IBM Software Group, Compilation Technology © 2007 IBM Corporation Some Challenges Facing Effective Native Code Compilation in a Modern Just-In-Time Compiler Mark Stoodley and Compilation Control Team Testarossa JIT Compiler Team IBM Toronto Lab

IBM Software Group © 2007 IBM Corporation 2Compilation Technology Outline  Identification Challenge –Finding the right methods to compile  Effectiveness Challenge –What is the right way to compile those methods?  Timing Challenge –When is the right time to compile those methods?  Summary

IBM Software Group © 2007 IBM Corporation 3Identification Challenge Identification Challenge: Finding the right methods to compile  What are the right methods? –Methods that will execute a lot in future –Methods that benefit best from compilation  Race with the program itself –Want to discover methods as early as possible –But minimize false positives  Cannot afford much overhead

IBM Software Group © 2007 IBM Corporation 4Identification Challenge Finding Methods with Invocation Count  We detect “hot” interpreted methods via counts –Theory: invoked a lot means program executes it a lot  On the plus side: –Easy to implement, low overhead for interpreter  But : –Frequently invoked methods don’t necessarily consume lots of CPU and may not be good compilation choices e.g. getters and setters invoked a lot but don’t consume CPU e.g. a big matrix multiply invoked less but consumes CPU

IBM Software Group © 2007 IBM Corporation 5Identification Challenge Finding Methods with Sampling  Periodically record top method of active thread stacks  In theory: –M of N ticks in one method  consuming M/N of CPU  In practice: –Depends on application characteristics –Hindered by sampling granularity

IBM Software Group © 2007 IBM Corporation 6Identification Challenge Thinking about Sampling  Most operating systems give sampling period  10ms –10ms  100Hz is Nyquist rate for signal with max frequency 50Hz Of course, we don’t need perfect knowledge of the input “signal”  Processor speeds measured in GHz –Method invocation rate still near or in MHz band  Sampling works best for applications where methods execute for a looooooong time, e.g. apps with hot spots –What about programs that don’t have hot spots?

IBM Software Group © 2007 IBM Corporation 7Identification Challenge Sampling Effectiveness Depends on Platform and Application Characteristics  More stuff happens in 10ms on faster machines than on slower machines –Raw machine speeds vary widely –Virtualized targets are entering the mainstream –Emulated targets seem really slow  Matters more for applications without hot spots than for those with hotspots –Sampling will find hot spots –Sampling frequency too course even on slow machines when no hot spots

IBM Software Group © 2007 IBM Corporation 8Identification Challenge The Identification Challenge  Identify the methods burning CPU as quickly as possible with low overhead –Machine speeds leveling off, but still a wide range of frequencies especially virtualized/emulated platforms –More cores Beware synchronization Cache per thread decreasing: cache footprint critical –Application characteristics evolving New layers of abstraction Easy to write lots of code automatically (visual interface) Increased use of generated classes

IBM Software Group © 2007 IBM Corporation 9Identification Challenge Identification Challenge: Steps We’ve Taken  Sampling framework: relative hotness –Compiles not only triggered by absolute sample count –Instead: how hot is this method compared to all others  Sampling windows adjusted based on method size –More likely to catch samples in big methods –Small hot methods harder to find Make it easier for them to reach compilation trigger  Large set of heuristics in this space

IBM Software Group © 2007 IBM Corporation 10Effectiveness Challenge Effectiveness Challenge: What’s the right way to compile a method?  Depends on many factors: –Application phase –Application requirements –Application characteristics –Availability of resources –System utilization

IBM Software Group © 2007 IBM Corporation 11Effectiveness Challenge Example: Middleware Server + Application  IBM WebSphere Application Server startup: –Loads  15,000 classes (includes DayTrader application) –Executes more than 38,000 methods –Takes 10s – 120s, depending on platform  Application then runs for extended period  Some methods active in start-up and steady-state –Forces trade-off: start-up vs. steady-state performance

IBM Software Group © 2007 IBM Corporation 12Effectiveness Challenge Start-up executes lots of methods a few times  Want to compile many many methods cheaply –Native code performance for highest number of methods –Cheap compilations means better coverage Also methods can appear hot at startup that aren’t important later –Benefit of aggressive optimization is lower Class hierarchy is highly unstable  Careful about methods also active in steady-state –Cheap compilations also means slower code –Will need fast performance for these methods in steady-state

IBM Software Group © 2007 IBM Corporation 13Effectiveness Challenge Steady-State is very different from Start-up  Flat profile, thousand(s) of active methods  Want to compile many methods more aggressively –Best throughput performance –Class hierarchy stabilized so aggressive opts more worthwhile –Application code complexity requires profiling and analysis –Large application scale limits effectiveness of some opts  Tough to find methods that matter due to flat profile –Also to upgrade cheap compilations from startup that matter

