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JavaOne 2015 CON7547 "Beyond the Coffee Cup: Leveraging Java Runtime Technologies for Polyglot"

The document discusses IBM's initiative to open-source a runtime technology toolkit aimed at creating a polyglot environment that integrates with various virtual machines (VMs). It focuses on leveraging existing Java VM technology (J9) to enhance integration, compatibility, and innovation across diverse programming languages and communities. The presentation outlines ongoing development efforts, including garbage collection enhancements and just-in-time compilation strategies, while emphasizing the importance of community collaboration for future advancements in runtime infrastructure.

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Beyond the Coffee Cup: Leveraging Java Runtime Technologies for Polyglot Daryl Maier Senior Software Developer at IBM Canada IBM Runtimes maier@ca.ibm.com
About me… • IBM Canada Lab in Toronto (-ish) • Member of IBM Runtime Technologies team • Compiler and runtime optimizations for 20 years • Leading a not-so-secret project to open-source IBM compilation technology
Trademarks, Copyrights, Disclaimers IBM’s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. IBM, the IBM logo, and ibm.com are trademarks or registered trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of other IBM trademarks is available on the web at "Copyright and trademark information" at http://www.ibm.com/legal/copytrade.shtml Other company, product, or service names may be trademarks or service marks of others. THE INFORMATION CONTAINED IN THIS PRESENTATION IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY. WHILE EFFORTS WERE MADE TO VERIFY THE COMPLETENESS AND ACCURACY OF THE INFORMATION CONTAINED IN THIS PRESENTATION, IT IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. IN ADDITION, THIS INFORMATION IS BASED ON IBM’S CURRENT PRODUCT PLANS AND STRATEGY, WHICH ARE SUBJECT TO CHANGE BY IBM WITHOUT NOTICE. IBM SHALL NOT BE RESPONSIBLE FOR ANY DAMAGES ARISING OUT OF THE USE OF, OR OTHERWISE RELATED TO, THIS PRESENTATION OR ANY OTHER DOCUMENTATION. NOTHING CONTAINED IN THIS PRESENTATION IS INTENDED TO, NOR SHALL HAVE THE EFFECT OF, CREATING ANY WARRANTIES OR REPRESENTATIONS FROM IBM (OR ITS SUPPLIERS OR LICENSORS), OR ALTERING THE TERMS AND CONDITIONS OF ANY AGREEMENT OR LICENSE GOVERNING THE USE OF IBM PRODUCTS OR SOFTWARE. © Copyright International Business Machines Corporation 2015. All rights reserved.
Additional Important Disclaimers THE INFORMATION CONTAINED IN THIS PRESENTATION IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY. WHILST EFFORTS WERE MADE TO VERIFY THE COMPLETENESS AND ACCURACY OF THE INFORMATION CONTAINED IN THIS PRESENTATION, IT IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. ALL PERFORMANCE DATA INCLUDED IN THIS PRESENTATION HAVE BEEN GATHERED IN A CONTROLLED ENVIRONMENT. YOUR OWN TEST RESULTS MAY VARY BASED ON HARDWARE, SOFTWARE OR INFRASTRUCTURE DIFFERENCES. ALL DATA INCLUDED IN THIS PRESENTATION ARE MEANT TO BE USED ONLY AS A GUIDE. IN ADDITION, THE INFORMATION CONTAINED IN THIS PRESENTATION IS BASED ON IBM’S CURRENT PRODUCT PLANS AND STRATEGY, WHICH ARE SUBJECT TO CHANGE BY IBM, WITHOUT NOTICE. IBM AND ITS AFFILIATED COMPANIES SHALL NOT BE RESPONSIBLE FOR ANY DAMAGES ARISING OUT OF THE USE OF, OR OTHERWISE RELATED TO, THIS PRESENTATION OR ANY OTHER DOCUMENTATION. NOTHING CONTAINED IN THIS PRESENTATION IS INTENDED TO, OR SHALL HAVE THE EFFECT OF: - CREATING ANY WARRANT OR REPRESENTATION FROM IBM, ITS AFFILIATED COMPANIES OR ITS OR THEIR SUPPLIERS AND/OR LICENSORS
• Announced by Mark Stoodley at JVMLS 2015 • See the complete talk here. IBM to open-source a runtime technology toolkit!
