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Data Sciences Corporation celebrates 10-years

Daniela — Thu, 16 Nov 2023 08:45:30 +0000

Data Sciences Corporation celebrates 10 years of offering a wide range of services, from storage and backup appliances to artificial intelligence and machine learning.

Its winning strategy over the past 10 years was to stay relevant to its valued enterprise customers by growing its core products and expanding into new technology offerings, as and when the market was ready.

With a consultative approach, it has maintained a good standing with its customers, always aiming to tailor solutions to specific needs. Reflecting on the successes achieved over the past 10 years, the company’s ability to build close partnerships with its vendors also stands out as a highlight. Data Sciences Corporation is very proud to be recognised by its vendors as representing the highest tier of partnership available, and often referred to as their “extension” in the market. A major differentiator for Data Sciences Corporation is its ability to service the technology it sells and provide its customers with a single engagement point.

Although the core business lies in the data centre infrastructure, Data Sciences Corporation has, over the past few years, invested time and money to identify and adopt next-generation technology. With its commitment to remain always forward-looking and expanding into new markets, Data Sciences Corporation excels at translating value to its existing and future customer-base. For example, its partnership with NVIDIA and the investment into AI technologies will be a strong focus for the business going forward, embracing the new wave of technology.

Data Sciences Corporation has also developed a unique Multi-Cloud Platform-as-a-Service, offering cloud-native solutions, using modern models that provide unmatched flexibility, scalability and reliability, resulting in improved uptime and security.

The company believes it is through this ability to provide unique, relevant and value-adding technologies and solutions that help solve customers’ challenges that gives Data Sciences Corporation the edge to solve customers’ complex needs.

With growth and expansion in mind, Jean Coetzee has been appointed as Data Sciences Corporation’s Group Executive for Enterprise Development. He is responsible for developing and executing strategies that increase revenue, expand their customer base and develop new business opportunities.

On sharing his vision, Coetzee said: “My chosen approach is to have a customer-advocate mindset, meaning that one must stay in tune with the customers you have or would like to have. With this outlook and through cross-functional collaboration within the business, as well as an understanding of the market dynamics, I will develop strategies and ultimately execute on the kind of experiences we would like to create.”

Coetzee has close to 30 years of ICT experience, encompassing various senior roles. He has proven expertise in managing and leading teams and businesses. In his previous role, he was responsible for growth at Teraco.

Coetzee is an MBA graduate from the Unisa School for Business Leaders, with a dissertation focus on business-to-business relationship marketing, along with several other qualifications, such as Digital Strategies for Business at Columbia Business School.

“We are very excited about Jean joining the team and look forward to the next 10 years of collaborating with our current and future customers and providing unique and relevant technologies that solve their challenges,” says Gerard Almon, MD of Data Sciences Corporation.

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Commvault, Data Sciences partnership ensures data activation

Daniela — Tue, 08 Mar 2022 10:42:16 +0000

With the exponential growth of data and given the fact that it is essential to the smooth operation of any business, protecting and backing up your data is critical.

Equally important is the activation of that data, which provides your business with insight. However, this needs to be underpinned by using a partner you can trust. Commvault, a global leader in enterprise backup, recovery, data management and the cloud, partnered with Data Sciences in early 2017, which subsequently extended its reach throughout South Africa and bolstered the company’s service offering.

Click here to find out more about data protection from Commvault.

Further reinforcing the partnership, Data Sciences recently received additional certifications under the Commvault Service Advantage programme. Not only has Data Sciences achieved the prestigious Service Advantage Status, but it has gone the extra mile to be certified as a Service Advantage+ partner.

Johan Scheepers, Country Head at Commvault, said: “Commvault has always partnered with companies who have a great understanding and passion for data, and more importantly understand the need for businesses to activate their data. Our goal is to impart the importance of this and educate the South African market with information, advice and guidance.”

As part of this drive, Data Sciences, in conjunction with Commvault, recently held an event that emphasised the value of activating business data.

Sashen Govender, IT Executive at Rheinmetall Denel Munition, who attended the event, explains: “Data Sciences has delivered a world-class data management system ensuring that our information is appropriately safeguarded and available. Utilising Commvault’s intelligent data services technology, Rheinmetall Denel Munition is assured that the data is reactive from the edge to the data centre and across the cloud.”

