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A strong advanced knowledge of Parallel and Distributed Computing can significantly boost the career path of any computer scientist who wants to be distinguished. Given the complexity and diverse range of applications in Parallel and Distributed Computing, TECH has entrusted a team of experts to prepare all the program's content.

Therefore, computer scientists will encounter topics dedicated to communication and coordination in computer systems, the analysis and programming of parallel algorithms, and distributed systems in computing, among other pertinent subjects. This content is written with a modern and innovative perspective, leveraging the accumulated experience of the teaching staff.

Thus, computer scientists who complete this program have a decisive advantage in projecting their careers towards the development of applications or systems in the fields of: climate, health, big data, cloud computing or blockchain. Furthermore, given the advanced nature of the syllabus, students have the opportunity to pursue a research project in the field of computer science or other related areas.

Moreover, the program is offered entirely online, eliminating the need for physical attendance in classes and removing the constraints of a predetermined schedule. Computer scientists have the freedom to distribute the course load according to their individual interests, enabling them to strike a balance between studying for their Master's degree and managing their personal or professional responsibilities.

Enroll now to explore the latest developments in Parallel Computing in cloud environments and Distributed Computing-oriented programming”

This Professional master’s degree in Parallel and Distributed Computing contains the most complete and up-to-date program on the market. The most important features include:

• The program includes the development of practical cases presented by experts in Parallel and Distributed Computing.The contents are created with graphics, schematics, and practical examples, providing relevant information on the disciplines that are essential for professional practice
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You will receive guidance throughout the program from the teaching team, consisting of professionals with extensive experience in Parallel and Distributed Computing”

The program's teaching staff comprises professionals from the sector who bring their valuable work experience to the program. In addition, renowned specialists from leading societies and prestigious universities are also part of the teaching team.

The program offers multimedia content developed using the latest educational technology. This content provides professionals with a contextual and situated learning environment. Through simulated environments, professionals can engage in immersive education that prepares them for real-life situations.

The design of this program focuses on Problem-Based Learning, in which professionals must try to solve the different professional practice situations that are introduced to them throughout the academic year.To facilitate the learning process, students will be supported by an innovative interactive video system developed by renowned and experienced experts.

As a student, you will receive comprehensive support from TECH, the world's largest online academic institution. You will have access to the latest educational technology"

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To promote optimal study and skill acquisition,TECH has integrated the most effective pedagogical methodology into this program. Through the utilization of relearning techniques, students experience a significant reduction in the time required to acquire essential knowledge within the program. This accelerated learning is further reinforced by a wealth of audiovisual materials, supplementary readings, and practical exercises that aid in solidifying understanding across all subject matters.

Practical exercises that are based on real cases. These exercises are meticulously crafted by our experienced teacher who have developed detailed videos to guide you through the entire process”

Module 1. Parallelism in Parallel and Distributed Computing

1.1. Parallel Processing

1.1.1. Parallel Processing
1.1.2. Parallel Processing in Computing. Purpose
1.1.3. Parallel Processing. Analysis

1.2. Parallel System

1.2.1. The Parallel System
1.2.2. Levels of Parallelism
1.2.3. Parallel System

1.3. Processor Architectures

1.3.1. Processor Complexity
1.3.2. Processor Architecture. Mode of Operation
1.3.3. Processor Architecture. Memory Organization

