1. DISSERTATION RESEARCH PLAN Mitesh Meswani

2. Outline  Dissertation Research Update  Previous Approach and Results  Modified Research Plan  Identifying Resources  Identifying Signatures  Performance Counters for profiling  Representative tracing and validation

3. Previous Methodology  Trace Selection: Trace the steady state execution of the benchmark suite using CPI for measuring representativeness, One trace per benchmark.  Simulate the traces for different SMT knob settings recording the best setting for each pair  Use regression modeling techniques to generate an analytical prediction model to predict best settings for a pair  Prove model effectiveness for predicting settings for traces from other benchmarks

4. Recap of Previous Results  Models using Decision Trees for SPEC CPU2000 and Stream  Prediction of SMT mode: 97.5%  Prediction of SMT Thread Priority: 83%

5. Modified Plan Summary  Represent the use of relevant shared resources by a benchmark  Identify signatures of shared resource usage within benchmarks using performance counters  Use traces that represent signatures of shared resource usage that can cover 80% of the benchmarks execution  Finally, identify the best SMT knob settings of the representative traces

6. Shared Resources  Shared Resources (seven): TLB, Cache Memory (L2, L3), Branch Unit, FP Unit, FXU Unit, Compare- register Unit, Branch prediction hardware (history table)  How many resources to consider? :- Analyze current traces to eliminate resources contribute less than a threshold value to cycles spent in shared resources.  Compare-register unit is not significant  Branch unit is also not significant

7. Signatures  How many? :-  A resource may have mild, moderate, or high contribution, to cycles spent in shared resources  Idea: If we have five resources, equal contribution would mean 100/5 = approx 20% of cycles per resource, using this as basis  Mild (1% to 15%)  Moderate: 16% to 24%  High : Greater than 24%

8. Finding Signatures  Profile the benchmark execution to find cycles spent in the monitored shared resources  Using performance counters sample the counters periodically  Categorize the benchmark execution (SPEC CPU2000) in one of the possible permutation

9. Finding signatures Continued  Profiling benchmark execution:  Only six counters allowed per execution  What are the Counts for a sample period? :-Merge them from different executions ?  Use the highest sampling rate ?

10. Perf Counters to collect data  Identified Counters  FP: Completion stalls due to FPU (CMPLU_STALLS_FPU)  FXU: Completion stalls due to FXU (CMPLU_STALLS_FXU)  Derived Counters:  LSU Stalls= Total Stalls in LSU – Stalls due to d-cache miss – stalls due to d-tlb miss

11. Perf Counters to collect data continued  Unsolved TLB:  Total d-tlb misses, Total i-tlb misses, miss resolution sites not known  Total Cycles spent for accessing d-tlb known, includes cost of hits and misses  Caches  L2, L3 hit for data and instruction known,  Maybe greater than actual penalty, execution overlaps misses, or miss down misspredicted branches  Maybe use d-cache miss penalty and i-cache miss penatly on POWER5 which are counted only if completion is stalled.  Branch History  Affects prediction, Counter available to count cycles missprediction stalls completion

12. Representative Traces  Collect traces if required, that represent the signatures found in benchmark profiling  Use the performance data from simulation of single traces to verify the signatures  Collect data for evaluating SMT-knobs on representative traces

13. Validation  Use Scientific applications to verify if they are covered by signatures for 80% of their execution  TO DO Identify test applications.