Warren Hulley, Regional Executive at Data Sciences, said: “Increasing data volumes along with penalties for poor data collection and handling can no longer be taken lightly. This was one of the key focal points at this joint event, providing customers with an understanding on the importance of activating their data. We emphasised that in doing so, customers would know what data the business had, what it is used for, who owns it and whether it should be retained.”

Further to this, Data Sciences believes that harnessing the data and technology that surrounds us is paramount in driving innovation, developing knowledge and stimulating growth in respective markets.

Moreover, activated data is about gaining insight and giving you foresight. By working with partners like Data Sciences, which is Commvault certified, enterprises can adapt to the ever-changing technology landscape.

“Whether you’ve already embarked on a cloud adoption journey or whether you are about to, working with the right partners is critical to achieving data activation. Businesses therefore need to ensure they select a service provider who can adapt to changing business needs and shift with their customers as they grow,” concluded Scheepers.

For more information on how your business can activate your data and ensure data protection, click here.

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Data Sciences Corporation joins the ranks of Commvault Authorised Support Program partners

Daniela — Tue, 08 Mar 2022 10:28:27 +0000

Data Sciences Corporation is a provider of innovative technologies to help businesses capture, process, analyse and retain data. The company aims to provide customers with world-class service and support on its products. As part of this commitment, it has joined the ranks of Commvault Authorised Support Program (CASP) partners, becoming only the second organisation in South Africa to attain this status.

“Our CASP partners are effectively an extension of Commvault, allowing us to provide the same level of support from local partners as our customers expect from our global teams. The programme is offered on an invitation-only basis, to carefully selected partners. Partners selected to participate in the programme are empowered to deliver customer support based on industry-leading standards as established by Commvault. Having been a Commvault partner for the past five years, Data Sciences has embraced our methodology and has invested significantly in our product and the required skills matrix, earning them this accolade,” says Gerhard Fourie, District Channel Manager at Commvault South Africa.

This localised support has numerous benefits for Commvault customers. They are able to access support in all 11 official South African languages, from a partner that is located in the same time zone and has an understanding of the nuances of the local market. This offers a far more personalised experience when dealing with the Commvault offering, and removes many of the typical communication barriers. It also allows engineers to be deployed on site at short notice, should this be required to troubleshoot a problem. Customers have peace of mind that critical issues will be resolved in short time frames.

“We always embrace opportunities to further enhance our services capabilities and ensure that our customers have the best possible service experience available. The CASP programme allows us to extend our offering and better bridge the overall service requirements between our customers and Commvault support. It is a privilege to partake in CASP, and this certification will benefit both our business and our customers with a wider range of professional and support services and an enhanced value proposition,” says Isaac Makoto, Business Development Manager at Data Sciences.

“For Commvault, bringing another CASP partner into the fold shows our commitment towards growing our channel locally, as well as to providing the highest levels of support and service. We now have two CASP support partners and multiple certified partners in the market, expanding our technical community with skills and knowledge around our product. Commvault customers can rest assured that they are supported every step of the way, from sales through to implementation and support,” Fourie adds.

Achieving CASP partner status requires training from certified instructors and certification via the Commvault examination board. There is also a practical component that involves handling specific scenarios and tasks, where the speed and accuracy of response is judged. Data Sciences passed all aspects on its first attempt, resulting in the company being awarded Foundation level certification. This is the first of four levels, culminating in Master status. Data Sciences is already making solid progress in skilling up to achieve the second level of Core partner status.

“As a strategic Commvault partner, we function as a cohesive team with common goals and strategies, which creates the springboard for future success. Becoming a certified CASP partner is a milestone, displaying the commitment of both organisations to the partnership. Ultimately, this will see the end customer benefiting from excellent support and service and world-class technology – a win-win scenario for all stakeholders,” Makoto concludes.

