| Course Unit Code | 460-4117/03 |
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| Number of ECTS Credits Allocated | 4 ECTS credits |
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| Type of Course Unit * | Compulsory |
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| Level of Course Unit * | Second Cycle |
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| Year of Study * | First Year |
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| Semester when the Course Unit is delivered | Winter Semester |
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| Mode of Delivery | Face-to-face |
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| Language of Instruction | Czech |
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| Prerequisites and Co-Requisites | There are no prerequisites or co-requisites for this course unit |
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| Name of Lecturer(s) | Personal ID | Name |
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| KRO080 | prof. Ing. Pavel Krömer, Ph.D. |
| Summary |
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This course provides the students with working knowledge in the area of parallel systems, algorithms and programming.
It concentrates on practical development of parallel codes so that the students can make effective use of the modern parallel hardware, from supercomputers with distributed memory through multicore processors to floating point accelerators and general purpose graphic processing units, to solve computationally extensive problems from different application areas.
An emphasis is put on the introduction to standard parallel paradigms, interfaces, languages, and libraries, as well as on the reflection of the actual development in the field by providing overview of the latest parallel platforms and environments. Parallel programming of distributed memory systems (i.e. the message passing model), shared memory systems (symmetric multiprocessors), and floating-point accelerators will be presented. However, cloud platforms, map-reduce model, and parallel Matlab will be discussed as well.
The tutorials will be devoted to practical design and implementation of parallel algorithms with the help of MPI, OpenMP, UPC, CUDA-C, or parallel Matlab.
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| Learning Outcomes of the Course Unit |
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| An overview of design, implementation, and evaluation of parallel algorithms. Practical use of programming techniques for selected parallel architectures. Working knowledge of parallel systems and programming. In particular, design of parallel algorithms and parallelization of sequential ones. Implementation of parallel algorithms based on message passing. Analysis and evaluation of parallel algorithms. Optimization and improvement of efficiency. |
| Course Contents |
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Lectures:
1. Introduction to parallel programming. Concurrency, pseudoparallelism, parallelism. Processes and threads. Processes and threads from the operating system perspective. SIMD and FMA instructions of modern processors.
2. Sequential vs. parallel programming. Parallel programming caveats. Typical issues of parallel programs. Synchronization and mutual exclusion. Deadlock (definition, properties, conditions, detection, elimination).
3. Classification of parallel systems. Shared memory systems and distributed memory systems. Flynn's taxonomy. Data parallel and task-parallel problems.
4. Shared memory systems programming. Threads in Java, C#, C++11, and Python. The fork-join model and the OpenMP interface.
5. Distributed memory systems programming. Message passing. Posix queues, sockets. The MPI interface, fundamental MPI operations.
6. Other options for distributed memory systems: bulk synchronous parallel and partitioned global address space models.
7. Introduction to the accelerator and co-processor programming. Computation offloading. GPGPU architecture (program organization, memory organization). Data parallelism on the GPU. CUDA platform and CUDA-C language. The OpenACC interface.
8. Parallel algorithm design. Task/channel parallel computation model. Foster's design methodology (partitioning, communication, agglomeration, mapping) and its use.
9. Parallel programming patterns: finding concurrency, algorithm structure, implementation. Typical scenarios: independent tasks, replicable, reduction, divide and conquer, pipeline.
10. Representation and analysis of parallel algorithms (parallel random-access machine, bulk synchronous parallel, informal work depth, etc.). Performance and effectiveness of parallel algorithms.
11. Parallel data structures: stack, queue, pool, linked list, hash table, trees, priorities. Major differences from sequential variants.
12. Algorithms for selected problems: prefix-sum, merging, search (orthogonal trees, sorting networks). Major differences between sequential, naive parallel, and efficient parallel solutions.
13. Data structures and algorithms for parallel search: 2-3 tree, pipelining. Parallel search, insertion, and deletion.
14. Parallel algorithms and data structures for graph problems. Graph traversal, search for shortest paths, connectivity, independent components, etc.
Seminars:
The seminars take place in computer labs and are dedicated to practical examples of the introduced concepts. Students work in the course of the term on the following assignments:
1. Computationally expensive task solved with and without parallelism. The traveling salesman problem: from naive parallelism (brute-force) to more advanced solution with inter-process communication (simple parallel branch-and-bound).
2. Data parallelism and big data processing. Data decomposition, data-parallel approach, vectorization. Parallel matrix operations, clustering.
3. Parallel image processing and the impact of local dependences on parallel algorithms (convolution). Parallel seam carving.
4. A parallel approach to graph algorithms, graphs, and non-linear data structures. Parallel implementation of the iterative version of the PageRank algorithm. |
| Recommended or Required Reading |
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| Required Reading: |
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1. PA I lecture notes
2. Introduction to Parallel Computing: From Algorithms to Programming on State-of-the-Art Platforms. Roman Trobec, Boštjan Slivnik, Patricio Bulić, Borut Robič. Springer Nature Switzerland, 2018
3. Parallel Programming for Multicore and Cluster Systems. Thomas Rauber, Gudula Rünger. Springer-Verlag Berlin Heidelberg, 2013
4. Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering. Ian Foster, Addison Wesley, 1995 |
1. Sylaby k předmětu Paralelní algoritmy I.
2. Introduction to Parallel Computing: From Algorithms to Programming on State-of-the-Art Platforms. Roman Trobec, Boštjan Slivnik, Patricio Bulić, Borut Robič. Springer Nature Switzerland, 2018
3. Parallel Programming for Multicore and Cluster Systems. Thomas Rauber, Gudula Rünger. Springer-Verlag Berlin Heidelberg, 2013
4. Designing and Building Parallel Programs: Concepts and Tools for Parallel Software Engineering. Ian Foster, Addison Wesley, 1995 |
| Recommended Reading: |
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1. The OpenMP Common Core: Making OpenMP Simple Again, Timothy G. Mattson, Yun He, Alice E. Koniges. MIT Press, 2019
2. Using OpenMP-The Next Step: Affinity, Accelerators, Tasking, and SIMD. Ruud van der Pas, Eric Stotzer, and Christian Terboven. MIT Press, 2017
3. Distributed Systems (3rd ed.), Andrew S. Tanenbaum, Maarten van Steen, 2017
4. Distributed Computing Principles, Algorithms, and Systems, Ajay D. Kshemkalyani, Mukesh Singhal, Cambridge, 2008
5. Patterns for parallel programming. Timothy Mattson, Beverly Sanders, Berna Massingill, Addison-Wesley, 2004
6. Introduction to Parallel Computing (2nd Edition). Ananth Grama, George Karypis, Vipin Kumar, Anshul Gupta, Addison-Wesley, 2003 |
1. The OpenMP Common Core: Making OpenMP Simple Again, Timothy G. Mattson, Yun He, Alice E. Koniges. MIT Press, 2019
2. Using OpenMP-The Next Step: Affinity, Accelerators, Tasking, and SIMD. Ruud van der Pas, Eric Stotzer, and Christian Terboven. MIT Press, 2017
3. Distributed Systems (3rd ed.), Andrew S. Tanenbaum, Maarten van Steen, 2017
4. Distributed Computing Principles, Algorithms, and Systems, Ajay D. Kshemkalyani, Mukesh Singhal, Cambridge, 2008
5. Patterns for parallel programming. Timothy Mattson, Beverly Sanders, Berna Massingill, Addison-Wesley, 2004
6. Introduction to Parallel Computing (2nd Edition). Ananth Grama, George Karypis, Vipin Kumar, Anshul Gupta, Addison-Wesley, 2003 |
| Planned learning activities and teaching methods |
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| Lectures, Tutorials |
| Assesment methods and criteria |
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| Task Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
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| Graded credit | Graded credit | 100 | 51 |