IBM Software Group © 2007 IBM Corporation 14Effectiveness Challenge Classic Phase Identification Problem, Right?  Distinguish “start-up” from “steady-state” –Apply different compilation strategy in each phase  Testarossa uses class load phase heuristic –Loading lots of classes means start-up Compilations during class load phase done cheaply (cold) Compilations outside class load phase more aggressive (warm)  Mostly works –But not easy

IBM Software Group © 2007 IBM Corporation 15Effectiveness Challenge Class Load Phase Heuristic Complexities  What does “lots” of classes mean? –Need to establish some threshold –IBM SE JVMs supports 12 platforms Ranging from laptops to mainframes Processor / memory / disk speeds vary substantially from machine to machine and platform to platform Especially with growth of virtualized and emulated targets  Sensitivity –How long to wait before saying in or out? –How long does the decision last?

IBM Software Group © 2007 IBM Corporation 16Effectiveness Challenge More Complexities  Compiles hurt more on some platforms than others –Slower systems seem to pay a higher (relative) price –Easy to miss mistakes because they don’t hurt you everywhere  Not all class loads are equal –Classes vary widely in size –Increased use of generated classes Tools that “precompile” to bytecode

IBM Software Group © 2007 IBM Corporation 17Effectiveness Challenge Some (Annoying) Facts Fact #1: Lots of people care about how fast the application can process transactions (steady-state throughput) Fact #2: Lots of people care about how fast the server can start (startup time) Personal Observation: These two sets intersect less than I’d like Fact #3: Everyone wants what they care about to get better Really Annoying Fact of Life: People complain a LOT if the thing they care about gets worse Really Annoying Fact of Life: Customers rarely care if something works well for their platform but not for another platform

IBM Software Group © 2007 IBM Corporation 18Effectiveness Challenge …And I’ve Simplified the Problem  Other criteria matter too (not just start-up and throughput) –Throughput ramp-up time –Throughput variability from run to run –Maximum application pause –Application utilization –Power and energy consumption are becoming important –Memory for code and used by JIT  All these criteria are also sensitive to the target platform  Matter to varying degrees, from not at all to very very much  Evolving heuristics is really hard

IBM Software Group © 2007 IBM Corporation 19Effectiveness Challenge The Effectiveness Challenge  Properly account for relative importance of a growing set of criteria when generating native code while adapting to the characteristics of increasingly complex applications running on a wide range of targets  We’ve always had to deal with platform sensitivity –Increases the challenge

IBM Software Group © 2007 IBM Corporation 20Effectiveness Challenge Effectiveness Challenge: Steps We’ve Taken  Adaptive class load phase –Tries to adjust for different machine speeds  Ahead-Of-Time (AOT) compilation –Store code in a persistent cache –Avoid compilation cost completely –Can be used to amortize compilation cost across JVMs –Trade-off is lower code quality for Java conformant persistence

IBM Software Group © 2007 IBM Corporation 21Timing Challenge Timing Challenge: When is the Right Time to Compile a Method?  A1: As early as possible (maximize benefit?)  A2: After behaviour has “settled” –Resolve references, class hierarchy stabilized, code paths executed  A3: When application is idle (minimize impact?)  A4: Not “now” –e.g. Real-time applications have utilization expectations –e.g. CPU consumption may cost money  A5: RIGHT NOW!

IBM Software Group © 2007 IBM Corporation 22Timing Challenge The Timing Challenge  Compile methods at the right time to maximize benefit and minimize impact to application –Current approach relies on when we identify a method –“Benefit” comes back to effectiveness

IBM Software Group © 2007 IBM Corporation 23Compilation Technology Timing Challenge: Steps We’ve Taken  Avoid aggressive class hierarchy optimizations during startup  Real-Time: lower compilation thread priority so real-time tasks take precedence  Real-Time: Avoid compiling while GC is active  Dynamic Loop Transfer –Identify interpreted methods “stuck” in a loop –Generate a compiled body that can accept transition from interpreter on the loop back-edge

IBM Software Group © 2007 IBM Corporation 24Compilation Technology Summary  Three big challenges facing effective native code generation in modern JITs: –Identification Challenge: find the right methods to compile –Effectiveness Challenge: compile methods in the right way –Timing Challenge: compile methods at the right time  Complex system: lots of overlap in these challenges  Any functional JIT must deal with these challenges –But degree of success varies!

IBM Software Group © 2007 IBM Corporation 25Compilation Technology Questions?  Mark Stoodley 

IBM Software Group © 2007 IBM Corporation 26Compilation Technology Backup Slides

IBM Software Group © 2007 IBM Corporation 27Compilation Technology When do you find the method?  After program completes üGood knowledge of what methods matter  But no opportunity to improve program execution  Before program executes üMost opportunity to improve execution time  But no knowledge of what to focus on People tend to get stuck on “most opportunity” But how much improvement will native code bring? Answer can depend on when you compile it