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Data Sciences achieves Pure Storage Elite Partnership for 4th year in a row

Daniela — Tue, 08 Mar 2022 09:48:16 +0000

Data Sciences Corporation (“Data Sciences” or “the company“), an infrastructure and solutions enablement provider, is pleased to announce it has achieved Elite Partner status with Pure Storage for the fourth consecutive year. Elite Partner Status is the highest achievement within the Pure Storage Partner Program and is designed to support partners by investing in those who are fully committed to Pure.

In partnership with Pure Storage, we are in a position to deliver products and solutions that enable customers to optimise their most valuable asset: their data. This can include accelerating traditional applications using NVMe technology; leveraging data to support AI and ML; migrating workloads to the cloud; and implementing storage as a service models. Over the past several years, Data Sciences has accumulated a wealth of knowledge, expertise and experience in transforming our enterprise customers’ data centres by delivering a better customer experience with innovation and simplicity.

“Becoming a Pure Elite partner is by invitation only and we are proud to have met this criteria again,” says Rui de Oliveira, Business Development Director at Data Sciences. “Our continued focus on maintaining this partnership and growing our business together underpins our strategic alliance. Together we work hard to bring technology to customers, which helps them do more with their data and ultimately accelerate their businesses.”

Data Sciences prides itself in bringing innovative data centre technologies to the South African market with a key focus on providing local world-class technical service and support capabilities as demanded by enterprise customers. The partnership with Pure Storage has been successful because of the close collaboration between the two companies over the past several years.

“Data Sciences is a long-standing and dedicated partner who we work with closely across South Africa,” commented Rupert Brazier, country manager, South Africa, Pure Storage. “Together we offer organisations the opportunity to access a modern data experience through Pure’s subscription to innovation. Like Pure, Data Sciences is heavily focused on customer service – we both place high value in going the extra mile for our customers. Data Sciences demonstrates what we value in our channel – long-term strategic partnership, which is focused on exceeding customer needs and solving their challenges.”

Data Sciences Corporation is a solutions and infrastructure-styled business with specific market penetration within the intelligent flash storage segment. This infrastructure facilitates key business processes like online transaction processing, server virtualisation, virtual desktop infrastructure and services enterprise business IT requirements. Our targeted market includes the financial, telecommunications and banking sectors where legislation dictates IT information being kept for a prescribed length of time.

Through innovative technologies, thought leadership and top-tier expertise in delivering enterprise data centre solutions, the team at Data Sciences Corporation is well positioned to deliver differentiating solutions underpinned by world-class services to capture, process, analyse and retain the crucial data that drives businesses. Key to Data Sciences and its clients’ success are its technology partners. As an important pillar to Data Science’s business: strategic relationships with the partner ecosystem are of the utmost importance to leverage innovative and forward-thinking technologies to deliver solutions that are integrated, scalable and reliable.

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High-performance computing and the enterprise: A new era?

Daniela — Tue, 08 Mar 2022 09:02:53 +0000

Read time 4min 40sec

Adrian Wood, Business Operations Director

Everyone has heard about supercomputers. These specialised machines have been around for about as long as digital computing. Their first jobs were to crunch heavy mathematical workloads – one of the original supercomputer languages, FORTRAN, is a portmanteau of formula and translation.

Supercomputers, also called high-performance computers (HPCs), helped win the Cold War through their intensive analysis capabilities. Even today, in a world dominated by microprocessor systems, HPCs make a unique impact. It’s the power behind HPCs that enabled scientists to analyse the COVID-19 virus so quickly.

All those examples still speak of bespoke boxes in very specialised environments, and many enterprises might say HPCs are not for them. But this picture is changing – and fast.

“HPCs were traditionally suited for academia and research,” says Adrian Wood, Business Operations Director at Data Sciences Corporation. “But with changes in technology there’s been this convergence of technologies from a commodity perspective.”

HPC for the AI era

HPCs continually evolve, embracing the best technology trends as they emerge. In the 1980s, HPCs leaned towards x86 processors and, in the emerging 2000s, they enthusiastically adopted multi-core systems. Today, new factors prompt another HPC evolution, one of them being the $800 billion chip company reshaping the computing market.

