Stay updated on the latest advancements in Parallel and Distributed Computing, encompassing both theoretical and practical aspects, including parallel decomposition”

The cloud has significantly expanded the possibilities in the realm of computing, particularly in the context of Parallel Computing. It has substantially decreased the cost of required services while simultaneously augmenting the available capacity. The combination of these factors, along with the emergence of new programming tools, has made Parallel and Distributed Computing accessible to ambitious computer scientists. 

Whether the objective is to tackle projects of varying sizes or engage in computational research, this Postgraduate diploma presents the most vital knowledge about Parallel and Distributed Computing in a convenient and accessible format, essential for every computer scientist. 

All of this is delivered in a 100% online format, eliminating the need for face-to-face classes and fixed schedules. The entire program is available for students to download and they will be the ones to decide when to take on the full course load. The virtual classroom is accessible 24 hours a day, offering unparalleled flexibility for students to balance this Postgraduate diploma with their other professional or personal responsibilities. 

Redirect your career towards advanced programming or even computational academic research environments by enrolling in this program”

This Postgraduate diploma in Parallel and Distributed Computing contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Parallel and Distributed Computing
• The graphic, schematic, and practical contents with which they are created, provide practical information on the disciplines that are essential for professional practice 
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies 
• Theoretical lessons, questions for experts and individual reflection work 
• Content that is accessible from any fixed or portable device with an Internet connection 

You will delve into various forms of application of Parallel and Distributed Computing, including blockchain, databases, and distributed systems in the field of medicine”

The program’s teaching staff includes professionals from sector who contribute their work experience to this program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed using the latest educational technology, enables professionals to learn in a contextual and situated learning environment. This simulated environment provides immersive education, specifically designed to prepare individuals for real-life situations.

The program is designed with a focus on Problem-Based Learning, where professionals are required to solve various professional practice situations that are presented to them throughout the academic year. To facilitate this process, the students will receive assistance through an innovative interactive video system developed by renowned and experienced experts in the field.

Indeed, this program will provide the necessary quality boost to your CV, enabling your career to reach new heights”

You will have access to a comprehensive library of diverse multimedia resources, including videos created by the instructors”

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”