|Language of Instruction:||Czech|
|Guarantor:||Jaroš Jiří, doc. Ing., Ph.D., DCSY|
|Lecturer:||Jaroš Jiří, doc. Ing., Ph.D., DCSY|
|Instructor:||Bordovský Gabriel, Ing., DCSY|
Jaroš Jiří, doc. Ing., Ph.D., DCSY
Kadlubiak Kristián, Ing., DCSY
|Faculty:||Faculty of Information Technology BUT|
|Department:||Department of Computer Systems FIT BUT|
| || ||To familiarize students with architecture of the newest processors exploiting the instruction-level, thread-level and data-level parallelism. To clarify the role of a compiler and its cooperation with CPU. To be able to orientate oneself on the processor market, to evaluate and compare various CPUs. Next to familiarize with architecture of graphical processors and its use for acceleration of numerical calculations (GPGPU), and with low-power techniques in processors for mobile applications. |
| || ||The course covers architecture of universal as well as special-purpose processors. Instruction-level parallelism (ILP) is studied on scalar, superscalar and VLIW processors. Then the processors with thread-level parallelism (TLP) are discussed. Data parallelism is illustrated on SIMD streaming instructions and on graphical processors (SIMT). Parallelization of numerical calculations for GPU is also covered (CUDA). Techniques of low-power processors are also explained.|
|Knowledge and skills required for the course:|
| || ||Von Neumann computer architecture, memory hierarchy, programming in assembly language, compiler's tasks and functions|
|Learning outcomes and competences:|
| || ||Overview of processor microarchitecture and its future trends, ability to compare processors and using suitable tools, simulate the influence of changes in their architecture. Get acquainted with processor performance measurement. The knowledge of architecture and hardware support of parallel computation on graphic processors can be directly applied for acceleration of intensive calculations. |
|Syllabus of lectures:|
- Scalar processors. Pipelined instruction processing and compiler asistance
- Superscalar CPU. Dynamic instruction scheduling, branch prediction.
- Advanced superscalar processing techniques: register renaming, data flow through memory hierarchy.
- Optimization of instruction and data fetching. Examples of superscalar CPUs.
- Multi-threaded processors.
- Data parallelism. SIMD extensions and vectorization.
- Architecture of graphics processing units, SIMT programming model.
- CUDA programming language, thread and memory model.
- Synchronisation and reduction on GPU, design and tuning of GPU codes.
- Stream processing, multi-GPU systems, GPU libraries.
- Architecture of many core systems (MIC, Xeon Phi) and their programming.
- VLIW processors. SW pipelining, predication, binary translation.
- Low power processors.
|Syllabus of computer exercises:|
- Performance measurement for sequential codes.
- Vectorisation using OpenMP 4.0.
- CUDA: Memory transfers, simple kernels.
- CUDA: Shared memory.
- CUDA: Texture and constant memory, reduction operation.
|Syllabus - others, projects and individual work of students:|
- Performance evaluation and code optimization using OpenMP 4.0
- Acceleration of computational job using CUDA 8.0
- Baer, J.L.: Microprocessor Architecture. Cambridge University Press, 2010, 367 s., ISBN 978-0-521-76992-1
- Hennessy, J.L., Patterson, D.A.: Computer Architecture - A Quantitative Approach. 5. vydání, Morgan Kaufman Publishers, Inc., 2012, 493 s., ISBN: 978-0-12-383872-8
- Kirk, D., and Hwu, W.: Programming Massively Parallel Processors: A Hands-on Approach, Elsevier, 2010, s. 256, ISBN: 978-0-12-381472-2
- Jeffers, J., and Reinders, J.: Intel Xeon Phi Coprocessor High Performance Programming, 2013, Morgan Kaufmann, p. 432), ISBN: 978-0-124-10414-3
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- Missed labs can be substituted in alternative dates (monday or friday)
- There will be a place for missed labs in the last week of the semester.
| || ||Assessment of two projects, 13 hours in total and, computer laboratories and a midterm examination.|
| || ||To get 20 out of 40 points for projects and midterm examination.|