What Does JVM Do

A JVM Does

That???
Cliff Click www.azulsystems.com/blogs

A JVM Does That???
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Been a JVM Engineer for over a decade I'm still amazed at what goes in a JVM Services have increased over time Many new services painfully "volunteered" by naive change in specs

Some JVM Services
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High Quality GC
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Parallel, Concurrent, Collection Low total allocation cost Two JITs, JIT'd Code Management, Profiling Bytecode cost model Locks (synchronization), volatile, wait, notify

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High Quality Machine Code Generation
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Uniform Threading & Memory Model
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Type Safety

Some JVM Services
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Dynamic Code Loading
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Class loading, Deoptimization, re-JIT'ing System.currentTimeMillis Reflection, JNI, JVMTI, JVMDI/JVMPI, Agents

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Quick high-quality Time Access
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Internal introspection services
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Access to huge pre-built library Access to OS
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threads, scheduling, priorities, native code

Too Many Services?
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Where did all this come from? Mostly incrementally added over time The Language, JVM, & Hardware all co-evolved
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e.g. incremental addition of finalizers, JMM, 64-bits Support for high core-count machines

Why Did We Add All These Services?
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Because Illusions Are Powerful Abstractions

The 'V' in JVM
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"Virtual" – Its a Great Abstraction Programmers focus on value-add elsewhere JVM Provides Services The selection of Services is ad-hoc
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Grown over time as needed Some services are unique to Java or the JVM Many services overlap with existing OS services
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But sometimes have different requirements

Agenda
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Introduction (just did that) Illusions We Have Illusions We Think We Have or Wish We Had Sorting Our Illusions Out

Illusion: Infinite Memory
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Garbage Collection – The Infinite Heap Illusion
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Just allocate memory via 'new' Do not track lifetime, do not 'free' GC figures out What's Alive and What's Dead Fewer bugs, quicker time-to-market Just too hard to track liveness otherwise

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Vastly easier to use than malloc/free
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Enables certain kinds of concurrent algorithms
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Illusion: Infinite Memory
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GC have made huge strides in the last decade
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Production-ready robust, parallel, concurrent Still major user pain-point
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Too many tuning flags, GC pauses, etc

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Major Vendor point of differentiation, active dev Throughput varies by maybe 30% Pause-times vary over 6 orders of magnitude
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(Azul GPGC: 100's of Gig's w/10msec) (Stock full GC pause: 10's of Gig's w/10sec) (IBM Metronome: 100's Megs w/10microsec)

Illusion: Bytecodes Are Fast
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Class files are a lousy way to describe programs There are better ways to describe semantics than Java bytecodes
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But we're stuck with them for now Main win: hides CPU details

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Programmers rely on them being "fast" It's a big Illusion: Interpretation is slow JIT'ing brings back the "expected" cost model

Illusion: Bytecodes Are Fast
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JVMs eventually JIT bytecodes
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To make them fast! Some JITs are high quality optimizing compilers
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Amazingly complex beasties in their own rights

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i.e. JVMs bring "gcc -O2" to the masses Tracking OOPs (ptrs) for GC Java Memory Model (volatile reordering & fences) New code patterns to optimize

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But cannot use "gcc"-style compilers directly:
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Illusion: Bytecodes Are Fast
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JIT'ing requires Profiling
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Because you don't want to JIT everything

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Profiling allows focused code-gen Profiling allows better code-gen
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Inline whats hot Loop unrolling, range-check elimination, etc Branch prediction, spill-code-gen, scheduling

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JVMs bring Profiled code to the masses!

Illusion: virtual calls are fast
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C++ avoids virtual calls – because they are slow Java embraces them – and makes them fast
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Well, mostly fast – JIT's do Class Hierarchy Analysis CHA turns most virtual calls into static calls JVM detects new classes loaded, adjusts CHA
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May need to re-JIT

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When CHA fails to make the call static, inline caches When IC's fail, virtual calls are back to being slow

Illusion: Partial Programs Are Fast
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JVMs allow late class loading, name binding
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i.e. classForName Adding new parts in (e.g. Class loading) is "cheap" May require: deoptimization, re-profiling, re-JIT Deoptimzation is a hard problem also

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Partial programs are as fast as whole programs
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Illusion: Consistent Memory Models
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ALL machines have different memory models
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The rules on visibility vary widely from machines And even within generations of the same machine X86 is very conservative, so is Sparc Power, MIPS less so IA64 & Azul very aggressive So must match the JMM Else program meaning would depend on hardware!