“I don’t think you can talk about modern HPCs and not talk about NVIDIA,” says Werner Coetzee, Data Sciences Corporation’s Business Development Executive. “They started on producing graphics chips (GPUs) for gaming and visual workloads, then expanded as users realised GPUs are great for crypto-mining and training artificial intelligence. NVIDIA saw the future that these changes could create and made several strategic acquisitions to support the case for industrial HPCs.”

Industrial HPCs is the term NVIDIA uses to describe commodity HPCs. Such systems remain specialised and immensely powerful, but their costs are much more appealing to a broader enterprise market. Industrial HPCs step beyond traditional use cases such as academic research and military modelling. As AI and big data needs grow in large companies, so does the usefulness of HPCs. The chipset market already banks on this shift – apart from NVIDIA, Intel and ARM are also actively producing new HPC architectures.

Emerging use cases include spotting fraud in financial services, training service desk bots, modelling factories, creating digital twins, emulating real-world conditions (popular among agritech companies), planning logistics and supply chains, generating actuarial predictions, accelerating R&D testing, and many more concepts that can involve AI, automation and data analysis.

“There’s a lot of businesses that are amassing huge amounts of data through various points, whether it’s IOT devices or just general connections into various data processing platforms,” Wood explains. “They have accumulated this huge amount of data that they are assuming there’s value in but they don’t know how to get there. The traditional kind of data warehouse can offer some simple modelling tools to get an output, but it hasn’t really hasn’t contributed to the vision of currency in the data.”

Why now?

HPCs make an excellent case for enterprise adoption. But why now, and what about alternatives?

The widening market for GPUs contributed to lower HPC costs. As the world realised that these graphics processors could do more than render game visuals, their popularity motivated more supply. Companies like NVIDIA noted this shift and made strategic acquisitions and consolidations to take advantage of the trend.

For example, notes Coetzee, NVIDIA “acquired Mellanox, bringing high performance, low latency infiniband networking to the commercial enterprise world. They also embraced the advent of flash storage. Those three fundamental components – GPUs, low-latency infiniband networking and flash storage – have allowed HCPs to become affordable.”

The HPC boom even extends to hyperscale cloud providers, which offer HPC services. This begs the question: Why get an HPC system if you can just rent one in the cloud?

“There are definitely conceptual overlaps between hyperscale cloud and HPC, and there are times when using the cloud is a better choice,” says Wood. “It’s useful especially for companies that want to dip their toes into what HPC can do for them. But cloud systems also have limitations in how much you can customise the HPC environment, and there are extra considerations such as ingress and egress costs for data.”

It’s not a question of one or the other. Indeed, if HPCs remained the cloistered mega boxes that hide behind university doors, that answer would be different. But since HPC has become more affordable, enterprises are seeing more reasons why their data, research and operational environments can benefit from an HPC system they control and configure.

“There’s probably not an industry out there that will not be disrupted by machine and deep learning and, most likely, every form of application going forward will have some form of artificial intelligence aspect to it,” says Coetzee. “Whether it’s in the sales side or in the production side, it really doesn’t matter. Artificial Intelligence supported by HPC is causing disruption everywhere.”

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Supercomputers in enterprise AI

Daniela — Tue, 08 Mar 2022 08:57:36 +0000

NVIDIA DGX SuperPod – 5th Fastest Supercomputer & 1st Green Supercomputer in the world.

In 2009, the world’s first crypto-currency, Bitcoin, was created and the graphics processing units (GPU) proved to be the ideal platform to “mine” or calculate the algorithms required for the blockchain to function. At that point in time, GPUs were almost exclusively the domain of consumer-based gaming computers or high-end video processing workstations. Fast forward a decade to today and you will find that six out of the top 10 fastest algorithm cruncher computers on the planet contain GPUs (see the full list here: Top500). Supercomputers are not new, but with the advent of GPU it has unleashed the power of these supercomputers to new heights.

Now GPU-based supercomputing and AI are almost synonymous. The vast amounts of data made up of many different data types requires enormous computational power to be processed, inferred or analysed. Traditionally, use cases for supercomputers were the sole domain for government and scientific research. This is no longer the case – forward-thinking enterprises across the globe have recognised that the future of business success will depend on their ability to leverage AI. They will do this by creating new products and services that incorporate AI as part of the organisation’s DNA and that of their customers. But this was not always the case, let’s consider a very brief history in supercomputer adoption.