1.4. Networks in Parallel Processing

1.4.1. Mode of Operation
1.4.2. Control Strategy
1.4.3. Switching Techniques
1.4.4. Topology

1.5. Parallel Architectures

1.5.1. Algorithms
1.5.2. Coupling
1.5.3. Communication

1.6. Performance of Parallel Computing

1.6.1. Performance Evolution
1.6.2. Performance Measures
1.6.3. Parallel Computing Study Cases

1.7. Flynn’s Taxonomy

1.7.1. MIMD: memoria compartida
1.7.2. MIMD: memoria distribuida
1.7.3. MIMD: sistemas híbridos
1.7.4. Data Flow

1.8. Forms of Parallelism: TLP (Thread Level Paralelism)

1.8.1. Forms of Parallelism: TLP (Thread Level Paralelism)
1.8.2. Coarse grain
1.8.3. Fine grain
1.8.4. SMT

1.9. Forms of Parallelism: DLP (Data Level Paralelism)

1.9.1. Forms of Parallelism: DLP (Data Level Paralelism)
1.9.2. Short Vector Processing
1.9.3. Vector Processors

1.10. Forms of Parallelism: ILP (Instruction Level Paralelism)

1.10.1. Forms of Parallelism: ILP (Instruction Level Paralelism)
1.10.2. Segmented Processors
1.10.3. Superscalar Processor
1.10.4. Very Long Instruction Word (VLIW) Processor

Module 2. Parallel Decomposition in Parallel and Distributed Computing

2.1. Parallel Decomposition

2.1.1. Parallel Processing:
2.1.2. Architecture
2.1.3. Supercomputers

2.2. Parallel Hardware and Parallel Software

2.2.1. Serial Systems
2.2.2. Parallel Hardware
2.2.3. Parallel Software
2.2.4. Input and Output
2.2.5. Performance

2.3. Parallel Scalability and Recurring Performance Issues

2.3.1. Parallelism
2.3.2. Parallel Scalability
2.3.3. Recurring Performance Issues

2.4. Shared Memory Parallelism

2.4.1. Shared Memory Parallelism
2.4.2. OpenMP and Pthreads
2.4.3. Shared Memory Parallelism Examples:

2.5. Graphics Processing Unit (GPU)

2.5.1. Graphics Processing Unit (GPU)
2.5.2. Computational Unified Device Architecture (CUDA)
2.5.3. Unified Computational Device Architecture (CUDA) 2.5.3. Examples:

2.6. Message Passing Systems

2.6.1. Message Passing Systems
2.6.2. MPI. Message Passing Interface
2.6.3. Message Passing Systems. Examples:

2.7. Hybrid Parallelization with MPI and OpenMP

2.7.1. Hybrid Programming
2.7.2. MPI/OpenMP Programming Models
2.7.3. Hybrid Decomposition and Mapping

2.8. MapReduce Computing

2.8.1. Hadoop
2.8.2. Other Computing Systems
2.8.3. Parallel Computing Examples:

2.9. Model of Actors and Reactive Processes

2.9.1. Stakeholder Model
2.9.2. Reactive Processes
2.9.3. Actors and Reactive Processes. Examples:

2.10. Parallel Computing Scenarios

2.10.1. Audio and image processing
2.10.2. Statistics/Data Mining
2.10.3. Parallel Sorting
2.10.4. Parallel Matrix Operations

Module 3. Communication and Coordination in Computing Systems

3.1. Parallel and Distributed Computing Processes

3.1.1. Parallel and Distributed Computing Processes
3.1.2. Processes and Threads
3.1.3. Virtualization
3.1.4. Clients and Servers

3.2. Communication in parallel computing

3.2.1. Parallel computing
3.2.2. Layered Protocols
3.2.3. Communication in parallel computing Typology

3.3. Remote Procedure Calling

3.3.1. Functioning of RPC (Remote Procedure Call)
3.3.2. Parameter Passing
3.3.3. Asynchronous RPC
3.3.4. Remote Procedure. Examples:

3.4. Message-Oriented Communication

3.4.1. Transient Message-Oriented Communication
3.4.2. Persistent Message-Oriented Communication
3.4.3. Message-Oriented Communication. Examples:

3.5. Flow-Oriented Communication

3.5.1. Support for Continuous Media
3.5.2. Flows and Quality of Service
3.5.3. Flow Synchronization
3.5.4. Flow-Oriented Communication. Examples:

3.6. Multicast Communication

3.6.1. Multicast at Application Level
3.6.2. Rumor-Based Data Broadcasting
3.6.3. Multicast Communication. Examples:

3.7. Other Types of Communication

3.7.1. Remote Method Invocation
3.7.2. Web Services / SOA / REST
3.7.3. Event Notification
3.7.4. Mobile Agents

3.8. Name Service

3.8.1. Name Services in Computing
3.8.2. Name Services and Domain Name System
3.8.3. Directory Services

3.9. Synchronization

3.9.1. Clock Synchronization
3.9.2. Logical Clocks, Mutual Exclusion and Global Positioning of Nodes
3.9.3. Choice of Algorithms

3.10. Communication Coordination and Agreement

3.10.1. Coordination and Agreement
3.10.2. Coordination and Agreement Consensus and Problems
3.10.3. Communication and Coordination. Currently

Module 4. Analysis and Programming of Parallel Algorithms

4.1. Parallel Algorithms

4.1.1. Problem Decomposition
4.1.2. Data Dependencies
4.1.3. Implicit and Explicit Parallelism

4.2. Parallel programming paradigms

4.2.1. Parallel programming with shared memory
4.2.2. Parallel programming with distributed memory
4.2.3. Hybrid Parallel Programming
4.2.4. Heterogeneous Computing - CPU + GPU
4.2.5. Quantum Computing New Programming Models with Implicit Parallelism

4.3. Parallel programming with shared memory

4.3.1. Parallel programming models with shared memory
4.3.2. Parallel Algorithms with Shared Memory
4.3.3. Libraries for parallel programming with shared memory

4.4. OpenMP

4.4.1. OpenMP
4.4.2. Running and Debugging Programs with OpenMP
4.4.3. Parallel Algorithms with Shared Memory in OpenMP

4.5. Parallel message-passing programming

4.5.1. Fundamental operations of Message Passing
4.5.2. Communication and collective computing operations
4.5.3. Parallel Message-Passing Algorithms
4.5.4. Libraries for parallel programming with message passing

4.6. Message Passing Interface (MPI)

4.6.1. Message Passing Interface (MPI)
4.6.2. Execution and Debugging of Programs with MPI
4.6.3. Parallel Message Passing Algorithms with MPI

4.7. Hybrid Parallel Programming

4.7.1. Hybrid Parallel Programming
4.7.2. Execution and Debugging of Hybrid Parallel Programs
4.7.3. MPI-OpenMP Hybrid Parallel Algorithms

4.8. Parallel Programming with Heterogeneous Computing

4.8.1. Parallel Programming with Heterogeneous Computing
4.8.2. AIH vs. GPU
4.8.3. Parallel Algorithms with Heterogeneous Computing

4.9. OpenCL and CUDA

4.9.1. OpenCL vs. CUDA
4.9.2. Executing and Debugging Parallel Programs with Heterogeneous Computing
4.9.3. Parallel Algorithms with Heterogeneous Computing

4.10. Design of Parallel Algorithms

4.10.1. Design of Parallel Algorithms
4.10.2. Problem and Context
4.10.3. Automatic Parallelization vs. Manual Parallelization
4.10.4. Problem Partitioning
4.10.5. Computer Communications

Module 5. Parallel Architectures

5.1. Parallel Architectures

5.1.1. Parallel Systems. Classification
5.1.2. Sources of Parallelism
5.1.3. Parallelism and Processors

5.2. Performance of Parallel Systems

5.2.1. Performance Metrics and Measurements
5.2.2. Speed-up
5.2.3. Granularity of Parallel Systems

5.3. Vector Processors

5.3.1. Basic Vector Processor
5.3.2. Interleaved or Interleaved Memory
5.3.3. Performance of Vector Processors

5.4. Matrix Processors

5.4.1. Basic Organization
5.4.2. Programming in Matrix Processors
5.4.3. Programming in Matrix Processors. Practical Example

5.5. Interconnection Networks

5.5.1. Interconnection Networks
5.5.2. Topology, Flow Control and Routing
5.5.3. Interconnection Networks. Classification According to Topology