{groan} Oh, great – another common runtime environment! Polyglot VM Community
{cheer} Woo hoo – a runtime toolkit that integrates with my VM! Polyglot VM Community
Motivation for a runtime toolkit • Experiment with leveraging investment in J9 Java VM technology in a way that facilitates integration of this technology into other VMs • Compatibility with existing runtimes and their communities • Lots of vibrant language communities can’t tolerate disruptive technologies • Technology should be flexible enough to bend to the community rather than the other way around • Work with all the features that make that language great • Simple consumption into existing runtimes
Unlock the “VM” from the J9 Java VM for polyglot • Refactor several J9 components to create a language-agnostic toolkit designed for integration into language runtimes (including J9 JVM) • Memory allocator, thread library, platform port library, event hook framework, VM and application level trace engine, garbage collector, JIT compiler • Experiments to bring capabilities from J9 to Ruby MRI, CPython, and CSOM • Integration by specializing toolkit components with runtime details from MRI, CPython, CSOM • Gauge promise of this approach • Not a research project: our JDK product development team aggressively refactoring our VM, GC, and JIT technology • Shipped IBM JDK8 from snapshot of refactored code base • JDK9 development ongoing as we continue to experiment
Common utilities Thread library Port library Trace GC JIT VM Structures … Core runtime infrastructure Tool Agents Transplanting J9 capabilities to other runtimes
Method profiling for Ruby MRI: introduce tracepoint to feed call stacks samples to Health Center agent
Scalable garbage collector integration • Integrated GC into Ruby MRI • Type accurate, but used conservatively so extensions work as-is • MRI: can move off-heap native memory into manageable heap • Proof point: verbose GC <cycle-start id="2" type="global" contextid="0" timestamp="2015-08-05T17:21:58.105" intervalms="5066.731" /> <gc-start id="3" type="global" contextid="2" timestamp="2015-08-05T17:21:58.105"> <mem-info id="4" free="596848" total="4194304" percent="14"> <mem type="tenure" free="596848" total="4194304" percent="14" /> </mem-info> </gc-start> <allocation-stats totalBytes="3596216" > <allocated-bytes non-tlh="720016" tlh="2876200" /> </allocation-stats> <gc-op id="5" type="mark" timems="4.881" contextid="2" timestamp="2015-08-05T17:21:58.110"> <trace-info objectcount="8914" scancount="7208" scanbytes="288320" /> </gc-op> <gc-op id="8" type="sweep" timems="0.688" contextid="2" timestamp="2015-08-05T17:21:58.111" /> <gc-end id="9" type="global" contextid="2" durationms="5.896" usertimems="7.999" systemtimems="1.999" timestamp="2015-08-05T17:21:58.111" activeThreads="2"> <mem-info id="10" free="2508160" total="4194304" percent="59"> <mem type="tenure" free="2508160" total="4194304" percent="59" micro-fragmented="297048" macro-fragmented="723458" /> </mem-info> </gc-end> <cycle-end id="11" type="global" contextid="2" timestamp="2015-08-05T17:21:58.111" />
GC visualization for Ruby MRI via existing GC trace points feeding GC events to Health Center agent
Garbage Collection Memory Visualizer for Ruby MRI with zero changes to the tool
JIT integration • Ruby MRI and CPython do not have JIT compilers • Both environments are challenging for JIT compilers • Highly dynamic • Unmanaged direct use of internal data structures by extensions • Design choices in the runtimes themselves (e.g., setjmp/longjmp) • Our effort to date has particular emphases • Compile native instructions for methods and blocks • Avoid big changes to how MRI/CPython works (to ease adoption) • Consistent behavior for compiled code vs. interpreted code • No restrictions on native code used by extension modules • No benchmark tuning or specials • Compatibility success story: We can run Rails! • Performance success story: 1.2x + on many Bench9k kernels on 3 architectures without tuning
Proof point: IBM JDK 8 Health Center enables remote Bluemix performance analysis ORB optimizations can show 3x Improvements in real-World banking scenarios Liberty and tWAS Daytrader3 workload increases (15% on Linux Intel) 2x improvement per core seen with SSL by using SMT vs Java 7.1 on zEC12 Improved IBMJCE crypto performance New instruction exploitation CPACF instructions: AES, 3DES, SHA1, SHA2 etc Up to 50% less CPU to ramp-up to steady state JZOS Toolkit enhancements Public Key improvements with ECC Up to 25% faster Liberty workload deployment 20% better ramp-up using Runtime Instrumentation NVIDIA GPU support IBMJCE crypto improvements on both AES and ECC encryption using hardware exploitation PERFORMANCE ! Java6 SR16 FP4 Java 6.1 SR8 FP4 Java 7 SR9 Java 7.1 SR3 Java 8 SR1 Apache Spark 1.4 Daytrader3 Linux Intel