Mainstream supercomputer adoption challenges

In the past, it was almost impossible for enterprises to access supercomputers. It essentially meant that an organisation had to commission a science experiment to access a supercomputer that was hosted by a government science department or research laboratory. Data had to be prepared, wrangled, extracted and transformed. Specialised code had to be written, checked and compiled by people who have the letters PhD behind their name. Then you had to wait your turn in a long queue of people wearing long white lab coats to access the supercomputer. Once you reached the front of the line you had to load your data and application and process it, and hope you got your answers the first time without having to get back in the line of people with long white lab coats with the letters PhD behind their name.

If an enterprise did not feel like queuing at government departments and could contort themselves through the financial budget control hoops of their accounting departments, they could consider spending an astronomical amount of money and time to build a data centre that could house a supercomputer and coat racks. Once they accomplished that feat of corporate agility, they could embark on the odyssey of building the actual supercomputer. By the time that was finished, and they had lured a few people with long white lab coats out of the waiting line to their newly built supercomputer, the enterprise could start thinking about the questions they needed to answer with the supercomputer and if the questions are still relevant.

In the more recent past, in addition to governments and research labs, hyperscale public cloud providers have proven to be an alternative. Using the powers of economies of scale, volume-based discounted purchasing and not a small amount of people with PhD behind their name, they have provided enterprises with the opportunity to access supercomputer hardware.

This miracle of ready-to-run cloud-based supercomputer power does not come without its own challenges. Those being the not so trivial challenges related to the average enterprise trying to adopt a public cloud-based operating model. Couple that with the vast amount of data that is continuously generated, not always in the cloud, mind you, that must be sucked into said cloud, secured from threats, and then repatriated back to not yet cloud-native on-premises systems to make the significant investment worthwhile.

Though this method of supercomputing is a giant leap in the right direction and enables many organisations to start their supercomputer AI journey. (Even better, it does not require the space of a small farm in Midrand filled with thousands of computers and anti-load-shedding generators.) Despite these benefits, public cloud is not always a feasible option.

Enterprise supercomputer AI adoption

To return to our opening statement, organisations are looking towards emerging technologies to gain advantage and remain relevant in an ever-changing marketplace. The consumers they are catering for are also becoming more and more sophisticated and their needs more complex than ever before.

The digitisation of every industry has unleashed a drive to simplify the customer experience and remove the complexities of transacting with relevant customers. Even as the “front end” customer experience is constantly simplified, the “back end” systems and regulatory demands have become increasingly more complex to abstract the user experience. At the risk of oversimplification, the days of having a CRM or ERP system and database that feeds hundreds of dashboards hosted on what now can be considered general purpose compute to run the enterprise decision-making capability is a legacy era.

Interconnected systems at enterprise scale require in-the-moment decision-making to satisfy the next-generation digitised consumer. Whether the use case is as critical as fraud detection at point of sale or simply determining the most appropriate white lab coat to advertise on a research lab PhD’s Google search results, supercomputing with the application of artificial intelligence is redefining the IT landscape of the modern enterprise.

True value creation and investment is shifting away from general purpose compute and rapidly being redirected to establishing supercomputer AI estates, as executives seek to establish differentiation.

In the very near future, it will not be uncommon for enterprises to be commissioning Petaflop scale supercomputers as part of their future digitisation strategy. These supercomputers will allow enterprises to strip complex industry demands down to mature digitised services and products.

No need to invest in a small farm in Midrand or PhD level consulting fees to decipher your cloud provider’s itemised service billing. Thanks to the combination of emerging technologies, enterprises can adopt a cloud-native supercomputer AI platform at roughly the size of a small household oven.

In our next press release, we will explore the building blocks of a cloud native AI data centre.

NVIDIA DGX A100 – 5 Petaflops in 6 Rack Units.

Data Sciences Corporation is a leading IT solutions provider and emerging technologies systems integrator. Contact us for more information about our NVIDIA AI/ML supercomputers for the enterprise.

