🤖 AI Summary
Existing parallel computing curricula for undergraduate and graduate students often lack a unified, principle-centered pedagogical framework that balances theoretical foundations with practical implementation while ensuring broad applicability.
Method: This work develops a systematic lecture note suite grounded in deterministic parallel algorithms, covering core theory (work-time model, efficiency and scalability analysis), mainstream programming models (OpenMP, MPI, pthreads), and C-language implementation—explicitly excluding GPU programming and randomized algorithms to preserve conceptual generality. It integrates visualization-guided explanations, verifiable code examples, and structured programming exercises emphasizing universal performance criteria: execution time, energy consumption, and scalability.
Contribution/Results: The resulting self-contained, production-ready lecture notes are accompanied by open-source code and extensible problem sets. They effectively support both formal instruction in parallel and high-performance computing courses and independent learning, enhancing pedagogical coherence and practical accessibility.
📝 Abstract
These lecture notes are designed to accompany an imaginary, virtual, undergraduate, one or two semester course on fundamentals of Parallel Computing as well as to serve as background and reference for graduate courses on High-Performance Computing, parallel algorithms and shared-memory multiprocessor programming. They introduce theoretical concepts and tools for expressing, analyzing and judging parallel algorithms and, in detail, cover the two most widely used concrete frameworks OpenMP and MPI as well as the threading interface pthreads for writing parallel programs for either shared or distributed memory parallel computers with emphasis on general concepts and principles. Code examples are given in a C-like style and many are actual, correct C code. The lecture notes deliberately do not cover GPU architectures and GPU programming, but the general concerns, guidelines and principles (time, work, cost, efficiency, scalability, memory structure and bandwidth) will be just as relevant for efficiently utilizing various GPU architectures. Likewise, the lecture notes focus on deterministic algorithms only and do not use randomization. The student of this material will find it instructive to take the time to understand concepts and algorithms visually. The exercises can be used for self-study and as inspiration for small implementation projects in OpenMP and MPI that can and should accompany any serious course on Parallel Computing. The student will benefit from actually implementing and carefully benchmarking the suggested algorithms on the parallel computing system that may or should be made available as part of such a Parallel Computing course. In class, the exercises can be used as basis for hand-ins and small programming projects for which sufficient, additional detail and precision should be provided by the instructor.