Processor Architecture

• 39 hrs lectures
• 26 hrs projects

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. 

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.  

Von Neumann computer architecture, memory hierarchy, programming in assembly language, compiler's tasks and functions

• aktuální PPT prezentace přednášek
• http://inst.eecs.berkeley.edu/~cs152/sp13/
• https://www.anandtech.com
• Agner Fog: Software optimization resources
• Intel Architecture Optimization Manual
• Nvidia CUDA SDK Manual

• 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, 1136 s., ISBN 1-55860-596-7. 
• 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

1. Scalar processors. Pipelined instruction processing and compiler asistance
2. Superscalar CPU. Dynamic instruction scheduling, branch prediction.
3. Advanced superscalar processing techniques: register renaming, data flow through memory hierarchy.
4. Optimization of instruction and data fetching. Examples of superscalar CPUs.
5. VLIW processors. SW pipelining, predication, binary translation.
6. Multi-threaded processors. Performance measurement and evaluation (PAPI).
7. Low power processors.
8. Data parallelism. SIMD extensions, SWAR, and SIMT inside GPU.
9. Architecture of graphics processing units.
10. CUDA programming language, thread and memory model.
11. Synchronisation and reduction on GPU, design and tuning of GPU codes.
12. Stream processing, multi-GPU systems, GPU libraries.
13. Architecture of many core systems (MIC, Xeon Phi) and their programming.

To get 20 out of 40 points for projects and midterm examination.

Assessment of three small projects, 26 hours in total (5 + 8 + 13) and a midterm examination.