1. Amdahl’s Law in the Multicore Era Mark D.Hill & Michael R.Marty 2008 ECE 259 / CPS 221 Advanced Computer Architecture II Presenter : Tae Jun Ham 2012. 1. 17

2. Outline Summary - Amdahl’s law in the multicore era - Symmetric MC Case - Asymmetric MC Case - Dynamic MC Case Review - Strong Point - Negative Point - Possible Questions

3. Problem  Multicore Chip Design has additional degree of freedom - Total number of Cores - Complexity of the individual core - Multicore Chip Design Style (Symmetric / Asymmetric / Dynamic)  Goal of this paper : To explore the design space of multicore chip and obtaining some useful implication for computer architects

4. Amdahl’s Law  Original :  Multicore :

5. Basic Assumptions  Limited Resource : Area  Resource Unit : BCE(Base Core Equivalence)  Simple Core : Consume : 1 BCE Performance : 1  Complex Core : Consume : r BCEs Performance : perf(r) = sqrt(r)

6. Symmetric Multicore Model  Resource : n BCEs  Each core consumes r BCEs  Total number of core : n/r  Serial Performance : perf(r)  Parallel Performance : perf(r) * (n/r)

7. Symmetric Multicore Analysis  Parallelization is important  rBCEs>1 can be optimal (Complex core is still important even with the diminishing return in performance per area)

8. Asymmetric Multicore Model  Resource : n BCEs  One complex core consumes r BCEs  Other cores consumes 1 BCE  Total number of core : n-r+1  Serial Performance : perf(r)  Parallel Performance : perf(1) * (n-r)+perf(r)

9. Asymmetric Multicore Analysis  Asymmetric multicore allows better speedups  For asymmetric multicore, having a nice complex core is crucial

10. Dynamic Multicore Model  Resource : n BCEs  Forms a r BCEs complex core for sequential operation  Other part consumes 1 BCE  Total number of core : n ( parallel ) / n-r+1 (serial)  Serial Performance : perf(r)  Parallel Performance : n * perf(1) = n

11. Dynamic Multicore Analysis  Dynamic Multicore provides better speedups

12. Strength  Identified the future research direction 1. Increase Parallelism 2. Increase Core Performance 3. Better asymmetric & dynamic multicore design  Derived corollary for Amdahl’s law for multicore cases

13. Limitation  Not very accurate model 1. Limited Resource : combination of power, area and cost 2. Performance Model : can be different from sqrt(r) 3. Need to consider partially parallel portion  Skepticism 1. Can Moore’s law continue till 256 core per chip? 2. Can we really achieve 99.9% parallelization?  Optimal point highly depends on parallel portion. As parallel portion differs among applications, it is hard to determine the best hardware design

14. Future work / Discussions  What would be the appropriate ways to implement dynamic multicore design with HW?  How do we develop a better analytical model for multicore performance?  What would be software challenges for asymmetric multicore or dynamic multicore?  What would be the most power efficient multicore design among three choices presented?