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ZDNet explores the way AI, the cloud, and Big Data in “the AI regeneration era”. The industry dubbed the adjacent infrastructure stack Industry 3.0. Due to the new generation of AI chips, data-centric software tasks should benefit in terms of both operational databases and analytics, as well as in what machine learning (ML) implies.

The easier way of handling Big Data can make companies of all sizes and from any location access new operational horizons. But organizing Big Data processing still involves a series of key decisions, which often call for experienced consultants. The pharmaceutical companies have a number of hard choices in the cloud and on-premise already laid out for them.

Bear in mind that the LASTING Software implementation of analytical algorithms and engines for statistical analysis is at the base of the world leading, FDA-approved solution. Our solution is being used by 93% of the world’s pharmaceutical companies. We will therefore walk you through the main expected pharma industry challenges, inspired by the article we mentioned.


The importance of processing units in accelerating your software workloads

It’s more exactly about GPUs – Graphical Processing Units, the ones that “leverage parallelism” and better keep up with Moore’s law. Their architecture responded to the new challenges well, and now one of the GPU main producers (NVIDIA), announced a set of innovative products with a new architecture.

Hardware to match the upgraded modern request is therefore on the way. But the software is also of importance. Seeing how “how GPUs are currently the AI chip of choice for ML workloads”, the ML libraries come into play.

For detailed recommendations, you may access the original article. What you need to remember is that “GPUs can greatly accelerate workloads that can be broken down in parts to be executed in parallel”. Enough said.


Field Programmable Gateway Arrays and their software scope

FPGAs, simplistically describable as “boards containing low-level chip fundamentals, such as AND and OR gates” are not new. Specific tasks or applications find their correspondence in the hardware description language (HDL) that specifies the FPGAs’ configuration.

Changing the said configuration at need suffers from a certain software layer immaturity. This time, the player that stands out is Intel. By investing into FPGAs R&D, this company tries to catch up on GPUs with a new line of next-gen FPGAs.

Again, the software is crucial. Along with it, the databases and libraries need to support the FPGA-accelerated analytics.  


Once having decided what you want, different choices ensue

To quote our inspirational article of the week: “Should you build your own infrastructure, or use the cloud? Should you wait until offerings become more mature, or jump onboard now and reap the early adopter benefits? Should you go for GPUs, or FPGAs? And then, which GPU or FPGA vendor?”

You may check some of the details and possible answers put forth by the author.

If you can make sense of them, or even get the big picture, then you must be familiar with both hardware and software – kudos to you.

Even so, the activity of your company might need the time to focus on different matters. You can still use a partnership where you can state what you need, infrastructure-wise, and your software solutions partner would deliver it.

Unable to follow the detailed options presented by ZDNet? Then the assumption that your company is a typical pharmaceutic industry entity could be the right one. No need to get stuck trying to learn software-specific notions or trying to make sense by yourself what would the best hardware elements be.


The multitude of available options pushes for the right partnerships. The wisest, poised for efficiency and success organizations learn to delegate tasks. To make next-gen digitization simple and get right to the point where you benefit from it, find the right software solutions partner.

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