1. Exploring Core Designs for Chip Multiprocessors
Allison Holloway
Matthew Allen

2. Outline
Motivation
Hypotheses
Methodology
Results
Conclusions

3. Motivation What should core of a CMP look like?
Workloads: commercial, scientific
OOO wide-issue superscalar?
Tradeoffs: Performance, Power, Area, Complexity

4. Hypotheses
Commercial workloads will not benefit much from OOO / wide-issue
Scientific workloads will benefit significantly from OOO / wide-issue
OOO & wide-issue will be less beneficial for larger scale systems
Augmenting an in-order processor with non-blocking caches will close OOO gap

5. Methodology Simulator: Multifacet, Ruby, Opal (OOO)
In-order processor model
Looked at Simics functional – not comparable
Restrict Opal to in-order issue
Register renaming not removed
Limitations:
Can’t recompile code for scheduling
Does not model UltraSPARC issue rules

6. Methodology Workloads Issues Commercial: Apache, SPECjbb, OLTP, Zeus
Scientific: Barnes-Hut, Ocean
Issues
No 4 processor simulation
No cache warmup files

7. Methodology Baseline configuration used
ROB, instruction window, and # functional units halved for 2-wide processor

8. Results
OOO vs. in-order provides more performance benefit than widening issue from 2 to 4
Tolerating cache misses is the key

10. Results
Hypothesis 1: Commercial workloads will not benefit much from OOO / wide-issue
~30% speedup
Hypothesis 2: Scientific workloads will benefit significantly from OOO / wide-issue
~60% speedup
Commercial workloads DO benefit from OOO, but not as much as scientific.

12. Results
OOO & wide-issue will be less beneficial for larger scale systems
True, BUT
Workloads don’t scale above 8 processors (except apache)

15. (Non) Results
Hypothesis 4: Augmenting an in-order processor with non-blocking caches will close OOO gap
Simulations still running!

16. Future Work Analyze performance trade-offs
vs. power? vs. area?
4 processor runs (if possible)
Vary # of MSHRs

17. Conclusions
Out-of-order provides substantial benefit over in-order, even for commercial workloads
Other methods for tolerating/reducing cache misses may be effective
Diminishing returns for larger systems, but workloads don’t scale well
Need to consider power and area constraints