Open community • Create an open community of contribution based around a toolkit of components that can be used to build VMs for any language • Efficient place for individuals, communities, and companies to safely collaborate on core VM infrastructure • Enable everyone to focus more energy on innovation, not on building more wheels • Build more robust core technology – Fix bugs once – Tested in many different scenarios • Collection of best practices and shared learning • Lower entry barrier for new languages and VM ideas – Test ideas faster and more reliably
What this talk is about… • Mark’s JVMLS talk focused on the “whys” and “whats”, this talk will focus on the “hows” • GC experience deep dive • CON7863: What’s in an Object? Java Garbage Collection for the Polyglot (Charlie Gracie) • JIT experience with refactoring the “VM” from the “JVM”
Are there enough re-usable components in a Java JIT to build a polyglot toolkit?
Sampling thread determines which methods spend the most time executing Add “hot” methods to compilation queue for asynchronous compilation Translate Java bytecode into compiler IR Choose a method from the queue for compilation Choose a tiered optimization strategy Perform high-level classical, speculative, and Java-specific optimizations Generate code (instructions from IR, register assignment, binary encoding, relocations) Publish method metadata Bind method into VM Direct interpreter and JIT call sites to newly compiled body Recompile at higher optimization level Patch runtime assumption guards, polymorphic inline caches, monitor speculative optimizations
Sampling thread determines which methods spend the most time executing Add “hot” methods to compilation queue for asynchronous compilation Translate Java bytecode into compiler IR Choose a method from the queue for compilation Choose a tiered optimization strategy Perform high-level classical, speculative, and Java-specific optimizations Generate code (instructions from IR, register assignment, binary encoding, relocations) Publish method metadata Bind method into VM Direct interpreter and JIT call sites to newly compiled body Recompile at higher optimization level Patch runtime assumption guards, polymorphic inline caches, monitor speculative optimizations Choose methods or blocks to compile based on invocation count Chosen method is compiled synchronously on application thread Translate CRuby iseq to compiler IR Choose a fixed optimization strategy Perform high-level classical, speculative, and Ruby-specific optimizations Generate code (instructions from IR, register assignment, binary encoding, relocations) Publish method metadata Bind method into VM Direct interpreter and JIT call sites to newly compiled body Patch runtime assumption guards, polymorphic inline caches, monitor speculative optimizations
Sampling thread determines which methods spend the most time executing Add “hot” methods to compilation queue for asynchronous compilation Translate Java bytecode into compiler IR Choose a method from the queue for compilation Choose a tiered optimization strategy Perform high-level classical, speculative, and Java-specific optimizations Generate code (instructions from IR, register assignment, binary encoding, relocations) Publish method metadata Bind method into VM Direct interpreter and JIT call sites to newly compiled body Recompile at higher optimization level Patch runtime assumption guards, polymorphic inline caches, monitor speculative optimizations Choose methods or blocks to compile based on invocation count Chosen method is compiled synchronously on application thread Translate CRuby iseq to compiler IR Choose a fixed optimization strategy Perform high-level classical, speculative, and Ruby-specific optimizations Generate code (instructions from IR, register assignment, binary encoding, relocations) Publish method metadata Bind method into VM Direct interpreter and JIT call sites to newly compiled body Patch runtime assumption guards, polymorphic inline caches, monitor speculative optimizations Select a method to compile Compile a method with appropriate optimizations Dispatch to compiled body Adapt compiled method to changing environment
