JVM JIT-compiler overview @ JavaOne Moscow 2013
•
33 likes•7,568 views
The document discusses just-in-time (JIT) compilers in the Java Virtual Machine (JVM). It describes how JIT compilers work by compiling bytecode to native machine code during execution based on profiling information. This allows for optimizations like inlining, devirtualization, loop unrolling and eliding unnecessary synchronization that improve performance. The JIT compiler uses feedback from profiling to enable more aggressive optimizations like these.
Report
Share
Related slideshows
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
Recommended for you
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to JVM JIT-compiler overview @ JavaOne Moscow 2013
Similar to JVM JIT-compiler overview @ JavaOne Moscow 2013 (20)
More from Vladimir Ivanov
More from Vladimir Ivanov (9)
Recently uploaded
Recently uploaded (20)
JVM JIT-compiler overview @ JavaOne Moscow 2013
- 1. 1 JIT-compiler in JVM seen by a Java developer Vladimir Ivanov HotSpot JVM Compiler Oracle Corp.
- 2. 2 Agenda § about compilers in general – … and JIT-compilers in particular § about JIT-compilers in HotSpot JVM § monitoring JIT-compilers in HotSpot JVM
- 3. 3 Static vs Dynamic AOT vs JIT
- 4. 4 Dynamic and Static Compilation Differences § Static compilation – “ahead-of-time”(AOT) compilation – Source code → Native executable – Most of compilation work happens before executing § Modern Java VMs use dynamic compilers (JIT) – “just-in-time” (JIT) compilation – Source code → Bytecode → Interpreter + JITted executable – Most of compilation work happens during executing
- 5. 5 Dynamic and Static Compilation Differences § Static compilation (AOT) – can utilize complex and heavy analyses and optimizations – … but static information sometimes isn’t enough – … and it’s hard to rely on profiling info, if any – moreover, how to utilize specific platform features (like SSE 4.2)?
- 6. 6 Dynamic and Static Compilation Differences § Modern Java VMs use dynamic compilers (JIT) – aggressive optimistic optimizations § through extensive usage of profiling info – … but budget is limited and shared with an application – startup speed suffers – peak performance may suffer as well (not necessary)
- 7. 7 JIT-compilation § Just-In-Time compilation § Compiled when needed § Maybe immediately before execution – ...or when we decide it’s important – ...or never?
- 9. 9 JVM § Runtime – class loading, bytecode verification, synchronization § JIT – profiling, compilation plans, OSR – aggressive optimizations § GC – different algorithms: throughput vs. response time
- 10. 10 JVM: Makes Bytecodes Fast § JVMs eventually JIT bytecodes – To make them fast – Some JITs are high quality optimizing compilers § But cannot use existing static compilers directly: – Tracking OOPs (ptrs) for GC – Java Memory Model (volatile reordering & fences) – New code patterns to optimize – Time & resource constraints (CPU, memory)
- 11. 11 JVM: Makes Bytecodes Fast § JIT'ing requires Profiling – Because you don't want to JIT everything § Profiling allows focused code-gen § Profiling allows better code-gen – Inline what’s hot – Loop unrolling, range-check elimination, etc – Branch prediction, spill-code-gen, scheduling
- 12. 12 Dynamic Compilation (JIT) § Knows about – loaded classes, methods the program has executed § Makes optimization decisions based on code paths executed – Code generation depends on what is observed: § loaded classes, code paths executed, branches taken § May re-optimize if assumption was wrong, or alternative code paths taken – Instruction path length may change between invocations of methods as a result of de-optimization / re-compilation
- 13. 13 Dynamic Compilation (JIT) § Can do non-conservative optimizations in dynamic § Separates optimization from product delivery cycle – Update JVM, run the same application, realize improved performance! – Can be "tuned" to the target platform
- 14. 14 Profiling § Gathers data about code during execution – invariants § types, constants (e.g. null pointers) – statistics § branches, calls § Gathered data is used during optimization – Educated guess – Guess can be wrong
- 15. 15 Profile-guided optimization (PGO) § Use profile for more efficient optimization § PGO in JVMs – Always have it, turned on by default – Developers (usually) not interested or concerned about it – Profile is always consistent to execution scenario
- 16. 16 Optimistic Compilers § Assume profile is accurate – Aggressively optimize based on profile – Bail out if we’re wrong § ...and hope that we’re usually right
- 17. 17 Dynamic Compilation (JIT) § Is dynamic compilation overhead essential? – The longer your application runs, the less the overhead § Trading off compilation time, not application time – Steal some cycles very early in execution – Done automagically and transparently to application § Most of “perceived” overhead is compiler waiting for more data – ...thus running semi-optimal code for time being Overhead
