CMP Design Space Exploration Subject to Physical Constraints Yingmin Li, Benjamin Lee, David Brooks, Zhigang Hu, Kevin Skadron HPCA’06 01/27/2010
Jan 18, 2018
CMP Design Space Exploration Subject to Physical Constraints
Yingmin Li, Benjamin Lee, David Brooks, Zhigang Hu, Kevin Skadron
HPCA’0601/27/2010
Issues
• Power and thermal issues are critical to architectural design
• Design space exploration under physical constraints– core count, pipeline depth, superscalar width,
L2 cache, and voltage and frequency, under area and thermal constraints
• Prior work– exclusively on performance or on single-core
Contributions
• Various new observations for the CMP design given the physical constraints
• Experiment methodology which largely reduces the cost of design space exploration
Approach• There are so many design parameters to
optimize and co-optimize• In this paper, several methods are used
– Modeling and approximation • Performance, power and area scaling• Temperature
– Decoupled core and interconnect/cache simulations. Simulation infrastructures are modular
– Simpoint for representative simulation points
Approach• Modeling
– Formulas to model the power and performance scaling and area for pipeline width and depth
– Temperature - at the granularity of core• Decoupled Simulation
– Use IBM’s Turnandot/PowerTimer to generate L2 cache-access traces – one time cost
– Feed the traces to Zauber, a cache simulator. – Interpolation
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Approaches
• DVFS• Workloads
– SPEC 2000– CPU bound and memory bound
• Constraints– 200 + LR+ MEMORY (Area + Thermal + CPU/Memory)
• Performance and power/performance efficiency
Results
• Without constraints• CPU-bound benchmarks favor deeper
pipelines• Memory-bound benchmarks favor
shallower pipelines
With Area Constraints• To meet the area constraints,
– Workloads• Decrease the cache size for CPU-bound workloads• Decrease the number of cores for memory-bound
workloads– Pipeline dimensions
• Shifting to narrower widths provides greater area impact
• CPU-bound and memory-bound workloads have different, incompatible optima
Results
Optimal Configurations with Varying Pipeline Width, Fixed Depth (18FO4)
Results
Optimal Configurations with Varying Pipeline Depth, Fixed Width (4D)
With Thermal Constraints
• To meet the thermal constraints– Decrease the cache size for CPU-bound
workloads– Decrease the number of cores for Memory-
bound workloads
Thermal Constraints
• Thermal constraints exert great influence on the optimal design configurations
• Thermal constraints should be considered early in the design process
Conclusions
• Joint optimization across multiple design variables is necessary
• Thermal constraints appear to dominate other physical constraints and tend to favor shallower pipelines and narrower cores