Title description
Cloud computing and high-performance application programming are revolutionizing the digital and scientific world, driving scalability, efficiency and innovation in all sectors. There is now a great demand for professionals capable of mastering both the flexible and distributed infrastructures in the cloud and the languages, libraries and tools required for the development of High Performance Computing (HPC) and High Throughput Computing (HTC).
In this master, you will not only learn how to design resilient architectures and automate deployments on cloud platforms such as AWS or Azure, but you will also learn how to parallel program and optimize applications that run complex simulations, matrix algorithms, weather models, genetic analysis and other computationally demanding scientific tasks. All this combined with the massive data processing of Big Data and Artificial Intelligence, which require both computing power and efficient infrastructures.
You will explore the operation of advanced hardware devices, such as GPUs and multicore processors, and become familiar with key tools such as OpenMP, CUDA, MPI or Threading Building Blocks. In addition, you will learn how to optimize the performance of sequential code using advanced GNU or Intel OneAPI compilers, and how to deploy these environments on cloud infrastructures adapted to the requirements of each application.
This program prepares you to become a highly qualified professional, able to address current technological challenges from a holistic perspective: from the infrastructure in the cloud to the optimization of high performance software. An essential training to lead research, innovation and digital transformation projects in the academic, scientific and business world.
Objectives of the title
Training of professionals:
With knowledge of the main services offered by today’s most important public providers in the cluster.
With extensive knowledge of cloud platform architectures and programmatic content management and orchestration.
Master the main technologies and tools for the efficient processing of large volumes of data (big data) and AI processes (training, validation and testing), the creation of scalable architectures in the cloud and the use of distributed computing infrastructures.
With extensive knowledge in design and development of efficient and elastic distributed services to be deployed in cloud systems.
With extensive knowledge of parallel algorithms and skills and skills in the use of programming tools and machinery that allow the development of programs on parallel computing systems.
Able to apply parallel computing techniques to solve large dimensional problems and to solve real time problems.
Able to model engineering and scientific problems using High Performance Computing techniques.
Computer Science specialists, with a good understanding of the analysis and application of numerical algorithms, visualization techniques and the way in which algorithms use current data structures and computer architectures, as well as network technologies that allow access to remote computers.
Able to develop applications that guarantee the continuity of its service, even when they are updated.
With a solid base for the study and design of distributed algorithms.
Professional services
This master’s degree opens the door to a wide range of job opportunities, combining specialization in cloud infrastructures with in-depth knowledge of high-performance programming and computational optimization.
- Cloud Architect: Design and management of infrastructure in the public, hybrid or private cloud, optimizing performance, security and scalability.
- DevOps and Automation Engineer: Deployment automation, continuous integration and monitoring in cloud and high performance environments.
- High Performance Computing (HPC) Specialist: Development and optimization of scientific and industrial applications that require complex simulations or massive data processing, using parallel and distributed computing techniques.
- Big Data and Artificial Intelligence Engineer: Design of pipelines for mass data processing in cloud environments, as well as optimization of AI models in high performance platforms.
- Cloud and HPC Platform Administrator: Management of clusters and supercomputers, resource monitoring and optimization of scientific and industrial workloads.
- High Performance Program Developer: Efficient programming using libraries and parallel computing environments (OpenMP, CUDA, MPI), code optimization in multicore and multinode environments.
- Digital Transformation and Cloud Infrastructure Consultant: Strategic advice to companies that migrate their systems and applications to the cloud environment, incorporating HPC solutions for critical applications.
- Researcher in Scientific Computing and Cloud Technologies: Application of advanced programming, optimization and cloud deployment techniques for research projects in areas such as computational physics, bioinformatics, meteorology, etc.
Aimed primarily at
This master’s degree is designed for professionals with training in technical and scientific areas.
- Engineers, graduates in Computer Science, Telecommunications Engineers, Industrial Engineers, Physics and Mathematics graduates.
- Graduates in other engineering degrees who have a degree equivalent in teaching load to 180 ECTS credits, and engineers from other countries with similar characteristics to those mentioned above.
Structure of the master’s program
Credits: 60 ECTS
Compulsory:32 ects |Electives:16 ects |External practices:0 ects |Final Master’s Thesis (TFM):12 ects
Module 1. Core of the Master. Phase I :16 ects obligatory
Matter:Parallel Computing Concepts
Minimum Credits: 4 | Character: Compulsory
Matter:Basic Concepts of Scientific Computing
Minimum Credits: 4 | Character: Compulsory
Matter:Grid Computing Concepts
Minimum Credits: 4 | Character: Compulsory
Matter:Distributed Applications Fundamentals
Minimum Credits: 4 | Character: Compulsory
Module 2. Core of the Master. Phase II :16 ects obligatory
Matter:Parallel and Scientific Computing Methods
Minimum credits: 8 | Character: Compulsory
Matter:Distributed and Grid Computing Methods
Minimum credits: 8 | Character: Compulsory
Module 3. Specialization :16 ects electives
Matter:Applications and Seminars
Minimum Credits: 16 | Character: Optional
Module 4. Master’s thesis :12 ects treballe fi titulació
Matter:Master’s thesis
Minimum credits: 12 | Character: Treballe Fi Qualifications
In-company internships
In-company internships are not compulsory in the master’s program, but students can carry them out according to the corresponding regulations.
In-Company Internship Procedure
Treballe Fi de Màster en Empresa
Students who wish to do their master’s thesis may do so in a company with which there is an agreement. Interested students should contact the master’s coordinator.
Links of interest
Research and access to doctorate
R&D groups in which the professors of the Master’s program participate:
Institutes and Departments where the professors participating in the master’s degree develop R&D&I and technology transfer projects:
Research lines:
- Parallel Computing
- Cloud Technologies
- Distributed Systems and Highly Available Systems
- Computational Sciences
Doctoral School: https://www.upv.es/contenidos/docinf/
Master’s thesis
The Master’s Thesis, of 12 ECTS credits, is mandatory. It must be an original exercise to be carried out individually.
It consists of the development of a basic research work (scientific orientation of initiation to research), or of practical application (professional orientation), in the lines of Parallel and Scientific Computing, or Distributed and Grid Computing, in which the competences acquired in the subjects taken are synthesized and integrated.
The student will have to write a report of the work that, in addition, will have to expose and defend publicly in front of an examining board. In order to be able to present the TFM it is necessary to have passed at least 48 credits of the subjects.
For the evaluation, the quality of the report and presentation in general and, specifically, the originality and relevance for research-oriented work and the methodology and applicability for professional-oriented work will be taken into account by the panel. As a guideline, the duration of the work should be approximately 3 months.
Selection of the Master’s Thesis:
The particular work can be requested within the public offer that exists through the TFM application (Ebrón), available on the UPV Intranet, or it can be agreed directly between a Master’s professor and a student (TFM concerted).
