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Our Approach

Do IT Systems provides development services of HPC solutions with horizontal or vertical scalability on-premise or cloud that allow the client to achieve his set goals in the most efficient and effective way possible.

Working in constant contact with the client, our development team fully examines the required functionalities and your requests, and implements the best solutions to satisfy all the neds related to the corporate process.




With an experience that varies from small-size clusters to top500 Supercomputers, we can help you in your HPC project, regardless of the sizes.



GPU computing uses a GPU (graphic processing unit) as co-processor to accelerate the CPUs for scientific and technical processing of general use.

Gpu computing


Storage systems that can be configured on line with scale-out and scale-up principles. We will be extremely glad to advise you on this topic and also on the interfaces and protocols most suitable to your individual needs.


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Nowadays, more clients like Bristol-Myers Squibb, FINRA, BP and Autodesk rely on HPC systems in cloud to execute their most critical workloads.

Scalability and speed are the key characteristics of HPC systems. The market is currently offering various flexible and scalable cloud HPC solutions (Microsoft Azure, AWS, Oracle, Google Cloud and many players specialised in HPC of smaller sizes). Identifying the most suitable solution for your needs with the right support to integrate it with your applications, allows you greater efficiency and efficacy, speeding up your time-to-discovery or time-to-market.

Structuring custom projects on these systems is simple with our support. Supercomputers can be used through cloud based on last-generation Intel® and AMD processors and (GPU) NVIDIA accelerators. In addition, on cloud, you can access low-latency and high actual speed object-storage systems. 

With virtually unlimited skills, engineers, researchers and owners of HPC systems can innovate beyond the limits of the local HPC infrastructure. The workloads cover the traditional HPC applications such as genomics, computational chemistry, modelling of the financial risk, computer-assisted engineering, weather forecasting and seismic imaging, as well as new learning machine and deep learning applications.

HPC in cloud offers greater flexibility and quicker installation times compared to local HPC clusters. The flexible configuration and the virtually unlimited scalability allow to increase and reduce the infrastructure based on workloads. Moreover, thanks to the large portfolio of services based on cloud like data analysis, Artificial Intelligence (AI) and Machine Learning (ML), the traditional HPC work flows can be re-defined to innovate more rapidly.



The impact of AI (Artificial Intelligence) on information technology is substantial, as stated by . McKinsey & Company in their “The state of AI in 2020” report. Over 70% of the high tech companies surveyed are currently implementing AI projects in at least one their functions. Likewise, AI continues to be increasingly important to companies worldwide; MarketWatch estimates the global Compund Annual Growth Rate(CAGR) of the AI sector at 46.76% through 2025.

Artificial Intelligence has had a major impact on HPC, as reported by Intel® in its "The Case for Running AI and Analytics on HPC Clusters" Solution Brief. The data-intensive nature of AI workloads and the need to run them in HPC environments means that vendors now speak in terms of "building blocks" that include processors, accelerators, fast storage and network blocks, as well as simulation and modeling focused tools and optimization. Hence many vendors now sell workload-optimized servers as building blocks that simplify cluster configuration and ensure performance.

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