Appentra Solutions SL

  • Michal Riha profile
    Michal Riha
    11 September 2020 - updated 3 weeks ago
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Country: 
Spain

What is the innovation?

Parallelware Technology: analysis tool aiming to reduce the burden of making code parallel.

What problem does the innovation solve?

Multicore chips are everywhere but, in order to take advantage of the performance promised by modern multicore CPUs and accelerator devices like Graphical Processing Units (GPUs), software needs to be written with parallelism in mind from the very beginning. The development and maintenance of parallel software is far more complex than sequential software. Bugs related to parallelism, including both race conditions and data movement issues, are difficult to find and fix, and thus they are more likely to go undiscovered. DevOps and Continuous Integration (CI) help to detect them as early as possible while coding, testing and debugging, when fixing them is less expensive. Appentra introduce a new innovative approach that uses static code analysis to reduce parallel software development costs and speed up the parallel software runtime.

How does the innovation solve the problem?

Appentra introduce a new innovative approach that uses static code analysis to reduce parallel software development costs and speed up the parallel software runtime. It is based on two pillars: first, work with experts to publish a set of rules and recommendations that leverage parallel programming best practices; and second, work with software vendors to develop new static code analysis tools specialized in parallelism that integrate seamlessly with other professional software development tools. From 2019, Appentra is involved in a world-wide community effort to create new guidelines for the development of parallel code using C/C++/Fortran targeting multicore CPUs and GPUs. At the same time, Appentra is developing Parallelware Analyzer, a new static code analysis tool specialized in parallelism, which is not appropriately covered by the commercial and open source static code analyzers available today.

Is there any other existing cutting edge solution? If so, how does yours differ?

Not specialized in parallelism. With respect to other static analysis tools the key technological differentiation that enables Parallelware’s capabilities is a unique Artificial Intelligence (AI) engine that behaves as an expert system. Our competitive advantage is centered around two pillars: Speed = we do real-time code analysis Precision = we understand complex code deeply

Tell us about your team?

The APPENTRA team, currently comprises eleven people, has two partners-promoters who have extensive professional experience relevant to the activity of APPENTRA. The team is led by Prof. Manuel Arenaz (45 years old, CEO / CTO), Ph.D. in Computer Engineering and Parallel Computing; Rosa Vázquez (50, CFO), graduated in international trade and has more than 18 years of experience in business development and financial management; Marcos Torres (Busines Developer in Apac), Alejandra Pérez (Deputy CFO) Javier Novo PhD (Head of Engineering and Product) a team of five Daniel Otero, Jaime González, Robert Esclapez, Rui Marques, Diego Alonso computer science engineers experts in High Performance Computing, and Estefanía García a marketing and graphic design assistant. The company has a board of directors formed by the partners-developers and representatives of international and national venture capital funds with shareholdings in APPENTRA.

How big is the market for this innovation?

In this article (7 minutes read) our investor Armilar Ventures Partner explain a few insights on the rise of parallel computing and why invested in Appentra https://medium.com/armilar-venture-partners/a-few-insights-on-the-rise-of-parallel-computing-1a8f1dd9470f. The Enterprise High Performance Computing market, analyzed by Tractica in May 2018, is expected to grow significantly at a CAGR of +29% achieving, by 2025, a market value +$31b. This is probably going to be fueled by AI-driven applications, with a specific example being embedded real-time decision systems.

What EU-funded research project was this innovation developed in?

Project EPEEC.