1. Srinivas Neginhal Anantharaman Kalyanaraman CprE 585: Survey Project
Methodologies for Performance Simulation of Super-scalar OOO processors
Srinivas Neginhal
Anantharaman Kalyanaraman
CprE 585: Survey Project

2. Architectural Simulators
Explore Design Space
Evaluate existing hardware, or Predict performance of proposed hardware
Designer has control
Functional Simulators:
Model architecture (programmers’ focus)
Eg., sim-fast, sim-safe
Performance Simulators:
Model microarchitecture (designer’s focus)
Eg., cycle-by-cycle (sim-outoforder)

3. Simulation Issues
Real-applications take too long for a cycle-by-cycle simulation
Vast design space:
Design Parameters:
code properties, value prediction, dynamic instruction distance, basic block size, instruction fetch mechanisms, etc.
Architectural metrics:
IPC/ILP, cache miss rate, branch prediction accuracy, etc.
Find design flaws + Provide design improvements
Need a “robust” simulation methodology !!

4. Two Methodologies HLS BBDA Hybrid: Statistical + Symbolic REF:
HLS: Combining Statistical and Symbolic Simulation to Guide Microprocessor Designs. M. Oskin, F. T. Chong and M. Farrens. Proc. ISCA
BBDA
Basic block distribution analysis
REF:
Basic Block Distribution Analysis to Find Periodic Behavior and Simulation Points in Applications. T. Sherwood, E. Perelman and B. Calder. Proc. PACT

5. HLS: An Overview A hybrid processor simulator HLS
Statistical Model
HLS
Performance Contours spanned by design space parameters
Symbolic Execution
What can be achieved?
Explore design changes in architectures and compilers that would be impractical to simulate using conventional simulators

6. HLS: Main Idea Statistical Profiling Application code
Synthetically generated code
Application code
Statistical Profiling
Instruction stream, data stream
Structural Simulation of FU, issue pipeline units
Code characteristics:
basic block size
Dynamic instruction distance
Instruction mix
Architecture metrics:
Cache behavior
Branch prediction accuracy

7. Statistical Code Generation
Each “synthetic instruction” contains the following parameters based on the statistical profile:
Functional unit requirements
Dynamic instruction distances
Cache behavior

8. Validation of HLS against SimpleScalar
For varying combinations of design parameters:
Run original benchmark code on SimpleScalar (use sim-outoforder)
Run statistically generated code on HLS
Compare SimpleScalar IPC vs. HLS IPC

9. Validation: Single- and Multi-value correlations
IPC vs. L1-cache hit rate
For SPECint95:
HLS Errors are within 5-7% of the cycle-by-cycle results !!

10. HLS: Code Properties Basic Block Size vs. L1-Cache Hit Rate
Correlation suggests that:
Increasing block size helps only when L1 cache hit rate is >96% or <82%

11. HLS: Value Prediction GOAL: Break True Dependency
Stall Penalty for mispredict vs. Value Prediction Knowledge
DID vs. Value predictability

12. HLS: Conclusions
Low error rate only on SPECint95 benchmark suite. High error rates on SPECfp95 and STREAM benchmarks
Findings: by R. H. Bell et. Al, 2004
Reason:
Instruction-level granularity for workload
Recommended Improvement:
Basic block-level granularity

13. Basic Block Distribution Analysis
Basic Block Distribution Analysis to Find Periodic Behavior and Simulation Points in Applications.
T. Sherwood, E. Perelman and B. Calder.
Proc. PACT

14. Introduction Program Execution Initialization Goal Approach Period
To capture large scale program behavior in significantly reduced simulation time.
Approach
Find a representative subset of the full program.
Find an ideal place to simulate given a specific number of instructions one has to simulate
Accurate confidence estimation of the simulation point.
Initialization
Simulation Points
Period
Program Execution

15. Program Behavior
Program behavior has ramifications on architectural techniques.
Program behavior is different in different parts of execution.
Initialization
Cyclic behavior (Periodic)
Cyclic Behavior is not representative of all programs.
Common case for compute bound applications.

16. BBDA Basics
Fast profiling is used to determine the number of times a basic block executes.
Behavior of the program is directly related to the code that it is executing.
Profiling gives a basic block fingerprint for that particular interval of time.
The interval chosen is ideally a representative of the full execution of the program.
Profiling information is collected in intervals of 100 million instructions.

17. Basic Block Vector B1 B2 BD BBV for Interval i:
Frequency
Interval i …
BBV = Fingerprint of an interval
Varying size intervals
A BBV collected over an interval of N times 100 million instructions is a BBV of duration N. Bx

18. Target BBV BBVs are normalized Target BBV Objective
Each element divided by the sum of all elements.
Target BBV
BBV for the entire execution of the program.
Objective
Find a BBV of smallest duration “similar” to Target BBV.

19. Basic Block Vector Difference
Difference between BBVs
Euclidean Distance
Manhattan Distance

20. Basic Block Difference Graph
Plot of how well each individual interval in the program compares to the target BBV.
For each interval of 100 million instructions, we create a BBV and calculate its difference from target BBV.
Used to
Find the end of initialization phase.
Find the period for the program.

21. Basic Block Difference Graph

22. Initialization Initialization is not trivial.
Important to simulate representative sections of the initialization code.
Detection of the end of the initialization phase is important.
Initialization Difference Graph
Initial Representative Signal - First quarter of BB Difference graph.
Slide it across BB difference graph.
Difference calculated at each point for first half of BBDG.
When IRS reaches the end of the initialization stage on the BB difference graph, the difference is maximized.

23. Initialization

24. Period Period Difference Graph
Period Representative Signal
Part of BBDG, starting from the end of initialization to ¼th the length of program execution.
Slide across half the BBDG.
Distance between the minimum Y-axis points is the period.
Using larger durations of a BBV creates a BBDG that emphasizes larger periods.

25. Period

26. Summary of Results
IPC of chosen period vs. IPC of the full execution  Differed by 5%
BBV based technique (to be continued…)

27. Characterizing Program Behavior Through Clustering
Automatically characterizing Large Scale Program Behavior.
T. Sherwood, E. Perelman, G. Hamerly and B. Calder.
ASPLOS 2002

28. Clustering Approach #1 P1 #2 P2 N BBVs Clustering … … #K Pk Clusters
Multiple Simulation Points … … #K Pk
Clusters

29. Clustering (k-means)
Goal is to divide a set of points into groups such that points within each group are similar to one another by a desired metric.
Input: N points in D-dimensional space
Output: A partition of k clusters
Algorithm:
Randomly choose k points as centroids (initialization)
Compute cluster membership of each point based on its distance from each centroid
Compute new centroid for each cluster
Iterate steps 2 and 3 until convergence
Runtime complexity affected by the “curse of dimensionality”

30. Random Projection Reduce the dimension of the BBVs to 15
Dimension Selection
Dimension Reduction
Random Linear Projection.

31. BBDA: Conclusions
BBDA provides better sensitivity and lower performance variation in phases
Other related work such as instruction working set technique provides higher “stability”