1. Electrical and Computer Engineering Fun Size Your Data: Using Statistical Techniques to Efficiently Compress and Exploit Benchmarking Results David J. Lilja Electrical and Computer Engineering University of Minnesota lilja@umn.edu

2. Electrical and Computer Engineering The Problem We can generate heaps of data But its noisy Too much to understand or use efficiently Heaps o data 445 446 397 226 388 3445 188 1002 47762 432 54 12 98 345 2245 8839 77492 472 565 999 1 34 882 545 4022 827 572 597 364 … Benchmark programs

3. Electrical and Computer Engineering A Solution Statistical design of experiments techniques Compress complex benchmark results Exploit the results in interesting ways Extract new insights Demonstrate using Microarchitecture-aware floorplanning Benchmark classification

4. Electrical and Computer Engineering Why Do We Need Statistics? Draw meaningful conclusions in the presence of noisy measurements Noise filtering Aggregate data into meaningful information Data compression Heaps o data 445 446 397 226 388 3445 188 1002 47762 432 54 12 98 345 2245 8839 77492 472 565 999 1 34 882 545 4022 827 572 597 364 …

5. Electrical and Computer Engineering Why Do We Need Statistics? Draw meaningful conclusions in the presence of noisy measurements Noise filtering Aggregate data into meaningful information Data compression Heaps o data 445 446 397 226 388 3445 188 1002 47762 432 54 12 98 345 2245 8839 77492 472 565 999 1 34 882 545 4022 827 572 597 364 …

6. Electrical and Computer Engineering Design of Experiments for Data Compression 445 446 397 226 388 3445 188 1002 47762 432 54 12 98 345 2245 8839 77492 472 565 999 1 34 882 545 4022 827 572 597 364 … ABC V1 V2 V3 V4 Effects of each input A, B, C Effects of interactions AB, AC, BC, ABC

7. Electrical and Computer Engineering Types of Designs of Experiments Full factorial design with replication O(v m ) experiments = O(4 3 ) Fractional factorial designs O(2 m ) experiments = O(2 3 ) Multifactorial design (P&B) O(m) experiments = O(3) Main effects only – no interactions m-factor resolution x designs k O(2 m ) experiments = k O(2 3 ) Selected interactions ABC V1 V2 V3 V4

8. Electrical and Computer Engineering Example: Architecture-Aware Floor-Planner V. Nookala, S. Sapatnekar, D. Lilja, DAC05.

9. Electrical and Computer Engineering Motivation Imbalance between device and wire delays Global wire delays > system clock cycle in nanometer technology wire Layout

10. Electrical and Computer Engineering Solution Wire-pipelining If delay > a clock cycle insert flip- flops along a wire Several methods for optimal FF insertion on a wire Li et al. [DATE 02] Cocchini et al. [ICCAD 02] Hassoun et al. [ICCAD 02] wire Layout FF But what about the performance impact of the pipeline delays?

11. Electrical and Computer Engineering Impact on Performance Execution time = num-instr * cycles/instr (CPI) * cycle-time Wire-pipelining

12. Electrical and Computer Engineering Impact on Performance Key idea Some buses are critical Some can be freely pipelined without (much) penalty Execution time = num-instr * cycles/instr (CPI) * cycle-time Wire-pipelining

13. Electrical and Computer Engineering Change Objective Function Traditional physical design objectives Minimize area, total wire length, etc. New objective Optimize only throughput critical wires to maximize overall performance Execution time = num-instr * cycles/instr (CPI) * cycle-time Wire-pipelining

14. Electrical and Computer Engineering Conventional Microarchitecture Interaction with Floor Planner Simulation Methodology Physical Design µ-arch Benchmarks CPI info Frequency

15. Electrical and Computer Engineering Microarchitecture-aware Physical Design Incorporate wire-pipelining models into the simulator Extra pipeline stages in processor Simulator needs to adjust operation latencies Simulation Methodology Physical Design µ-arch Benchmarks CPI info Frequency Layout

16. Electrical and Computer Engineering But There are Problems Simulation is too slow 2000-3000 instructions per simulated instruction Numerous benchmark programs to consider Exponential search space Thousands of combinations tried in physical design step Simulation Methodology Physical Design µ-arch Benchmarks CPI info Frequency Layout

17. Electrical and Computer Engineering Design of Experiments Methodology Design of Experiments based Simulation Methodology FloorplanningValidation µ-arch benchmarks Bus, interaction weights Layout MinneSPEC Reduced input sets # Simulations is linear in the number of buses (if no interactions) Frequency

18. Electrical and Computer Engineering Related Floorplanning Work Simulated Annealing (SA) CPI look up table [Liao et al, DAC 04] Bus access ratios from simulation profiles Minimize the weighted sum of bus latencies [Ekpanyapong et al, DAC 04] Throughput sensitivity models for a selected few critical paths Limited sampling for a large solution space [Jagannathan et al, ASPDAC 05] Our approach Design of experiments to identify criticality of each bus

19. Electrical and Computer Engineering Microarchitecture and factors 22 buses 19 factors in experimental design Some factors model multiple buses FetchDecode RUU REG BPRED IL1 DL1 L2ITLB LSQ DTLB IADD1 IADD2 IADD3 IMULT FMULT FADD

20. Electrical and Computer Engineering 2-level Resolution III Design 2-levels for each factor Lowest and highest possible values (range) Latency range of buses Min = 0 Max = Chip corner-corner wire latency 19 factors 32 simulations (nearest power of 2) Captured by a design matrix (32x19) 32 rows - 32 simulations 19 columns - Factor values

21. Electrical and Computer Engineering Experimental setup Nine SPEC 2000 benchmarks MinneSPEC reduced input sets SimpleScalar simulator Floorplanner -- PARQUET Simulated annealing based Objective function Minimize the weighted sum of bus latencies Secondarily minimize aspect ratio and area

22. Electrical and Computer Engineering Comparisons CaseDescription SFPOur statistical floorplanner accAccess ratios from [Ekpanyapong et al, DAC 04] minWLTraditional floorplanning

23. Electrical and Computer Engineering Typical Results for Single Benchmark

24. Electrical and Computer Engineering Averaged Over All Benchmarks Compared to acc 3-7% point improvement Better improvements over acc at higher frequencies SFP-comb SFP (within about 1-3% points)

25. Electrical and Computer Engineering Summary Use statistical design of experiments Compress benchmark data into critical bus weights Used by microarchitecture-aware floorplanner Optimizes insertion of pipeline delays on wires to maximize performance Extend methodology for other critical objectives Power consumption Heat distribution

26. Electrical and Computer Engineering Collaborators and Funders Vidyasagar Nookala Joshua J. Yi Sachin Sapatnekar Semiconductor Research Corporation (SRC) Intel IBM