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META AI Research SuperCluster uses Pure Storage and NVIDIA

Daniela — Tue, 08 Mar 2022 08:48:12 +0000

Metaverse AI

Read time 2min 30sec

META has just announced its brand-new AI Research SuperCluster, or RSC, for short! It will aim to be the largest supercomputer in the world as the phased expansion progresses through 2022.

The primary objective of this supercomputer is to provide 5 Exaflops of compute power to develop next-generation AI models that will be critical in the evolution of metaverse technologies being researched by Meta. For example, AI models in computer vision (CV), natural language processing (NLP) and speech recognition are foundational in an augmented reality world. The next level would be powering these models in real-time on a global scale. This will require quintillions of operations per second.

Not only will the metaverse require real-time processing of raw data, but it will need advanced forms of AI learning, including complex self-supervised learning, where algorithms learn from unlabelled examples.

Using Pure Storage and NVIDIA Technologies

META selected best-of-breed technologies to build this ultimate supercomputer. These include:

760 NVIDIA DGX A100 systems totalling 6080 GPUs in the Compute Layer;
NVIDIA 200Gb/s Quantum InfiniBand network switches in the Networking Layer;
175 petabytes of Pure Storage FlashArray for primary storage;
10 petabytes of Pure Storage FlashBlade for object storage; and
46 petabytes of Penguin Computing for cache storage.

Data Sciences Corporation is a proud partner of Pure Storage and NVIDIA and has delivered AI Supercomputer solutions for key South African organisations, enabling their AI journeys.

In 2020, Pure Storage awarded Data Sciences Corporation with the coveted EMEA Partner of the Year award for our continued excellence in technology leadership.
In 2021 NVIDIA awarded Data Sciences Corporation the prestigious Preferred Partner status for NVIDIA Data Centre and Cloud AI solution. We are currently the only NVIDIA partner in South Africa that holds this status for delivering the first NVIDIA DGX A100 supercomputer solution in Africa.

Please feel free to contact us on our website to learn more. www.datasciences.co.za

Check out the AI RSC video here:

https://www.youtube.com/watch?app=desktop&v=fZnykn1tDSE&feature=youtu.be

Link to Pure Storage announcement:

https://www.purestorage.com/company/newsroom/press-releases/pure-partners-with-meta-on-ai-research-supercluster.html

Link to Meta announcement:

https://www.purestorage.com/company/newsroom/press-releases/pure-partners-with-meta-on-ai-research-supercluster.html

Link to NVIDIA DGX A100:

https://datasciences.co.za/nvidia/

https://www.nvidia.com/en-us/data-center/dgx-a100/

Link to Data Sciences Corporation LinkedIn profile for NVIDIA videos:

https://www.linkedin.com/company/data-sciences-corporation/videos/

Please follow us on LinkedIn to get future information!

Data Sciences Corporation is a leading IT solutions provider and emerging technologies systems integrator. Contact us for more information about our Pure Storage and NVIDIA AI/ML Supercomputers for the enterprise.

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Building blocks of a cloud-native AI data centre – part I

Daniela — Tue, 08 Mar 2022 08:29:51 +0000

Read time 5min 10sec

The next era of enterprise AI technologies

The cloud revolution has changed the face of IT operations. Business leaders are looking for platforms that serve a broad user base and deliver non-stop services dynamically while providing performance and the lowest cost.

With HPC and supercomputers becoming more prevalent in commercial use cases and forming part of primary compute environments, new supercomputers must be architected to deliver as close to bare-metal performance, but do so in a multi-tenant fashion. Historically supercomputers were designed to run single applications (as discussed in our previous article, ‘Super Compute in Enterprise AI’).

Naturally, it stands to reason that a cloud-native supercomputer is needed to deliver on these demands that are fast becoming de facto table stakes for AI in the enterprise. Cloud-native supercomputer architecture aims to maintain the fastest possible compute, storage and network performance while meeting cloud services requirements, such as least-privilege security policies and workload isolation, data protection and instant, on-demand AI and HPC service. It seems so simple!