5.6. Multiprocessors

5.6.1. Multiprocessor Interconnection Networks
5.6.2. Memory and Cache Consistency
5.6.3. Probe Protocols

5.7. Synchronization

5.7.1. Bolts (Mutual exclusion)
5.7.2. P2P Synchronization Events
5.7.3. Global Synchronization Events

5.8. Multicomputers

5.8.1. Multicomputer Interconnection Networks
5.8.2. Switching Layer
5.8.3. Routing Layer

5.9. Advanced Architectures

5.9.1. Data Stream Machines
5.9.2. Other Architectures

5.10. Parallel and Distributed Programming

5.10.1. Parallel Programming Languages
5.10.2. Parallel Programming Tools
5.10.3. Design Patterns
5.10.4. Concurrency of Parallel and Distributed Programming Languages

Module 6. Parallel Performance

6.1. Performance of Parallel Algorithms

6.1.1. Ahmdal’s Law
6.1.2. Gustarfson’s Law
6.1.3. Performance Metrics and Scalability of Parallel Algorithms

6.2. Comparison of Parallel Algorithms

6.2.1. Benchmarking
6.2.2. Mathematical Analysis of Parallel Algorithms
6.2.3. Asymptotic Analysis of Parallel Algorithms

6.3. Hardware Resource Constraints

6.3.1. Memory
6.3.2. Processing
6.3.3. Communication
6.3.4. Dynamic Resource Partitioning

6.4. Parallel Program Performance with Shared Memory

6.4.1. Optimal Task Partitioning
6.4.2. Thread Affinity
6.4.3. SIMD Parallelism
6.4.4. Parallel Programs with Shared Memory. Examples:

6.5. Performance of Message-Passing Parallel Programs

6.5.1. Performance of Message-Passing Parallel Programs
6.5.2. Optimization of MPI Communications
6.5.3. Affinity Control and Load Balancing
6.5.4. Parallel I/O
6.5.5. Parallel programs by message passing. Examples:

6.6. Performance of Hybrid Parallel Programs

6.6.1. Performance of Hybrid Parallel Programs
6.6.2. Hybrid Programming for Shared/Distributed Memory Systems
6.6.3. Hybrid Parallel Programs. Examples:

6.7. Performance of Programs with Heterogeneous Computation

6.7.1. Performance of Programs with Heterogeneous Computation
6.7.2. Hybrid Programming for Systems with Multiple Hardware Accelerators
6.7.3. Programs with Heterogeneous Computing. Examples:

6.8. Performance Analysis of Parallel Algorithms

6.8.1. Performance Analysis of Parallel Algorithms
6.8.2. Performance Analysis of Parallel Algorithms. Data Science
6.8.3. Performance Analysis of Parallel Algorithms. Recommendations

6.9. Parallel Patterns

6.9.1. Parallel Patterns
6.9.2. Main Parallel Patterns
6.9.3. Parallel Patterns Comparison

6.10. High Performance Parallel Programs

6.10.1. Process
6.10.2. High Performance Parallel Programs
6.10.3. High Performance Parallel Programs Real Uses

Module 7. Distributed Computing Systems

7.1. Distributed Systems

7.1.1. Sistemas Distribuidos (SD)
7.1.2. Proof of the CAP Theorem (or Brewer’s Conjecture)
7.1.3. Fallacies of Distributed Systems Programming
7.1.4. Ubiquitous Computing

7.2. Distributed Systems Features

7.2.1. Heterogeneity
7.2.2. Security/Safety
7.2.3. Scales
7.2.4. Fault Tolerance
7.2.5. Concurrency
7.2.6. Transparency

7.3. Networks and Interconnection of Distributed Networks

7.3.1. Networks and Distributed Systems. Network Performance
7.3.2. Networks Available to Create a Distributed System. Typology
7.3.3. Distributed network protocols vs. Centralized
7.3.4. Interconnection of Networks. Internet

