Optimizing Compilers CISC 673 Spring 2011 Dynamic Compilation

UUNIVERSITYNIVERSITY OFOF D DELAWARE ELAWARE • • C COMPUTER & OMPUTER & IINFORMATION NFORMATION SSCIENCES CIENCES DDEPARTMENTEPARTMENT

Optimizing CompilersCISC 673

Spring 2011Dynamic Compilation

John CavazosUniversity of Delaware

High Level View of JVM

JVM Interpreter Reads a bytecode from a method “Interprets” the bytecode

Decodes opcode and operands Based on opcodes jumps to some C code Passes operands

Continues reading bytecodes from method until: Call Return Exception

Interpretation Popular approach for high-level languages

Ex, Python, APL, SNOBOL, BCPL, Perl, MATLAB

Useful for memory-challenged environments

Low startup time & space overhead, but much slower than native code execution

MMI (Mixed Mode Interpreter) [Suganauma’01] Fast interpreter implemented in assembler

Dynamic Compilation Techniques

Baseline compiler Translates bytecodes one by one to

machine code Quick compilation

Reduced set of optimizations for fast compilation

Dynamic Compilation Techniques

Full compilation Full optimizations only for selected hot

methods Classic just-in-time compilation

Compile methods to native code on first invocation

Ex, ParcPlace Smalltalk-80, Self-91 Initial high (time & space) overhead for each

compilation Precludes use of sophisticated optimizations (eg.

SSA) Responsible for many of today’s

Interpretation vs JIT

Intepreter Compiler

Initial Overhead Execution

Intepreter Compiler

Execution: 20 time units Execution: 2000 time units

Selective Optimization

Hypothesis: most execution is spent in a small percentage of methods (90/10 rule)

Idea: use two execution strategies1. Interpreter or non-optimizing compiler2. Full-fledged optimizing compiler

Strategy: Use option 1 for initial execution of all methods Profile to find “hot” subset of methods Use option 2 on this subset

Selective Optimization

Intepreter Compiler Selective

Selective opt: compiles 10%-20% of methods, representing 90-99% of execution time

Execution: 20 time units Execution: 2000 time units

Designing a Selective Optimizer AKA: Adaptive Optimization System What is the system architecture?

What are the profiling mechanisms and policies for driving recompilation? How effective are these systems?

Basic Structure of a Dynamic Compiler

ProgramMachine

Structural inlining

unrollingloop perm

Scalar cse

constantsexpressions

Memory scalar repl

Reg. Alloc

Scheduling peephole

Still needs good core compiler - but more

Raw Profile Data

Instrumented code

Basic Structure of a Dynamic Compiler

Compiler subsystem

Optimizations

Interpreter or Simple Translation

Program Executing Program

Profile Processor

History

prior decisionscompile time

ControllerCompilation

decisions

Processed Profile

Method Profiling

Counters Call Stack Sampling Combinations

Method Profiling: Counters Insert method-specific counter on method entry and loop

back edges Counts how often a method is called and approximates how

much time is spent in a method Very popular approach: Self, HotSpot Issues: overhead for incrementing counter can be

significant Not present in optimized code

Method Profiling: Counters

foo ( … ) { fooCounter++; if (fooCounter > Threshold) { recompile( … ); } . . .

Method Profiling: Call Stack Sampling

Periodically record which method(s) on call stack

Approximates amount of time spent in each method

Can be compiled into the code Jikes RVM, JRocket

or use hardware sampling Issues: timer-based sampling is not

deterministic

Method Profiling: Call Stack Sampling

......

Sample

Method Profiling Mixed Combinations

Use counters initially and sampling later on IBM DK for Java

foo ( … ) { fooCounter++; if (fooCounter > Threshold) { recompile( … ); } . . . }

Recompilation Policies

Problem: given optimization candidates, which should be optimized?

Counters: Optimize method that surpass threshold Simple, but hard to tune, doesn’t

consider context Sampling: Optimize method on call

stack top Addresses context issue

Recompilation Policies

Problem: given optimization candidates, which should be optimized?

Call Stack Sampling: Optimize all methods that are sampled

Simple to implement Use cost/benefit model

Seemingly complicated, but easy to engineer Maintenance free Naturally supports multiple optimization

levels

Jikes RVM: Recompilation Policy – Cost/Benefit Model

Define cur, current opt level for method m Exe(j), expected future execution time at level

j Comp(j), compilation cost at opt level j

Choose j > cur that minimizes Exe(j) + Comp(j)

If Exe(j) + Comp(j) < Exe(cur) recompile at level j

Jikes RVM: Recompilation Policy – Cost/Benefit Model

Assumptions Sample data determines how

long a method has executed Method will execute as much in

the future as it has in the past Compilation cost and speedup

are offline averages

Optimization LevelsOptimization

Opt LevelO0

Opt LevelO1

Opt LevelO2

Branch Opts Low Constant Prop / Local CSE

Reorder Code Copy Prop / Tail Recursion

Static Splitting / Branch Opt Med Simple Opts Low

While into Untils / Loop Unroll Branch Opt High / Redundant BR

Simple Opts Med / Load Elim Expression Fold / Coalesce

Global Copy Prop / Global CSE SSA

Optimizations Controlled

Short Running Programs

No FDO, Mar’04, AIX/PPC

Short Running Programs

Steady State

Profiling for What to Do

Myth: Sophisticated profiling is too expensive to perform online

Reality: Well-known technology can collect sophisticated profiles with sampling and minimal overhead

Optimizing Compilers CISC 673 Spring 2011 Dynamic Compilation

Documents

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