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Program semantics depend on the JMM
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Illusion: Consistent Memory Models
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Very different hardware memory models None match the Java Memory Model The JVM bridges the gap ● ●

While keeping normal loads & stores fast Via combinations of fences, code scheduling, placement of locks & CAS ops Requires close cooperation from the JITs Requires detailed hardware knowledge

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Illusion: Consistent Thread Models
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Very different OS thread models
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Linux, Solaris, AIX But also cell phones, iPad, etc On micro devices to 1000-cpu giant machines and synchronized, wait, notify, join, etc, all just work

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Java just does 'new Thread'
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Illusion: Locks are Fast
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Contended locks obviously block and must involve the OS
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(Expect fairness from the OS) Biased locking: ~2-4 clocks (when it works) Very fast user-mode locks otherwise

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Uncontended locks are a dozen nano's or so
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Highly optimized because synchronized is so common

Illusion: Locks are Fast
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People don't know how to program concurrently
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The 'just add locks until it works' mentality i.e. Lowest-common-denominator programming So locks became common So JVMs optimized them

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This enabled a particular concurrent programming style And we, as an industry, learned alot about concurrent programming as a result

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Illusion: Quick Time Access
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System.currentTimeMillis
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Called billions of times/sec in some benchmarks Fairly common in all large java apps Real Java programs expect that: if T1's Sys.cTM < T2's Sys.cTM then T1 <<<happens_before T2 Value not coherent across CPUs Not consistent, e.g. slow ticking in low-power mode Monotonic per CPU – but not per-thread

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But cannot use, e.g. X86's "tsc" register
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Illusion: Quick Time Access
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System.currentTimeMillis
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Switching from fastest linux gettimeofday call
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(mostly-user-mode atomic time struct read) gettimeofday gives quality time

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To a plain load (updated by background thread) Was worth 10% speed boost on key benchmark Means: uniform monotonic ticking Means: slows access to tsc by 100x?

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Hypervisors like to "idealize" tsc :
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Agenda
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Introduction (just did that) Illusions We Have Illusions We Think We Have or Wish We Had Sorting Our Illusions Out

Illusions We'd Like To Have
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Infinite Stack
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e.g. Tail calls. Useful in some functional languages e.g. Closures e.g. Auto-boxing optimizations e.g. Tagged integer math, silent overflow to infinite precision integers

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Running-code-is-data
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'Integer' is as cheap as 'int'
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'BigInteger' is as cheap as 'int'
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Illusions We'd Like To Have
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Atomic Multi-Address Update
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e.g. Software Transactional Memory e.g. invokedynamic

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Fast alternative call bindings
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Illusions We Think We Have
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This mass of code is maintainable:
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HotSpot is approaching 15yrs old Large chunks of code are fragile
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(or very 'fluffy' per line of code)

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Very slow new-feature rate-of-change Many major subsystems are simpler, faster, lighter >100K diffs from OpenJDK

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Azul Systems has been busy rewriting lots of it
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Illusions We Think We Have
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Thread priorities
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Mostly none on Linux without root permission But also relative to entire machine, not JVM Means a low-priority JVM with high priority threads
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e.g. Concurrent GC threads trying to keep up

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...can starve a medium-priority JVM Scale matters: programs for very small or very large machines are different

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Write-once-run-anywhere
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Illusions We Think We Have
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Finalizers are Useful
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They suck for reclaiming OS resources
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Because no timeliness guarantees Code "eventually" runs, but might be never e.g. Tomcat requires a out-of-file-handles situation trigger a FullGC to reclaim finalizers to recycle OS file handles

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What other out-of-OS resources situations need to trigger a GC? Do we really want to code our programs this way?