- 19. 19 Mixed-Mode Execution § Interpreted – Bytecode-walking – Artificial stack machine § Compiled – Direct native operations – Native register machine
- 20. 20 Bytecode Execution 1 2 34 Interpretation Profiling Dynamic Compilation Deoptimization
- 21. 21 Deoptimization § Bail out of running native code – stop executing native (JIT-generated) code – start interpreting bytecode § It’s a complicated operation at runtime…
- 22. 22 OSR: On-Stack Replacement § Running method never exits? § But it’s getting really hot? § Generally means loops, back-branching § Compile and replace while running § Not typically useful in large systems § Looks great on benchmarks!
- 23. 23 Optimizations
- 24. 24 Optimizations in HotSpot JVM § compiler tactics delayed compilation tiered compilation on-stack replacement delayed reoptimization program dependence graph rep. static single assignment rep. § proof-based techniques exact type inference memory value inference memory value tracking constant folding reassociation operator strength reduction null check elimination type test strength reduction type test elimination algebraic simplification common subexpression elimination integer range typing § flow-sensitive rewrites conditional constant propagation dominating test detection flow-carried type narrowing dead code elimination § language-specific techniques class hierarchy analysis devirtualization symbolic constant propagation autobox elimination escape analysis lock elision lock fusion de-reflection § speculative (profile-based) techniques optimistic nullness assertions optimistic type assertions optimistic type strengthening optimistic array length strengthening untaken branch pruning optimistic N-morphic inlining branch frequency prediction call frequency prediction § memory and placement transformation expression hoisting expression sinking redundant store elimination adjacent store fusion card-mark elimination merge-point splitting § loop transformations loop unrolling loop peeling safepoint elimination iteration range splitting range check elimination loop vectorization § global code shaping inlining (graph integration) global code motion heat-based code layout switch balancing throw inlining § control flow graph transformation local code scheduling local code bundling delay slot filling graph-coloring register allocation linear scan register allocation live range splitting copy coalescing constant splitting copy removal address mode matching instruction peepholing DFA-based code generator
- 25. 25 JVM: Makes Virtual Calls Fast § C++ avoids virtual calls – because they are slow § Java embraces them – and makes them fast – Well, mostly fast – JIT's do Class Hierarchy Analysis (CHA) – CHA turns most virtual calls into static calls – JVM detects new classes loaded, adjusts CHA § May need to re-JIT – When CHA fails to make the call static, inline caches – When IC's fail, virtual calls are back to being slow
- 26. 26 Call Site § The place where you make a call § Monomorphic (“one shape”) – Single target class § Bimorphic (“two shapes”) § Polymorphic (“many shapes”) § Megamorphic
- 27. 27 Inlining § Combine caller and callee into one unit – e.g.based on profile – … or prove smth using CHA (Class Hierarchy Analysis) – Perhaps with a guard/test § Optimize as a whole – More code means better visibility
- 28. 28 Inlining int addAll(int max) { int accum = 0; for (int i = 0; i < max; i++) { accum = add(accum, i); } return accum; } int add(int a, int b) { return a + b; }
- 29. 29 Inlining int addAll(int max) { int accum = 0; for (int i = 0; i < max; i++) { accum = accum + i; } return accum; }
- 30. 30 Inlining and devirtualization § Inlining is the most profitable compiler optimization – Rather straightforward to implement – Huge benefits: expands the scope for other optimizations § OOP needs polymorphism, that implies virtual calls – Prevents naïve inlining – Devirtualization is required – (This does not mean you should not write OOP code)
- 31. 31 JVM Devirtualization § Developers shouldn't care § Analyze hierarchy of currently loaded classes § Efficiently devirtualize all monomorphic calls § Able to devirtualize polymorphic calls § JVM may inline dynamic methods – Reflection calls – Runtime-synthesized methods – JSR 292
- 32. 32 Feedback multiplies optimizations § On-line profiling and CHA produces information – ...which lets the JIT ignore unused paths – ...and helps the JIT sharpen types on hot paths – ...which allows calls to be devirtualized – ...allowing them to be inlined – ...expanding an ever-widening optimization horizon § Result: Large native methods containing tightly optimized machine code for hundreds of inlined calls!
