Thanks to this TECH Postgraduate diploma, you can direct your career towards the most advanced computational research”

In parallel computing, it is essential for computer scientists to master the optimization of the various codes used. This allows them to extract maximum performance from the programming environment they are working with. To possess this ability, it is necessary to have not only knowledge of how to measure the performance of an algorithm or program but also a comprehensive understanding of how different computer systems communicate and coordinate. 

Therefore, this Postgraduate diploma starts by laying the foundations of message-oriented communication, flows, multicast, and other forms of communication in parallel computing Following that, the Postgraduate diploma delves into the most sophisticated methods for analyzing and programming parallel algorithms. The discussion concludes with a comprehensive review of  benchmarking  and the various considerations that need to be taken into account regarding parallel performance. 

All of these courses are conveniently offered in a 100% online format, eliminating the need for students to attend in-person classes or adhere to a pre-set schedule All the course content is available for download from the virtual classroom, enabling students to study conveniently from their preferred devices such as  tablets , computers, or even smartphones. This Postgraduate diploma also offers a decisive advantage for individuals who have demanding personal or professional responsibilities. 

Immerse yourself in state-of-the-art programming and computational performance models, guided by true experts in the field”

This Postgraduate diploma in Advanced Parallel 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 program is designed with graphical, schematic, and highly practical content, which gathers essential information about disciplines that are crucial for the professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies 
• The incorporation of theoretical lessons, interactive question-and-answer sessions with experts, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

You will have at your disposal a large number of didactic and interactive resources that will help you to contextualize all the knowledge imparted"

The program features a teaching staff comprising professionals from the industry who bring their valuable work experience to the training. Additionally, renowned specialists from prestigious reference societies and universities contribute their expertise to further enrich the program. 

The program offers multimedia content developed using the latest educational technology, creating a contextual and immersive learning environment for professionals. This includes a simulated environment designed to provide training in real-life situations. 

The program's design emphasizes Problem-Based Learning, requiring professionals to actively solve various real-world practice situations that are presented to them throughout the academic year. For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts. 

You have the freedom to choose when, where, and how to tackle the entire course load, allowing you to distribute the study material according to your own preferences and schedule"

You can achieve the career goal you deserve with the unwavering support of a teaching team that possesses in-depth knowledge of the job market and the strategies needed for success"

This Postgraduate diploma on parallel computing has been divided into three modules that comprehensively cover all the most advanced information in the field.  Computer scientists will have access to a high-quality reference material that they can refer to even after completing their degree. The concise and well-defined contents of the reference material facilitate easy navigation and comprehensive study of the subject matter. 

The educational method of relearning allows computer scientists to grasp the most important concepts in a natural manner, thereby reducing the need for extensive study hours”

Module 1. Communication and Coordination in Computing Systems

1.1. Parallel and Distributed Computing Processes 

1.1.1. Parallel and Distributed Computing Processes 
1.1.2. Processes and Threads 
1.1.3. Virtualisation 
1.1.4. Clients and Servers 

1.2. Parallel Computing Communication 

1.2.1. Parallel Computing 
1.2.2. Layered Protocols 
1.2.3. Communication in Parallel Computing. Typology 

1.3. Remote Procedure Calling 

1.3.1. Functioning of RPC (Remote Procedure Call) 
1.3.2. Parameter Passing 
1.3.3. Asynchronous RPC 
1.3.4. Remote Procedure. Examples: 

1.4. Message-Oriented Communication 

1.4.1. Transient Message-Oriented Communication 
1.4.2. Persistent Message-Oriented Communication 
1.4.3. Message-Oriented Communication. Examples: 

1.5. Flow-Oriented Communication 

1.5.1. Support for Continuous Media 
1.5.2. Flows and Quality of Service 
1.5.3. Flow Synchronization 
1.5.4. Flow-Oriented Communication. Examples: 

1.6. Multicast Communication 

1.6.1. Multicast at Application Level 
1.6.2. Rumor-Based Data Broadcasting 
1.6.3. Multicast Communication. Examples: 

1.7. Other Types of Communication 

1.7.1. Remote Method Invocation 
1.7.2. Web Services / SOA / REST 
1.7.3. Event Notification 
1.7.4. Mobile Agents 

1.8. Name Service 

1.8.1. Name Services in Computing 
1.8.2. Name Services and Domain Name System 
1.8.3. Directory Services 

1.9. Synchronization 

1.9.1. Clock Synchronization 
1.9.2. Logical Clocks, Mutual Exclusion and Global Positioning of Nodes 
1.9.3. Choice of Algorithms 

1.10. Communication Coordination and Agreement 

1.10.1. Coordination and Agreement 
1.10.2. Coordination and Agreement Consensus and Problems 
1.10.3. Communication and Coordination. Currently 

Module 2. Analysis and Programming of Parallel Algorithms 

2.1. Parallel Algorithms 

2.1.1. Problem Decomposition 
2.1.2. Data Dependencies 
2.1.3. Implicit and Explicit Parallelism 

2.2. Parallel Programming Paradigms 

2.2.1. Parallel Programming with Shared Memory 
2.2.2. Parallel Programming with Distributed Memory 
2.2.3. Hybrid Parallel Programming 
2.2.4. Heterogeneous Computing- CPU + GPU 
2.2.5. Quantum Computing New Programming Models with Implicit Parallelism

2.3. Parallel Programming with Shared Memory 

2.3.1. Models of Parallel Programming with Shared Memory
2.3.2. Parallel Algorithms with Shared Memory 
2.3.3. Libraries for Parallel Programming with Shared Memory 

2.4. OpenMP 

2.4.1. OpenMP 
2.4.2. Running and Debugging Programs with OpenMP 
2.4.3. Parallel Algorithms with Shared Memory in OpenMP 

2.5. Parallel Programming by Message Passing 

2.5.1. Message Passing Primitives 
2.5.2. Communication Operations and Collective Computing 
2.5.3. Parallel Message-Passing Algorithms 
2.5.4. Libraries for Parallel Programming with Message Passing 

2.6. Message Passing Interface (MPI) 

2.6.1. Message Passing Interface (MPI) 
2.6.2. Execution and Debugging of Programs with MPI 
2.6.3. Parallel Message Passing Algorithms with MPI 

2.7. Hybrid Parallel Programming 

2.7.1. Hybrid Parallel Programming 
2.7.2. Execution and Debugging of Hybrid Parallel Programs 
2.7.3. MPI-OpenMP Hybrid Parallel Algorithms 

2.8. Parallel Programming with Heterogeneous Computing 

2.8.1. Parallel Programming with Heterogeneous Computing 
2.8.2. AIH vs. GPU 
2.8.3. Parallel Algorithms with Heterogeneous Computing 

2.9. OpenCL and CUDA 

2.9.1. OpenCL vs. CUDA 
2.9.2. Executing and Debugging Parallel Programs with Heterogeneous Computing 
2.9.3. Parallel Algorithms with Heterogeneous Computing 

2.10. Design of Parallel Algorithms 

2.10.1. Design of Parallel Algorithms 
2.10.2. Problem and Context 
2.10.3. Automatic Parallelization vs. Manual Parallelization 
2.10.4. Problem Partitioning 
2.10.5. Computer Communications 

Module 3. Parallel Performance 

3.1. Performance of Parallel Algorithms 

3.1.1. Amdahl's Law 
3.1.2. Gustarfson's Law 
3.1.3. Performance Metrics and Scalability of Parallel Algorithms 

3.2. Comparison of Parallel Algorithms 

3.2.1. Benchmarking 
3.2.2. Mathematical Analysis of Parallel Algorithms 
3.2.3. Asymptotic Analysis of Parallel Algorithms 

3.3. Hardware Resource Constraints 

3.3.1. Memory 
3.3.2. Processing 
3.3.3. Communication 
3.3.4. Dynamic Resource Partitioning 

3.4. Parallel Program Performance with Shared Memory 

3.4.1. Optimal Task Partitioning 
3.4.2. Thread Affinity 
3.4.3. SIMD Parallelism 
3.4.4. Parallel Programs with Shared Memory. Examples: 

3.5. Performance of Message-Passing Parallel Programs

3.5.1. Performance of Message-Passing Parallel Programs
3.5.2. Optimization of MPI Communications 
3.5.3. Affinity Control and Load Balancing 
3.5.4. Parallel I/O 
3.5.5. Parallel Message Passing Programs Examples: 

3.6. Performance of Hybrid Parallel Programs 

3.6.1. Performance of Hybrid Parallel Programs 
3.6.2. Hybrid Programming for Shared/Distributed Memory Systems 
3.6.3. Hybrid Parallel Programs. Examples: 

3.7. Performance of Programs with Heterogeneous Computation 

3.7.1. Performance of Programs with Heterogeneous Computation 
3.7.2. Hybrid Programming for Systems with Multiple Hardware Accelerators 
3.7.3. Programs with Heterogeneous Computing. Examples: 

3.8. Performance Analysis of Parallel Algorithms 

3.8.1. Performance Analysis of Parallel Algorithms 
3.8.2. Performance Analysis of Parallel Algorithms. Data Science 
3.8.3. Performance Analysis of Parallel Algorithms. Recommendations 

3.9. Parallel Patterns 

3.9.1. Parallel Patterns 
3.9.2. Main Parallel Patterns 
3.9.3. Parallel Patterns Comparison 

3.10. High Performance Parallel Programs 

3.10.1. Process 
3.10.2. High Performance Parallel Programs 
3.10.3. High Performance Parallel Programs Real Uses

The detailed videos, summaries, real case studies, and various exercises provided will serve as essential supplementary materials for your study ofAdvanced Parallel Computing”