Postgraduate diploma Parallel and Distributed Computing

Parallel and Distributed Computing is undoubtedly the future of advanced computing. The continuing evolution of multi-core processors, the release of increasingly powerful smartphones, and the implementation of more efficient connection networks such as 5G are opening up an impressive range of possibilities. For this reason, it is crucial for computer scientists to possess comprehensive knowledge of Parallel and Distributed Computing, including its potential applications in fields such as climate analysis, power grids, and big data frameworks. This program is, therefore, an ideal option for achieving a distinctive professional boost guaranteed by the quality of its advanced knowledge.

University certificate

duration

6 months

Modality

Online

Schedule

At your own pace

Exams

Online

start date

Credits

18 ECTS

financing up to

7 months

Price(first year)

See price

The world's largest faculty of information technology”

Introduction to the Program

Ponte al día sobre las novedades más importantes en Parallel and Distributed Computing, incluyendo toda la teoría y práctica en torno a la descomposición en paralelo”

especializacion computacion paralela distribuida

La nube ha abierto una infinidad de posibilidades en el mundo de la computación, especialmente cuando se habla sobre Computación Paralela, ya que ha reducido considerablemente el coste de los servicios necesarios, aumentando a su vez la capacidad disponible. Esto, junto con nuevas herramientas y librerías de programación, ha hecho que la Parallel and Distributed Computing esté al alcance de informáticos con ánimo de emprender.

Ya sea para centrarse en un proyecto de cierta envergadura o incluso dedicarse a la investigación computacional, esta Postgraduate diploma recopila en un formato cómodo y accesible los conocimientos más esenciales que debe tener todo informático sobre la Parallel and Distributed Computing.

Todo ello en un formato 100% online en el que se han eliminado las clases presenciales y los horarios prefijados. Todo el temario está disponible en descarga para los alumnos, serán ellos mismos quienes decidan cuándo asumir toda la carga lectiva. El aula virtual está accesible las 24 horas del día, resultando en la mayor flexibilidad para compaginar esta Postgraduate diploma con otras responsabilidades profesionales o personales

Orienta tu carrera hacia la programación más elevada o incluso entornos de investigación académica computacional gracias a esta Postgraduate diploma”

Esta Postgraduate diploma en Parallel and Distributed Computing contiene el programa educativo más completo y actualizado del mercado. Sus características más destacadas son:

El desarrollo de casos prácticos presentados por expertos en Computación Paralela y Distribuida
Los contenidos gráficos, esquemáticos y eminentemente prácticos con los que está concebido recogen una información práctica sobre aquellas disciplinas indispensables para el ejercicio profesional
Los ejercicios prácticos donde realizar el proceso de autoevaluación para mejorar el aprendizaje
Su especial hincapié en metodologías innovadoras
Las lecciones teóricas, preguntas al experto y trabajos de reflexión individual
La disponibilidad de acceso a los contenidos desde cualquier dispositivo fijo o portátil con conexión a internet

Profundizarás en todas las aplicaciones de la Parallel and Distributed Computing, incluyendo blockchain, bases de datos y sistemas distribuidos en medicina”

El programa incluye, en su cuadro docente, a profesionales del sector que vierten en esta capacitación la experiencia de su trabajo, además de reconocidos especialistas de sociedades de referencia y universidades de prestigio.

Su contenido multimedia, elaborado con la última tecnología educativa, permitirá a los profesionales un aprendizaje situado y contextual, es decir, un entorno simulado que proporcionará una capacitación inmersiva programada para entrenarse ante situaciones reales.

El diseño de este programa se centra en el Aprendizaje Basado en Problemas, mediante el cual los profesionales deberán tratar de resolver las distintas situaciones de práctica profesional que se les planteen a lo largo del curso académico. Para ello, contarán con la ayuda de un novedoso sistema de vídeo interactivo realizado por reconocidos expertos.