- 33. 33 Loop unrolling public void foo(int[] arr, int a) { for (int i = 0; i < arr.length; i++) { arr[i] += a; } }
- 34. 34 Loop unrolling public void foo(int[] arr, int a) { for (int i = 0; i < arr.length; i=i+4) { arr[i] += a; arr[i+1] += a; arr[i+2] += a; arr[i+3] += a; } }
- 35. 35 Loop unrolling public void foo(int[] arr, int a) { int new_limit = arr.length / 4; for (int i = 0; i < new_limit; i++) { arr[4*i] += a; arr[4*i+1] += a; arr[4*i+2] += a; arr[4*i+3] += a; } for (int i = new_limit*4; i < arr.length; i++) { arr[i] += a; }}
- 36. 36 Lock Coarsening pubic void m1(Object newValue) { syncronized(this) { field1 = newValue; } syncronized(this) { field2 = newValue; } }
- 37. 37 Lock Coarsening pubic void m1(Object newValue) { syncronized(this) { field1 = newValue; field2 = newValue; } }
- 38. 38 Lock Eliding public void m1() { List list = new ArrayList(); synchronized (list) { list.add(someMethod()); } }
- 39. 39 Lock Eliding public void m1() { List list = new ArrayList(); synchronized (list) { list.add(someMethod()); } }
- 40. 40 Lock Eliding public void m1() { List list = new ArrayList(); list.add(someMethod()); }
- 41. 41 Escape Analysis public int m1() { Pair p = new Pair(1, 2); return m2(p); } public int m2(Pair p) { return p.first + m3(p); } public int m3(Pair p) { return p.second;} Initial version
- 42. 42 Escape Analysis public int m1() { Pair p = new Pair(1, 2); return p.first + p.second; } After deep inlining
- 43. 43 Escape Analysis public int m1() { return 3; } Optimized version
- 44. 44 Intrinsic § Known to the JIT compiler – method bytecode is ignored – inserts “best” native code § e.g. optimized sqrt in machine code § Existing intrinsics – String::equals, Math::*, System::arraycopy, Object::hashCode, Object::getClass, sun.misc.Unsafe::*
- 45. 45 HotSpot JVM
- 46. 46 JVMs § Oracle HotSpot § IBM J9 § Oracle JRockit § Azul Zing § Excelsior JET § Jikes RVM
- 47. 47 HotSpot JVM § client / C1 § server / C2 § tiered mode (C1 + C2) JIT-compilers
- 48. 48 HotSpot JVM § client / C1 – $ java –client § only available in 32-bit VM – fast code generation of acceptable quality – basic optimizations – doesn’t need profile – compilation threshold: 1,5k invocations JIT-compilers
- 49. 49 HotSpot JVM § server / C2 – $ java –server – highly optimized code for speed – many aggressive optimizations which rely on profile – compilation threshold: 10k invocations JIT-compilers
- 50. 50 HotSpot JVM § Client / C1 + fast startup – peak performance suffers § Server / C2 + very good code for hot methods – slow startup / warmup JIT-compilers comparison
- 51. 51 Tiered compilation § -XX:+TieredCompilation § Multiple tiers of interpretation, C1, and C2 § Level0=Interpreter § Level1-3=C1 – #1: C1 w/o profiling – #2: C1 w/ basic profiling – #3: C1 w/ full profiling § Level4=C2 C1 + C2