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Illusions We Think We Have
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Soft, Phantom Refs are Useful
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Again using GC to manage a user resource e.g. Use GC to manage Caches

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Low memory causes rapid GC cycles causes soft refs to flush causes caches to empty causes more cache misses causes more application work causes more allocation causes rapid GC cycles

Agenda
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Introduction (just did that) Illusions We Have Illusions We Think We Have or Wish We Had Sorting Our Illusions Out

Services Summary
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Services provided by JVM
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GC, JIT'ing, JMM, thread management, fast time Hiding CPU details & hardware memory model Threads, context switching, priorities, I/O, files, virtual memory protection, Threadpools & worklists, transactions, cypto, caching, models of concurrent programming Alt languages: new dispatch, big ints, alt conc

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Services provided below the JVM (OS)
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Services provided above the JVM (App)
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Move to OS: Fast Quality Time
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JVM provides fast quality time
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Fast not quality from X86 'tsc' Quality not fast from OS gettimeofday Tick memory word 1000/sec
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This should be an OS service
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Update with kernel thread or timer

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Read-only process-shared page This CTM is a coherent across CPUs on a clock-cycle basis

Move to OS: Thread Priorities
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OS provides thread priorities at the process level
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Higher priority JVMs can/should starve lower ones GC threads need cycles before mutator threads
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JVM also needs thread priorities within-process
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Or else that concurrent GC will won't be concurrent And the mutator will block for a GC cycle Or else the 1000-runnable threads will starve the JIT And the program will always run interpreted

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JIT threads need cycles
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Move to OS: Thread Priorities
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Right now Azul is faking thread priorities
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With duty-cycle style locks & blocks Required for a low-pauses concurrent collector OS already does process priorities & context switches Also, cannot raise thread priorities without 'root' Lowering mutator priorities changes behavior wrt non-Java processes

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Per-process Thread Priorities belong in the OS
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Keep Above JVM: Alternative Concurrency
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JVM provides thread management, fast locks Many new langs have new concurrency ideas
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Actors, Msg-passing, STM, Fork/Join are a few JVM too big, too slow to move fast here Should experiment 'above' the JVM ...at least until we get some concensus on The Right Way To Do Concurrency Then JVM maybe provides building blocks
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e.g. park/unpark or a specific kind of STM

Move to JVM: Fixnums
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Fixnums belong in the JVM, not language impl JVM provides 'int' & 'long'
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Many languages want 'ideal int' Obvious java translation to infinite math is inefficient
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Really want some kind of tagged integer Requires JIT support to be really efficient You (app-level programmer) know if you might need more Don't make everybody else pay for it

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I think "64bits ought to be enough for anybody"
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Keep in JVM: GC, JIT'ing, JMM, Type Safety
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JIT'ing (by itself) belongs above the OS and below the App – so in the JVM GC requires deep hooks into the JIT'ing process
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And also makes sense below the App And again (mostly) makes sense below the App Some alternative concurrency models would expose weaker MMs to the App, would enable faster, cheaper hardware – but this is still going require close JIT cooperation

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The JMM requires deep hooks into the JIT also
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Move Above JVM: OS Resource Lifetime
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Move outside-the-JVM resource lifetime control out of Finalizers
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Make the app do e.g. ref-counting or 'arena' or other lifetime management Do not burden GC with knowledge that more of resource 'X' can be had running finalizers GC should not change application semantics

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Move weak/soft/phantom refs to the App
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Summary
OS VirtualMemory ContextSwitches JVM Type-Safe Memory GC JIT'ing & Code Management JMM Fast Locks Thread Management Thread Priorities Fast Time OS Resource Management Application AlternativeConcurrency STM / FJ

Files, I/O

Fixnums Tail Calls Closures

Cliff Click http://www.azulsystems.com/blogs

Move To JVM (Azul): Virtual / Physical Mappings
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Azul's GPGC does aggressive virtual-memory to physical-memory remappings
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Tbytes/sec remapping rates mmap() & friends too slow Still safe across processes But within process can totally screw self up

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Need OS hacks to expose hardware TLB
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Move To JVM (Azul): Hardware Perf Counters
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JVM is already doing profile-directed compilation
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Natural consumer of HW Perf Counters JIT's code, manages JIT'd code "hotcode" mapped back to user's bytecodes

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JVM can map perf counters to bytecodes
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Want quickest & thin-est way to expose HW perf counters to JVM

Summary (Azul)
OS Virtual / PhysicalMemory Mapping HW Perf Counters Azul's JVM GPGC Application

Profiling Thread Priorities Fast Time

Cliff Click http://www.azulsystems.com/blogs

Summary

There's Work To Do
(full employment contract for JVM engineers)

Cliff Click http://www.azulsystems.com/blogs