Course

Syllabus

Review of Computer Design – Measuring performance Instruction level parallelism – Branch prediction techniques – Static & Dynamic scheduling Speculation – Limits of ILP. Thread-level parallelism, multi-issue, and multi-core processors Homogenous and Heterogenous multicore systems. Shared and Distributed memory -Transaction Memory issues Memory hierarchy design – Cache coherence, Memory wall problem – Advanced Cache Memory design – Virtual Memory, Storage Systems – Ware-house Scale Computers Power optimization- Dynamic Voltage Frequency Scaling – Multi-core architectures for embedded systems Fault Tolerant aspects for multi core systems- Programming environments for multi-core.

Text Books / References

1. Peter S. Pacheco, “An Introduction to Parallel Programming,” Morgan Kauffman/Elsevier, 2011. 
2. Yan Solihin, “Fundamentals of Parallel Multicore Architecture”, CRC Press, 2016. 
3. Georgios Kornaros, “Multi-core Embedded Systems”, CRC Press, Taylor and Francis Group, First edition, 2019. 
4. Victor Alessandrini, , “Shared Memory Application Programming, Concepts and Strategies in Multicore Application Programming”, 1^stEdition,Morgan Kaufmann,2015. 
5. Darryl Gove, “Multicore Application Programming for Windows, Linux, and Oracle Solaris”, Pearson, 2011. 

Pre-requisite: Nil

Course Objectives:

• To introduce computer performance metrics, instruction-level parallelism, and advanced processor design techniques.
• To provide knowledge on multi-core architectures, memory hierarchy, cache coherence, and large-scale storage systems.
• To develop an understanding of power optimization, fault tolerance, and programming environments for multi-core and embedded systems.

Course Outcomes:

CO-PO Mapping: