This first course in parallel programming is designed for students without prior experience in this field but requires knowledge of data structures and operating systems. The course covers the following key areas:
With the increasing number of cores in modern processors, efficient programming for these architectures is vital for future technological advancements. The course consists of lectures and significant practical components, ensuring students gain valuable experience on compute clusters, multi-core CPUs, and massive-core GPUs.
This module provides an introduction to the fundamentals of parallel computing. Students will learn about the necessity of parallel programming in the context of modern computing. Key topics include:
By the end of this module, students will have a solid foundation that prepares them for more advanced topics in subsequent lectures.
This module explores various parallel programming paradigms that empower developers to effectively utilize multiple processing units. Key areas covered include:
Students will engage in discussions and hands-on exercises to grasp how these paradigms influence program structure and performance.
In this module, students will learn about the architectural principles that underlie parallel computing systems. The focus will be on:
This knowledge is crucial for writing efficient parallel programs that take full advantage of underlying hardware capabilities.
This module presents case studies that illustrate the practical applications of parallel architecture. Students will analyze:
By examining these case studies, students will gain insights into best practices and pitfalls in parallel computing.
This module introduces OpenMP, a widely used API in parallel programming. Students will learn:
Hands-on examples and exercises will reinforce learning, enabling students to write parallel code effectively using OpenMP.
This module continues the exploration of OpenMP, delving deeper into its advanced features and capabilities. Topics include:
Students will participate in practical coding sessions to apply these advanced features in real-world scenarios.
This module further extends the study of OpenMP, focusing on more complex programming patterns. Students will explore:
Through hands-on projects, students will learn to optimize their parallel applications for better performance.
In this final module, students will examine the PRAM model of computation and its relevance in parallel programming. Key aspects include:
This foundational knowledge will help students appreciate theoretical underpinnings of parallel algorithms and their practical implications.
The PRAM (Parallel Random Access Machine) model is an essential concept in parallel computing. This module introduces students to the formal definition of PRAM and its significance in understanding parallel algorithms. Key topics include:
By the end of this module, students will have a solid understanding of the PRAM model and its applications in designing efficient parallel algorithms.
This module explores various models of parallel computation and their complexity. Students will learn about:
By exploring these topics, students will gain insights into how different models impact parallel performance and algorithm efficiency.
Memory consistency is crucial for ensuring correct execution of parallel programs. This module covers:
Students will learn how various models affect program behavior and performance in multi-threaded environments.
This module expands on the topic of memory consistency, highlighting performance issues that arise in parallel systems. Key areas of focus include:
Students will learn how to identify and mitigate performance bottlenecks caused by memory consistency constraints.
This module covers the fundamentals of parallel program design. Students will explore key concepts that include:
By the end of this module, participants will be equipped to create robust and efficient parallel programs.
This module introduces students to shared memory and message passing paradigms in parallel computing. Important topics include:
Understanding these paradigms is essential for effective parallel programming and optimizing resource utilization.
This module focuses on the Message Passing Interface (MPI), a standardized method for communication in parallel computing. Key areas covered include:
Students will gain practical skills in employing MPI for developing distributed applications, enhancing their parallel programming capabilities.
This module continues the exploration of MPI, delving into more advanced concepts and techniques. Students will cover:
By the end of this module, students will be adept at maximizing performance and troubleshooting MPI-based applications.
This module continues the exploration of the Message Passing Interface (MPI), a standard for parallel programming. Students will delve deeper into MPI's capabilities, examining various communication techniques essential for effective parallel processes.
Key topics include:
This module introduces algorithmic techniques crucial for parallel programming. Students will learn about various algorithms that leverage concurrency to enhance performance.
Topics to be covered include:
This module continues the discussion on algorithmic techniques, focusing on further optimization strategies and examples of successful implementations in various scenarios.
Students will explore:
This module further elaborates on algorithmic techniques, emphasizing real-time application of learned concepts in practical scenarios.
Key learning points include:
This module introduces CUDA (Compute Unified Device Architecture), a parallel computing platform and programming model developed by NVIDIA. Students will learn how to leverage GPU computing to accelerate applications.
Key topics include:
This module continues the exploration of CUDA, enhancing students' skills in writing more complex kernels and optimizing performance for specific applications.
Topics covered include:
This module further expands on CUDA, with a focus on implementing real-world applications and case studies that utilize CUDA for performance enhancements.
Students will learn about:
This module concludes the CUDA series, emphasizing the latest developments and future directions in GPU programming. Students will explore emerging trends and technologies.
Topics include:
This module continues the exploration of CUDA programming, focusing on advanced features and optimization techniques. Students will learn how to:
By the end of this module, students will have practical experience in enhancing CUDA applications and understanding best practices for parallel programming.
This module further extends students' knowledge of CUDA programming, emphasizing real-world applications and problem-solving strategies. Topics covered will include:
Students will gain hands-on experience through coding assignments and projects, reinforcing their understanding of effective CUDA programming.
This module continues the CUDA journey by introducing more complex concepts and techniques. Students will explore:
Through practical assignments, students will implement dynamic parallelism and understand its advantages for performance improvement.
This module introduces students to essential algorithms for merging and sorting in parallel computing. Key topics include:
Students will implement these algorithms in practical exercises, gaining insights into their performance and scalability.
This module continues the exploration of merging and sorting algorithms, delving into more complex scenarios. Topics covered will include:
Students will work on projects that challenge them to implement and optimize these algorithms in practical settings.
This module further builds upon the concepts of merging and sorting, focusing on the integration of these techniques in parallel applications. Key areas of study include:
Students will engage in coding exercises that allow them to apply these techniques effectively in real-world scenarios.
This module continues to advance students' knowledge of algorithms, focusing on further complexities and advanced techniques. Students will cover:
Students will engage in collaborative projects that emphasize teamwork and the application of theoretical knowledge in practical situations.
This final module wraps up the course by summarizing the key concepts learned throughout the course. Students will review:
Students will also present their final projects, demonstrating their comprehensive understanding of parallel computing principles.
This module focuses on the critical concepts of lower bounds, lock-free synchronization, and load stealing in parallel computing. Students will learn about:
Through hands-on exercises, participants will implement these concepts and analyze their impact on performance in various computing environments.
This module delves into the intersection of lock-free synchronization and graph algorithms in parallel programming. Key topics include:
Students will gain practical experience by coding these algorithms and assessing their efficiency in multi-threaded settings.