7.4. Communication Between Distributed Processes

7.4.1. Communication Between SD Nodes. Problems and Failures
7.4.2. Mechanisms to Implement Over RPC and RDMA to Avoid Failures
7.4.3. Mechanisms to Implement in the Software to Avoid Failures

7.5. Distributed Systems Design

7.5.1. Efficient Design of Distributed Systems (DS)
7.5.2. Programming Patterns in Distributed Systems (DS)
7.5.3. Service Oriented Architecture (SOA)
7.5.4. Service Orchestration and Microservices Data Management

7.6. Distributed Systems Operation

7.6.1. Systems Monitoring
7.6.2. Implementing an Efficient Logging System in a DS
7.6.3. Monitoring in Distributed Networks
7.6.4. Use of a Monitoring Tool for an SD Prometheus and Grafana

7.7. System Replication

7.7.1. System Replication Typology
7.7.2. Immutable Architecture
7.7.3. Container Systems and Virtualizing Systems as Distributed Systems
7.7.4. Blockchain Networks as Distributed Systems

7.8. Distributed Multimedia Systems

7.8.1. Distributed Exchange of Images and Videos. Problems
7.8.2. Multimedia Object Servers
7.8.3. Network Topology for a Multimedia System
7.8.4. Analysis of Distributed Multimedia Systems: Netflix, Amazon, Spotify, etc
7.8.5. Distributed Multimedia Systems in Education

7.9. Distributed File Systems

7.9.1. Distributed File Sharing. Problems
7.9.2. Applicability of the CAP Theory to Databases
7.9.3. Distributed Web File Systems: Akamai
7.9.4. IPFS Distributed Document File Systems
7.9.5. Sistemas de bases de datos distribuidas

7.10. Enfoques de seguridad en Sistemas Distribuidos

7.10.1. Security in Distributed Systems
7.10.2. Known Attacks on Distributed Systems
7.10.3. Tools for Testing the Security of a DS

Module 8. Parallel Computing Applied to Cloud Environments

8.1. Cloud Computing

8.1.1. State of the Art of the IT Landscape
8.1.2. The “Cloud”
8.1.3. Cloud Computing

8.2. Security and Resilience in the Cloud

8.2.1. Regions, Availability and Failure Zones
8.2.2. Tenant or Cloud account management
8.2.3. Cloud Identity and Access Control

8.3. Cloud Networking

8.3.1. Software-Defined Virtual Networks
8.3.2. Network Components of a Software-Defined Network
8.3.3. Connection with other Systems

8.4. Cloud Services

8.4.1. Infrastructure as a Service
8.4.2. Platform as a Service
8.4.3. Serverless Computing
8.4.4. Software as a Service

8.5. Cloud Storage

8.5.1. Block Storage in the Cloud
8.5.2. Block Storage in the Cloud
8.5.3. Block Storage in the Cloud

8.6. Block Storage in the Cloud

8.6.1. Cloud Monitoring and Management
8.6.2. Interaction with the Cloud: Administration Console
8.6.3. Interaction with Command Line Interface
8.6.4. API-Based Interaction

8.7. Cloud-Native Development

8.7.1. Cloud Native Development
8.7.2. Containers and Container Orchestration Platforms
8.7.3. Continuous Cloud Integration
8.7.4. Use of Events in the Cloud

8.8. Infrastructure as Code in the Cloud

8.8.1. Management and Provisioning Automation in the Cloud
8.8.2. Terraform
8.8.3. Scripting Integration

8.9. Creation of a Hybrid Infrastructure

8.9.1. Interconnection
8.9.2. Interconnection with Datacenter
8.9.3. Interconnection with other Clouds

8.10. High-Performance Computing

8.10.1. High-Performance Computing
8.10.2. Creation of a High-Performance Cluster
8.10.3. Application of High-Performance Computing

Module 9. Models and Formal Semantics. Examine programming approaches focused on distributed computing

