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Amdahl’s Law in the Multicore Era Mark D.Hill & Michael R.Marty 2008 ECE 259 / CPS 221 Advanced...

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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
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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 Ham2012. 1. 17

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

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

Amdahl’s Law Original :

Multicore :

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)

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)

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)

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)

Asymmetric Multicore Analysis

Asymmetric multicore allows better speedups For asymmetric multicore, having a nice complex

core is crucial

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

Dynamic Multicore Analysis

Dynamic Multicore provides better speedups

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

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

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?


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