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Supermicro Adds AI-Focused Systems to H13 JumpStart Program

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Supermicro Adds AI-Focused Systems to H13 JumpStart Program

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

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

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

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

3 simple steps

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

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

So very secure

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

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

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

Keep up with the times

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

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

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

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

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

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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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AMD drives AI with Instinct MI300X, Instinct MI300A, ROCm 6

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AMD drives AI with Instinct MI300X, Instinct MI300A, ROCm 6

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AMD this week formally introduced its AMD Instinct MI300X and AMD Instinct MI300A accelerators, two important elements of the company’s new push into AI.

During the company’s two-hour “Advancing AI” event, held live in Silicon Valley and live-streamed on YouTube, CEO Lisa Su asserted that “AI is absolutely the No. 1 priority at AMD.”

She also said that AI is both “the future of computing” and “the most transformative technology of the last 50 years.”

AMD is leading the AI charge with its Instinct MI300 Series accelerators, designed for both cloud and enterprise AI and HPC workloads. These systems offer GPUs, large and fast memory, and 3D packaging using the 4th gen AMD Infinity Architecture.

AMD is also relying heavily on cloud, OEM and software partners that include Meta, Microsoft and Oracle Cloud. Another partner, Supermicro, announced additions to its H13 generation of accelerated servers powered by 4th Gen AMD EPYC CPUs and AMD Instinct MI300 Series accelerators.

MI300X

The AMD Instinct MI300X is based on the company’s CDNA 3 architecture. It packs 304 GPU cores. It also includes up to 192MB of HBM3 memory with a peak memory bandwidth of 5.3TB/sec. It’s available as 8 GPUs on an OAM baseboard.

The accelerator runs on the latest bus, the PCIe Gen 5, at 128GB/sec.

AI performance has been rated at 20.9 PFLOPS of total theoretical peak FP8 performance, AMD says. And HPC performance has a peak double-precision matrix (FP64) performance of 1.3 PFLOPS.

Compared with competing products, the AMD Instinct MI300X delivers nearly 40% more compute units, 1.5x more memory capacity, and 1.7x more peak theoretical memory bandwidth, AMD says.

AMD is also offering a full system it calls the AMD Instinct Platform. This packs 8 MI300X accelerators to offer up to 1.5TB of HBM3 memory capacity. And because it’s built on the industry-standard OCP design, the AMD Instinct Platform can be easily dropped into an existing servers.

The AMD Instinct MI300X is shipping now. So is a new Supermicro 8-GPU server with this new AMD accelerator.

MI300A

AMD describes its new Instinct MI300A as the world’s first data-center accelerated processing unit (APU) for HPC and AI. It combines 228 cores of AMD CDNA 3 GPU, 224 cores of AMD ‘Zen 4’ CPUs, and 128GB of HBM3 memory with a memory bandwidth of up to 5.3TB/sec.

AMD says the Instinct MI300A APU gives customers an easily programmable GPU platform, high-performing compute, fast AI training, and impressive energy efficiency.

The energy savings are said to come from the APU’s efficiency. As HPC and AI workloads are both data- and resource-intensive, a more efficient system means users can do the same or more work with less hardware.

The AMD Instinct MI300A is also shipping now. So are two new Supermicro servers that feature the APU, one air-cooled, and the other liquid-cooled.

ROCm 6

As part of its push into AI, AMD intends to maintain an open software platform. During CEO Su’s presentation, she said that openness is one of AMD’s three main priorities for AI, along with offering a broad portfolio and working with partners.

Victor Peng, AMD’s president, said the company has set as a goal the creation of a unified AI software stack. As part of that, the company is continuing to enhance ROCm, the company’s software stack for GPU programming. The latest version, ROCm 6, will ship later this month, Peng said.

AMD says ROCm 6 can increase AI acceleration performance by approximately 8x when running on AMD MI300 Series accelerators in Llama 2 text generation compared with previous-generation hardware and software.

ROCm 6 also adds support for several new key features for generative AI. These include FlashAttention, HIPGraph and vLLM.

AMD is also leveraging open-source AI software models, algorithms and frameworks such as Hugging Face, PyTorch and TensorFlow. The goal: simplify the deployment of AMD AI solutions and help customers unlock the true potential of generative AI.

Shipments of ROCm are set to begin later this month.

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Research Roundup: GenAI use, public-cloud spend, tech debt’s reach, employee cyber violations

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Research Roundup: GenAI use, public-cloud spend, tech debt’s reach, employee cyber violations

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

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Generative AI is already used by two-thirds of organizations. Public-cloud spending levels are forecast to rise 20% next year. Technical debt is a challenge for nearly 75% of organizations. And info-security violations by staff are nearly as common as attacks by external hackers.

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

GenAI already used by 2/3 of orgs

You already know that Generative AI is hot, but did you also realize that over two-thirds of organizations are already using it?

In a survey of over 2,800 tech professionals, publisher O’Reilly found that fully 67% of respondents say their organizations currently use GenAI. Of this group, about 1 in 3 also say their organizations have been working with AI for less than a year.

Respondents to the survey were users of O’Reilly products worldwide. About a third of respondents (34%) work in the software industry; 14% in financial services; 11% in hardware; and the rest in industries that include telecom, public sector/government, healthcare and education. By region, nearly three-quarters of respondents (74%) are based in either North America or Europe.

Other key findings from the O’Reilly survey (multiple replies were permitted):

  • GenAI’s top use cases: Programming (77%); data analysis (70%); customer-facing applications (65%)
  • GenAI’s top use constraints: Lack of appropriate use cases (53%); legal issues, risk and compliance (38%)
  • GenAI’s top risks: Unexpected outcomes (49%); security vulnerabilities (48%); safety and reliability (46%)

Public-cloud spending to rise 20% next year

Total worldwide spending by end users on the public cloud will rise 20% between this year and next, predicts Gartner. This year, the market watcher adds, user spending on the public cloud will total $563.6 billion. Next year, this spend will rise to $678.8 billion.

“Cloud has become essentially indispensable,” says Gartner analyst Sid Nag.

Gartner predicts that all segments of the public-cloud market will grow in 2024. But it also says 2 segments will grow especially fast next year: Infrastructure as a Service (IaaS), predicted to grow nearly 27%; and Platform as a Service (PaaS), forecast to grow nearly 22.

What’s driving all this growth? One factor: industry cloud platforms. These combine Software as a Service (SaaS), PaaS and IaaS into product offerings aimed at specific industries.

For example, enterprise software vendor SAP offers industry clouds for banking, manufacturing, HR and more. The company says its life-sciences cloud helped Boston Scientific, a manufacturer of medical devices, reduce inventory and order-management operational workloads by as much as 45%.

Gartner expects that by 2027, industry cloud platforms will be used by more than 70% of enterprises, up from just 15% of enterprises in 2022.

Technical debt: a big challenge

Technical debt—older hardware and software that no longer supports an organization’s strategies—is a bigger problem than you might think.

In a recent survey of 523 IT professionals, conducted for IT trade association CompTIA, nearly three-quarters of respondents (74%) said their organizations find tech debt to be a challenge.

An even higher percentage of respondents (78%) say their work is impeded by “cowboy IT,” shadow IT and other tech moves made without the IT department’s involvement. Not incidentally, these are among the main causes of technical debt, mainly because they are not acquired as part of the organization’s strategic goals.

Fortunately, IT pros are also fighting back. Over two-thirds of respondents (68%) said they’ve made erasing technical debt a moderate or high priority.

Cybersecurity: Staff violations nearly as widespread as hacks

Employee violations of organizations’ information-security policies are nearly as common as attacks by external hackers, finds a new survey by security vendor Kaspersky. 

The survey reached 1,260 IT and security professionals worldwide. It found that 26% of cyber incidents in business occurred due to employees intentionally violating their organizations’ security protocols. By contrast, hacker attacks accounted for 30%—not much higher.

Here’s the breakdown of those policy violations by employees, according to Kaspersky (multiple replies were permitted):

  • 25%: Using weak passwords or failing to change passwords regularly
  • 24%: Visiting unsecured websites
  • 24%: Using unauthorized systems for sharing data
  • 21%: Failing to update system software and applications
  • 21%: Accessing data with an unauthorized device
  • 20%: Sending data (such as email addresses) to personal systems
  • 20%: Intentionally engaging in malicious behavior for personal gain

The issue is far from theoretical. Among respondents to the Kaspersky survey, fully a third (33%) say they’ve suffered 2 or 3 cyber incidents in the last 2 years. And a quarter (25%) say that during the same time period, they’ve been the subject of at least 4 cyberattacks.

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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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Tech Explainer: How does design simulation work? Part 1

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Tech Explainer: How does design simulation work? Part 1

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

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

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

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

Getting better all the time

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

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

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

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

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

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

Changing designs, saving lives

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

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

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

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

Making the business case

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

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

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

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

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

Greater efficiency

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

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

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

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

In countless ways, simulation has never been more real.

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

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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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