- 53. 53 Monitoring JIT-Compiler § how to print info about compiled methods? – -XX:+PrintCompilation § how to print info about inlining decisions – -XX:+PrintInlining § how to control compilation policy? – -XX:CompileCommand=… § how to print assembly code? – -XX:+PrintAssembly – -XX:+PrintOptoAssembly (C2-only)
- 54. 54 Print Compilation § -XX:+PrintCompilation § Print methods as they are JIT-compiled § Class + name + size
- 55. 55 Print Compilation $ java -XX:+PrintCompilation 988 1 java.lang.String::hashCode (55 bytes) 1271 2 sun.nio.cs.UTF_8$Encoder::encode (361 bytes) 1406 3 java.lang.String::charAt (29 bytes) Sample output
- 56. 56 Print Compilation § 2043 470 % ! jdk.nashorn.internal.ir.FunctionNode::accept @ 136 (265 bytes) % == OSR compilation ! == has exception handles (may be expensive) s == synchronized method § 2028 466 n java.lang.Class::isArray (native) n == native method Other useful info
- 57. 57 Print Compilation § 621 160 java.lang.Object::equals (11 bytes) made not entrant – don‘t allow any new calls into this compiled version § 1807 160 java.lang.Object::equals (11 bytes) made zombie – can safely throw away compiled version Not just compilation notifications
- 58. 58 No JIT At All? § Code is too large § Code isn’t too «hot» – executed not too often
- 59. 59 Print Inlining § -XX:+UnlockDiagnosticVMOptions -XX:+PrintInlining § Shows hierarchy of inlined methods § Prints reason, if a method isn’t inlined
- 60. 60 Print Inlining $ java -XX:+PrintCompilation -XX:+UnlockDiagnosticVMOptions -XX:+PrintInlining 75 1 java.lang.String::hashCode (55 bytes) 88 2 sun.nio.cs.UTF_8$Encoder::encode (361 bytes) @ 14 java.lang.Math::min (11 bytes) (intrinsic) @ 139 java.lang.Character::isSurrogate (18 bytes) never executed 103 3 java.lang.String::charAt (29 bytes)
- 61. 61 Inlining Tuning § -XX:MaxInlineSize=35 – Largest inlinable method (bytecode) § -XX:InlineSmallCode=# – Largest inlinable compiled method § -XX:FreqInlineSize=# – Largest frequently-called method… § -XX:MaxInlineLevel=9 – How deep does the rabbit hole go? § -XX:MaxRecursiveInlineLevel=# – recursive inlining
- 62. 62 Machine Code § -XX:+PrintAssembly § http://wikis.sun.com/display/HotSpotInternals/PrintAssembly § Knowing code compiles is good § Knowing code inlines is better § Seeing the actual assembly is best!
- 63. 63 -XX:CompileCommand= § Syntax – “[command] [method] [signature]” § Supported commands – exclude – never compile – inline – always inline – dontinline – never inline § Method reference – class.name::methodName § Method signature is optional
- 64. 64 What Have We Learned? § How JIT compilers work § How HotSpot’s JIT works § How to monitor the JIT in HotSpot
- 65. 65 “Quantum Performance Effects” Sergey Kuksenko, Oracle today, 13:30-14:30, «San-Francisco» hall “Bulletproof Java Concurrency” Aleksey Shipilev, Oracle today, 15:30-16:30, «Moscow» hall Related Talks