers that have the capacity to house their constituent components, including processors, flash storage, networking tech and cooling systems.

One such monster server is the Supermicro AS -4125GS-TNRT. It brings together eight direct attached, double-width, full-length GPUs; up to 6TB of RAM; and two dozen 2.5-inch solid-state drives (SSDs). This server also supports the AMD Instinct MI210 accelerator.

ML vs. DL

The difference between machine learning and deep learning begins with their all-important training methods. ML is trained using four primary methods: supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

Deep learning, on the other hand, requires more complex training methods. These include convolutional neural networks, recurrent neural networks, generative adversarial networks and autoencoders.

When it comes to performing real-world tasks, ML and DL offer different core competencies. For instance, ML is the type of AI behind the most effective spam filters, like those used by Google and Yahoo. Its ability to adapt to varying conditions allows ML to generate new rules based on previous operations. This functionality helps it keep pace with highly motivated spammers and cybercriminals.

More complex inferencing tasks like medical imaging recognition are powered by deep learning. DL models can capture intricate relationships within medical images, even when those relationships are nonlinear or difficult to define. In other words, deep learning can quickly and accurately identify abnormalities not visible to the human eye.

Up next: a Deep Learning deep dive

In Part 2, we’ll explore more about deep learning. You’ll find out how data scientists develop new models, how various verticals leverage DL, and what the future holds for this emerging technology.

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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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