Statistical Analysis of Inlining Heuristics in Jikes RVM

Jing Yangjy8y@cs.virginia.edu

Department of Computer Science, University of Virginia

Outline

Introduction Data Set Description Data Analysis, Summarization and

Interpretation Future Work Conclusion

Introduction

Java highly portable programming language portability is often at the cost of execution speed seeking optimizations to improve the performance inlining – one of the heavily used optimizations

reduce overhead associated with the function call code expansion aggressive inlining can hurt performance

Introduction

Introduction

Inlining Heuristic used in Java Virtual Machines to decide whether to

inline or not need to be tuned to achieve the optimal solution current tuning technique

manually – not accurate genetic algorithm – time-consuming due to the large

number of parameters

Introduction

Goal locate parameters used in the inlining heuristic that

actually impact its performance focus on these “effective” parameters

reduce time for tuning process simplify the inlining heuristic to reduce its running

overhead without sacrificing its performance

Data Set Description

Data Set Description

Data Set Description

Data Collection a complete combination of all the values – 500 for each configuration, run the whole SPECjvm98

benchmark (including nine Java programs) – 9 within each run, collect three performance metrics

(compiled code size, compilation time, execution time) – 3

raw data – 500*9*3=13,500 observations form the raw data into different formats for different

statistical analysis

Data Analysis, Summarization and Interpretation

Inside Performances 3 variables – compiled code size, compilation time,

execution time geometric mean from the first seven benchmarks in

SPECjvm98 500 observations standardization before analysis

Data Analysis, Summarization and Interpretation

Principal Component Analysis

Data Analysis, Summarization and Interpretation

First Principal Component explain 71.58% of the total sample variance. correlations between the first principal component

and the three variables

compiled code size/compilation time has a contrast to execution time

accords with our intuition of inlining

Data Analysis, Summarization and Interpretation

Performances versus Parameters 7 variables – four parameters and three

performance metrics 500 observations standardization before analysis

Data Analysis, Summarization and Interpretation

Canonical Correlation Analysis

Data Analysis, Summarization and Interpretation

Only Consider One Performance Metric

Data Analysis, Summarization and Interpretation

Conclusion CALLEE_MAX_SIZE and MAX_INLINE_DEPTH

are important ALWAYS_INLINE_SIZE and CALLER_MAX_SIZE

can be neglected unless only optimizing execution time

Data Analysis, Summarization and Interpretation

Verification classify data from the remaining two benchmarks the more important a parameter is, the better

classification can be performed based on it Fisher's discriminant function

pairwise classifications based on CALLEE_MAX_SIZE multi-population classification based on

CALLEE_MAX_SIZE most separate pairwise classification based on

ALWAYS_INLINE_SIZE, MAX_INLINE_DEPTH and CALLER_MAX_SIZE

Data Analysis, Summarization and Interpretation

Data Analysis, Summarization and Interpretation

Data Analysis, Summarization and Interpretation

Data Analysis, Summarization and Interpretation

Future Work

Verification by Jikes RVM Better Discriminant Function Statistical Method to Find Optimal Solution Program Properties

Conclusion

Locate and Verify the Important Parameters Reduce Time for Tuning Process Simplify the Inlining Heuristic