Cloud-native supercomputing key ingredients (CPU, GPU, DPU)

Cloud-native computing needs vital ingredients to deliver on those requirements. The core of these is CPUs and GPU accelerators. Without getting too technical, CPUs are the brains of any computing platform and handle all the standard management and precision tasks needed to run a server in AI. They have one weakness: they process all their instructions in serial. That’s where GPUs come to the rescue. GPUs process tasks in parallel and deal with tasks and instructions that don’t have to be precise, but they execute them way faster.

To illustrate, imagine your favourite fast food that you just ordered on your favourite delivery app. Now let’s say that 20 houses in your area also ordered from the same fast-food place. If the delivery service only had one scooter, they would have to deliver the food in serial like a CPU, basically going from one house to another. If they spend five minutes per delivery, it will take 100 minutes to complete the delivery. In contrast, if the delivery service behaved like a GPU, then all 20 orders would be delivered simultaneously by 20 scooters, reducing the delivery time by 95 minutes. (This is a hypothetical scenario, please bear with me!)

Now that you’re wondering what fast food to get, let’s get back into the digital world. CPU with GPU acceleration is key to any AI and HPC success, but these building blocks alone don’t meet the cloud-native requirements set out earlier. So, to move from traditional supercomputing to cloud-native supercomputing, we need the next fundamental building block, the data processing unit (DPU).

Dealing with cloud-native tasks

As brilliant as CPUs and GPUs are in delivering the raw performance that AI workloads need, in a traditional supercomputer architecture, cloud-native tasks would have to take some of that processing power to handle the tasks associated, such as shared platform tasks, security and isolation tasks, data protection tasks and many more infrastructure management and communication tasks that make multi-tenant supercomputing work. The trade-off in AI processing power is potentially significant.

This is where the DPU becomes critical. All the tasks required to enable cloud-native supercomputing are offloaded from the CPU and GPU to the DPU for processing.

Think about our fast food example. Imagine the scooter driver had to package the order and decide which scooter the order needs to go to in order to be delivered to the correct house. Well, not only would there be chaos with mixed-up orders, even if they did get it right, it would take time and impact the delivery time to the customer. The answer is to let the drivers focus on driving, navigating and getting to the house as quickly as possible. The task of isolating the orders, packaging them correctly and getting them to the right scooter is offloaded to an order controller at the fast food place.

In a similar principle, by migrating the cloud-native specific task from the CPU and GPU to the DPU, the architecture ensures maximum performance in executing AI and HPC tasks and the security and isolation required for multi-tenant environments that optimises performance and return on investment.

A NVIDIA BlueField DPU supports offload of security, communications and management tasks to create an efficient cloud-native supercomputer.

The cloud-native supercomputer needs the third brain to build faster and more efficient systems. With traditional supercomputers, the workloads sometimes must wait while the CPU handles a mundane communication task. This is called system noise. The DPU or third brain allows AI to compute tasks and communication and management tasks smoothly.

Today’s AI enterprise needs a turnkey, cloud-native supercomputing solution that can dynamically respond to the demands of its users, delivering resources right-sized to every workload with secure multi-tenancy. Purpose-built for demanding enterprise environments using NVIDIA Bluefield DPUs and NVIDIA Base Command Manager, NVIDIA DGX SuperPOD provides multi-tenancy with safe isolation of users and data, without sacrificing bare-metal performance.

A cloud-native supercomputer, such as NVIDIA DGX SuperPOD, offers innovators access to the highest-performance infrastructure with the same assured security found in the cloud.

Now go collect your food!

Our next article will explore how on-premises cloud-native AI infrastructure complements public cloud AI platforms.

Data Sciences Corporation is a leading IT solutions provider and emerging technologies systems integrator. Contact us for more information about our NVIDIA AI/ML supercomputers for the enterprise.

Watch our video explaining the difference between CPU and GPU here: https://www.linkedin.com/feed/update/urn:li:activity:6853945933231722496/

To learn more about the NVIDIA BlueField DPU watch this video: https://www.linkedin.com/feed/update/urn:li:activity:6856583741754736640/

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Building blocks of a cloud-native AI data centre – part II

Daniela — Tue, 08 Mar 2022 07:51:34 +0000

Werner Coetzee, Business Development Executive at Data Sciences Corporation

Read time 5min 00sec

We took a brief but enjoyable tour of the three compute brains found in modern AI supercomputers in our previous discussions. These are the CPU, GPU and DPU. These brains work together to provide one of the fastest data analytics platforms commercially available to enterprises today. However, the brains are just a part of the platform; we need to go beyond the brain to build a comprehensive cloud-native AI data centre!