Conseguirás el empujón de calidad que necesita tu CV para llegar aún más lejos en tu trayectoria profesional”

experto computacion paralela distribuida

Tendrás a tu disposición una biblioteca repleta de recursos multimedia variados, incluyendo vídeos creados por los propios docentes”

Syllabus

By employing the pedagogical methodology of relearning , TECH enables students to engage in their study work more effectively. This approach ensures that the most crucial concepts and fundamentals of Parallel and Distributed Computing are learned naturally and progressively throughout the program. This approach significantly reduces the time spent on the study itself, allowing students to allocate their efforts towards supplementary readings or practical exercises.

A wide range of resources, including in-depth videos, overviews, motivational videos, and case studies, will be available to assist you in gaining a comprehensive understanding of the diverse applications of Parallel and Distributed Computing”

Module 1. Parallel Decomposition in Parallel and Distributed Computing

1.1. Parallel Decomposition

1.1.1. Parallel Processing:
1.1.2. Architecture
1.1.3. Supercomputers

1.2. Parallel Hardware and Parallel Software

1.2.1. Serial Systems
1.2.2. Parallel Hardware
1.2.3. Parallel Software
1.2.4. Input and Output
1.2.5. Performance

1.3. Parallel Scalability and Recurring Performance Issues

1.3.1. Parallelism
1.3.2. Parallel Scalability
1.3.3. Recurring Performance Issues

1.4. Shared Memory Parallelism

1.4.1. Shared Memory Parallelism
1.4.2. OpenMP and Pthreads
1.4.3. Shared Memory Parallelism Examples:

1.5. Graphics Processing Unit (GPU)

1.5.1. Graphics Processing Unit (GPU)
1.5.2. Computational Unified Device Architecture (CUDA)
1.5.3. Unified Computational Device Architecture (CUDA) 2.5.3. Examples:

1.6. Message Passing Systems

1.6.1. Message Passing Systems
1.6.1. MPI. Message Passing Interface
1.6.3. Message Passing Systems. Examples:

1.7. Hybrid Parallelization with MPI and OpenMP

1.7.1. Hybrid Programming
1.7.2. MPI/OpenMP Programming Models
1.7.3. Hybrid Decomposition and Mapping

1.8. MapReduce Computing

1.8.1. Hadoop
1.8.2. Other Computing Systems
1.8.3. Parallel Computing. Examples:

1.9. Model of Actors and Reactive Processes

1.9.1. Stakeholder Model
1.9.2. Reactive Processes
1.9.3. Actors and Reactive Processes. Examples:

1.10. Parallel Computing Scenarios

1.10.1. Audio and image processing
1.10.2. Statistics/Data Mining
1.10.3. Parallel Sorting
1.10.4. Parallel Matrix Operations

Module 2. Parallel Computing Applied to Cloud Environments

2.1. Cloud Computing

2.1.1. State of the Art of the IT Landscape
2.1.2. The “Cloud”
2.1.3. Cloud Computing

2.2. Security and Resilience in the Cloud

2.2.1. Regions, Availability and Failure Zones
2.2.2. Tenant or Cloud Account Management
2.2.3. Cloud Identity and Access Control

2.3. Cloud Networking

2.3.1. Software-Defined Virtual Networks
2.3.2. Network Components of a Software-Defined Network
2.3.3. Connection with other Systems

2.4. Cloud Services

2.4.1. Infrastructure as a Service
2.4.2. Platform as a Service
2.4.3. Serverless Computing
2.4.4. Software as a Service

2.5. Cloud Storage

2.5.1. Block Storage in the Cloud
2.5.2. Block Storage in the Cloud
2.5.3. Block Storage in the Cloud

2.6. Block Storage in the Cloud

2.6.1. Cloud Monitoring and Management
2.6.2. Interaction with the Cloud: Administration Console
2.6.3. Interaction with Command Line Interface
2.6.4. API-Based Interaction