Life Cycle JIT startup and shutdown; initialization and destruction of resources (compilation threads, options processing, memory management, …) Compilation Trigger Logic for determining when a method should be compiled: runtime method sampling at execution, sample processing, count-and-send targets Infrastructure Supporting infrastructure for compilation: data structures (CFGs, blocks, trees, instructions, symbol reference tables, aliasing, code caches), tracing/logging, enabled feature processing VM/JIT Interface API between VM and JIT. Ask/answer questions about environment (e.g., lookup class or method info), language semantics (e.g., float association), available capabilities (e.g., GPU present), object model (e.g., array header size), configuration (e.g., GC policy), runtime helpers Compilation Intermediate representation; data types; optimization frameworks; classical, dynamic, and speculative optimizations; code generation; register assignment; instruction schedulers; binary encoders; code cache management; relocation processing; stack mapping Method Dispatch Dispatch to compiled method from interpreted and JITed call sites; call site fixup Runtime Adaptation Method meta data, runtime assumption managers, profiling, recompilation framework, code patching Components of a compiler toolkit
IBM creating a toolkit of extensible compiler components • Start with mature J9 Java just-in-time compiler (aka Testarossa, or TR) • Isolate the Java parts from the generic parts • Re-engineer source code to allow specialization • TR technology has already proven to be highly-adaptable to different compilation uses • 8 different compiler technology products or use-cases • Consumption model: clone VM; clone O/S JIT; make
Testarossa Java compiler technology • Heritage is a dynamic JIT for embedded Java • Clean room implementation • Mix of C++, C, native assembler • Design goals • Fast startup time • Miserly memory management • Flexible to meet different footprint configurations • Optimizations • Configurable high-level optimization framework • High performance code generation with deep platform exploitation • Dynamic recompilation with profile-directed feedback • Speculative optimizations and supporting runtime framework
Optimizer Optimizations cold warm hot FSDscorching AOT Java Bytecode … IL Generators x86 POWER z Systems Code Generators Profile Manager Hardware counters Sampling Thread Interpreter Profile Info JIT Profile Info Profiler .. Compile Time Connectors J9 JVM • Connector Options • Object Model • Language Specific Code Generators • C library functions • Threading • Tracing Testarossa Reusable Compilation Components codeMetadataRuntimeRT Connectors RT Helpers very hot profiling
Specialization of compiler components • Not straightforward how to distill generic functionality from core TR components and allow specialization for polyglot • Two main axes of specialization • the kind of compiler you’re trying to build • the processor architecture you’re targeting • Goals • Isolation of compiler features • Minimize impact to key compilation metrics: startup, compile-time, and footprint • Minimize future merge and integration costs of specializations (easy consumption) • Permit future extensibility
Engineering for extensibility • Some reorganization is necessary to facilitate building an extensible model • Code and source files organized into an ordered hierarchy of “projects” each of which contains some specialization of compiler functionality • Refactor core compiler technology classes into “extensible” C++ classes • Follows a single-inheritance, composition model for specialization • Static polymorphism for efficiency • Makefile and include path determine which specializations to use
Compiler Sprocket (Open source project) Power Extension X86 Extension i386 Ext Compiler Sprocket (AwesomeVM Project) X86 Ext ARMv7 Ext Z Ext Compiler Sprocket (MyOrg’s Multi-Language Runtime Project) X86 Power Ext Compiler Sprocket (MyOrg’s C++ Compiler Project) Power Ext X86 Ext ARMv7 Ext
Effective Sprocket compositions Compiler Sprocket (Open Source Project) Compiler Sprocket X86 Extension (O/S) Compiler Sprocket i386 Extension (O/S) Compiler Sprocket (MyOrg MLR Project) Compiler Sprocket (MyOrg C++ Project) Compiler Sprocket X86 Extension (MyOrg C++) Compiler Sprocket (Open Source Project) Compiler Sprocket for i386 MyOrg C++ Build Compiler Sprocket for Power MyOrg C++ Build Compiler Sprocket Power Extension (O/S) Compiler Sprocket (MyOrg MLR Project) Compiler Sprocket Power Ext (MyOrg MLR) Compiler Sprocket (MyOrg C++ Project) Compiler Sprocket Power Ext (MyOrg C++) Compiler Sprocket Compiler Sprocket
Testarossa intermediate language • Internal node opcodes and datatypes can be extended for different projects and architectures iload a iload b isub bipush 2 imul istore a istore atreetop iconst 2 imul iload a iload b isub Java Bytecode • TR uses a tree-based intermediate representation, where the “tree” represents a single expression or statement