Layers upon layers

We must explore the three key building blocks that complement the brain. We will discuss them as layers that build up to a complete platform. As already mentioned, the NVIDIA DGX A100 platform is a 5 Petaflop, Megamind, compute platform that houses the three brains as discussed above. This is the first building block or layer, known as the compute layer.

Network nervous system

Next, we need to connect multiple brains to scale the compute power to match our workload and data demand. To do this, we need to have a sort of “nervous system” network that allows messages to move between the compute brains. In IT, we call this the networking layer. Yes, not very original but most definitely practical.

The networking layer in AI supercomputing can get complex, but we will stick to the high-level concepts for this blog. Essentially, we need to build high-speed data transfer networks that allow all the components to talk to each other and move immense amounts of data from point A to point B, or C, D. In the supercomputer world, we will rely on two types of networks. First, the ubiquitous workhorse of the internet and all-conquering data switcher, commonly knowns as Ethernet. Secondly, the ultra-fast, built for extreme data sizes and parallelised algorithms, aptly named InfiniBand.

The Ethernet network’s primary function is to handle all the everyday traffic between computers and connects the entire AI supercomputer platform to the enterprise’s regular local area network (LAN).

The InfiniBand network handles all the heavy lifting data traffic. It moves vast amounts of actionable data between the computers and ensures the fastest time to travel to all parts of the compute layer. The InfiniBand network also has a unique function whereby it can directly interact with one of the compute brains, the data processing unit (DPU). This means that the InfiniBand network extends that data computing layer into the network layer; this capability is essential in AI supercomputers.

These two network technologies are used to create two or even three super-fast “nervous systems” that facilitate all the data traffic movement between the compute and third layers, which we will delve into next.

Data is brain fuel

Data is not ethereal; it does not lack material substance. Data can be likened to water in a way. Like water, data can flow and move. It can be stored, frozen or locked and evaporate if you are not careful. The point is, data has mass.

Vast amounts of data need to live somewhere. Therefore, it needs to be stored. Hence following in our tradition of unoriginal but functional naming conventions, IT gives you the storage layer. Yes, you guessed right, the storage layer for our cloud-native AI supercomputer platform comprises its own set of brains. A nervous system like networks and a stomach, yes, I may be stretching my body analogy a bit far here, but bear with me.

Technically speaking, we store data on data drives. There are many types of drives that will require a complete blog series of its own. But for our purpose, we will use the term flash storage. Flash storage or SSD-based storage is the fastest data platform commercially available now. As seen in our previous layers, speed is a golden thread that ties all our components together.

So, how does it all work? Well, the AI supercomputer is designed to process vast amounts of data. We must change the data’s state from being stored to being moved quickly, and enterprise-grade storage platforms are designed to do just that. The same InfiniBand nervous system network that carries data between compute brains can also fetch the data from the storage platform. The brain demands the data at a phenomenal speed. This means that we need super-speed Flash storage to feed the brain! (Somewhere in here is a vague but valuable connection with the stomach converting food into energy that the brain/body needs to function optimally.)

As Data Sciences Corporation, data is in our blood and we offer various Flash-based data platforms that are uniquely suited for this type of work. For example, we partner with Pure Storage FlashBlade and VAST Data to provide the best-in-class AI supercomputer storage layer.

We have spent the last two articles exploring the various hardware building blocks to our cloud-native AI data centre. Hopefully, we have given you a high-level insight into what goes into an AI supercomputer.

Data Sciences Corporation is a leading IT solutions provider and emerging technologies systems integrator. Contact us for more information about our NVIDIA AI/ML Supercomputers for the enterprise.

For more information about AI servers for the most complex AI challenges, visit: https://datasciences.co.za/nvidia/.

To enjoy our videos on Going Beyond the Brain: follow this link: https://www.linkedin.com/feed/update/urn:li:activity:6859488966761046016/.

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