9.1. Semantics Data Model

9.1.1. Semantics Data Model
9.1.2. Semantics Data Model. Purposes
9.1.3. Semantics Data Model. Applications

9.2. Semantic Model of Programming Languages

9.2.1. Language Processing
9.2.2. Translation and Interpretation
9.2.3. Hybrid Languages

9.3. Models of Computation

9.3.1. Monolithic Computing
9.3.2. Parallel Computing
9.3.3. Distributed Computing
9.3.4. Cooperative Computing (P2P)

9.4. Parallel Computing

9.4.1. Parallel Architecture
9.4.2. Hardware
9.4.3. Software

9.5. Distribution Models Grid Computing

9.5.1. Grid Computing Architecture
9.5.2. Grid Computing Architecture Analysis
9.5.3. Grid Computing Architecture Applications

9.6. Distributed Model. Cluster Computing

9.6.1. Cluster Computing Architecture
9.6.2. Grid Computing Architecture Analysis
9.6.3. Grid Computing Architecture Applications

9.7. Cluster Computing. Current Tools to Implement Cluster Computing. Hypervisors

9.7.1. Market Competitors
9.7.2. VMware Hypervisor
9.7.3. Hyper-V

9.8. Distribution Models Cloud Computing

9.8.1. Architecture Cloud Computing
9.8.2. Cloud Computing Architecture. Analysis
9.8.3. Cloud Computing Architecture. Applications

9.9. Distribution Models Amazon Cloud Computing

9.9.1. Amazon Cloud Computing Functional Criteria
9.9.2. Amazon Cloud Computing Licensing
9.9.3. Amazon Cloud Computing Reference Architectures

9.10. Distribution Models Microsoft Cloud Computing

9.10.1. Cloud Computing Microsoft. Functional Criteria
9.10.2. Cloud Computing Microsoft. Licensing
9.10.3. Cloud Computing Microsoft. Reference Architectures

Module 10. Parallel and Distributed Computing Applications

10.1. Parallel and Distributed Computing in Today’s Applications

10.1.1. Hardware
10.1.2. Software
10.1.3. Importance of Timing

10.2. Climate. Climate Change

10.2.1. Climate Applications. Data Sources
10.2.2. Climate Applications. Data Volumes
10.2.3. Climate Applications. Real Time

10.3. GPU computación paralela

10.3.1. GPU computación paralela
10.3.2. GPUs vs. CPU. GPU Usage
10.3.3. GPU. Examples:

10.4. Smart Grid. Computing in Power Grids

10.4.1. Smart Grid
10.4.2. Conceptual Models. Examples:
10.4.3. Smart Grid. Example

10.5. Distributed Engine. ElasticSearch

10.5.1. Distributed Engine. ElasticSearch
10.5.2. Architecture with ElasticSearch. Examples:
10.5.3. Distributed Engine. Case Uses

10.6. Big Data Framework

10.6.1. Big Data Framework
10.6.2. Architecture of Advanced Tools
10.6.3. Big Data in Distributed Computing

10.7. Memory Database

10.7.1. Memory Database
10.7.2. Redis Solution. Case Study
10.7.3. Deployment of Solutions With In-Memory Database

10.8. Blockchain

10.8.1. Blockchain Architecture. Components
10.8.2. Collaboration Between Nodes and Consensus
10.8.3. Blockchain Solutions. Implementations

10.9. Distributed Systems in Medicine

10.9.1. Architecture Components
10.9.2. Distributed Systems in Medicine. Operation
10.9.3. Distributed Systems in Medicine. Applications

10.10. Distributed Systems in the Aviation Sector

10.10.1. Architecture Design
10.10.2. Distributed Systems in the Aviation Sector. Component Functionalities
10.10.3. Distributed Systems in the Aviation Sector. Applications

This program acts as the catalyst you need to achieve the well-deserved professional advancement you have diligently worked towards over a significant period of time”