2.7. Cloud-Native Development

2.7.1. Cloud Native Development
2.7.2. Containers and Container Orchestration Platforms
2.7.3. Continuous Cloud Integration
2.7.4. Use of Events in the Cloud

2.8. Infrastructure as Code in the Cloud

2.8.1. Management and Provisioning Automation in the Cloud
2.8.2. Terraform
2.8.3. Scripting Integration

2.9. Creation of a Hybrid Infrastructure

2.9.1. Interconnection
2.9.2. Interconnection with Datacenter
2.9.3. Interconnection with other Clouds

2.10. High-Performance Computing

2.10.1. High-Performance Computing
2.10.2. Creation of a High-Performance Cluster
2.10.3. Application of High-Performance Computing

Module 3. Parallel and Distributed Computing Applications

3.1. Parallel and Distributed Computing in Today's Applications

3.1.1. Hardware
3.1.2. Software
3.1.3. Importance of Timing

3.2. Climate. Climate Change

3.3.1. Climate Applications. Data Sources
3.3.2. Climate Applications. Data Volumes
3.3.3. Climate Applications. Real Time

3.3. GPU Parallel Computing

3.3.1. GPU Parallel Computing
3.3.2. GPUs vs. CPU. GPU Usage
3.3.3. GPU. Examples:

3.4. Smart Grid. Computing in Power Grids

3.4.1. Smart Grid
3.4.2. Conceptual Models. Examples:
3.4.3. Smart Grid. Example

3.5. Distributed Engine. ElasticSearch

3.5.1. Distributed Engine. ElasticSearch
3.5.2. Architecture with ElasticSearch. Examples:
3.5.3. Distributed Engine. Case Uses

3.6. Big Data Framework

3.6.1. Big Data Framework
3.6.2. Architecture of Advanced Tools
3.6.3. Big Data in Distributed Computing

3.7. Memory Database

3.7.1. Memory Database
3.7.2. Redis Solution. Case Study
3.7.3. Deployment of Solutions With In-Memory Database

3.8. Blockchain

3.8.1. Blockchain Architecture. Components
3.8.2. Collaboration Between Nodes and Consensus
3.8.3. Blockchain Solutions. Implementations

3.9. Distributed Systems in Medicine

3.9.1. Architecture Components
3.9.2. Distributed Systems in Medicine. Operation
3.9.3. Distributed Systems in Medicine. Applications

3.10. Distributed Systems in the Aviation Sector

3.10.1. Architecture Design
3.10.2. Distributed Systems in the Aviation Sector. Functionalities of the components
3.10.3. Distributed Systems in the Aviation Sector. Applications

Gain access to the finest educational technology provided by TECH, the world's largest online academic institution”

Postgraduate Diploma in Parallel and Distributed Computing

The Postgraduate Diploma in Parallel and Distributed Computing is a postgraduate educational program designed to qualify professionals specialized in the programming and design of high-capacity computer systems. The advancement of technology has allowed the emergence of computing solutions to improve the performance of systems and processes. Parallel and distributed computing is a discipline that deals with the construction of software and hardware structures for parallel work of systems, thereby optimizing their performance. In this context, TECH Global University, offers the Postgraduate Diploma in Parallel and Distributed Computing, a quality educational experience for students interested in expanding their knowledge in the programming and design of high capacity computer systems. The program focuses on qualifying University Experts in the use and implementation of advanced programming tools, such as parallel and distributed processing, GPU programming and cloud computing, also in hardware design itself.

Take your career to another level at TECH

Students will also have the opportunity to work on practical projects, designing IT solutions tailored to the specific needs of companies or institutions. This will allow them to apply the knowledge acquired during the academic program and face real situations in the working world. In short, the Postgraduate Diploma in Parallel and Distributed Computing is an excellent option for those interested in developing specialized skills to work with high capacity computing systems. The depth and variety of the programs, as well as the opportunity to work on hands-on projects. At TECH Global University we offer 100% online education so you can manage your time as you wish, and we have the best teaching staff, come and study with us.