IL generators • Produce IL that can be consumed by JIT technology • Highly specialized to the environment VM • Depend largely on the input: bytecodes, instructions, etc. • Consume and represent symbol information • Each are generally independent, but shares IL construction utilities for tree, node, and block creation
High-level IL optimizer • Complete suite of classical and Java-specific optimizations • Platform-neutral, each optimization consumes and produces IL • Flexible configuration allows optimization strategies to be constructed • Spend compile-time where and when it makes sense for each VM • Most challenging to adapt for polyglot because some optimizations provide VM- or architecture-specific specializations that are entwined with analysis and transformation phases • Make it easier to adapt by separating policy from mechanism in each optimization, and then specializing both as necessary
Minimal compiler toolkit consumption model 1. Clone your favorite VM 2. Clone the open-source compiler toolkit 3. Implement a VM-specific extension to the compilation trigger and method dispatch interface 4. Implement an IL generator for your VM, perhaps on a subset of all possible “bytecodes” or “instructions” 5. Implement VM-specific extensions as needed to • Core technology (e.g., IL nodes, opcodes, code generation, instructions) • VM <-> JIT interface (e.g., Q&A about object model, name lookup, bytecode info) 6. Modify VM makefiles and include paths to integrate JIT technology 7. make Implement richer support for all inputs and VM features once basic hookup is completed!
Longer term challenges • Must optimize at a higher semantic level for maximum performance • Optimizing for compatibility has a point of diminishing returns • Can’t just optimize the connective tissue in the interpreter • e.g., arithmetic operations • Increase use of method meta data • Don’t waste execution time maintaining interpreter state • Leverage on-stack replacement to focus on what matters • Build in fork tolerance • Forking is the means by which some VMs achieve parallelism • Difficult to manage compilation efficiently across multiple processes
The road to open-source • Early results are very promising for all our technologies • Next steps • Some components in the toolkit are ready (Port, Thread, Trace, GC) • Compiler technology needs more time • Balance refactoring work against developing proof points • Engage with runtime communities and partners • I invite your feedback on our open proposal, our compiler toolkit, or your interest in becoming involved Daryl Maier O/S JIT Lead maier@ca.ibm.com John Duimovich CTO, IBM Runtime Technologies John_Duimovich@ca.ibm.com Mark Stoodley O/S Project Lead mstoodle@ca.ibm.com
Acknowledgements • Clip art on slides 6 and 7 was sourced from • https://openclipart.org/image/2400px/svg_to_png/92065/paro-AL- calling.png

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JavaOne 2015 CON7547 "Beyond the Coffee Cup: Leveraging Java Runtime Technologies for Polyglot"

  • 1.
    Beyond the CoffeeCup: Leveraging Java Runtime Technologies for Polyglot Daryl Maier Senior Software Developer at IBM Canada IBM Runtimes maier@ca.ibm.com
  • 2.
    About me… • IBMCanada Lab in Toronto (-ish) • Member of IBM Runtime Technologies team • Compiler and runtime optimizations for 20 years • Leading a not-so-secret project to open-source IBM compilation technology
  • 3.
    Trademarks, Copyrights, Disclaimers IBM’sstatements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM’s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. IBM, the IBM logo, and ibm.com are trademarks or registered trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of other IBM trademarks is available on the web at "Copyright and trademark information" at http://www.ibm.com/legal/copytrade.shtml Other company, product, or service names may be trademarks or service marks of others. THE INFORMATION CONTAINED IN THIS PRESENTATION IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY. WHILE EFFORTS WERE MADE TO VERIFY THE COMPLETENESS AND ACCURACY OF THE INFORMATION CONTAINED IN THIS PRESENTATION, IT IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. IN ADDITION, THIS INFORMATION IS BASED ON IBM’S CURRENT PRODUCT PLANS AND STRATEGY, WHICH ARE SUBJECT TO CHANGE BY IBM WITHOUT NOTICE. IBM SHALL NOT BE RESPONSIBLE FOR ANY DAMAGES ARISING OUT OF THE USE OF, OR OTHERWISE RELATED TO, THIS PRESENTATION OR ANY OTHER DOCUMENTATION. NOTHING CONTAINED IN THIS PRESENTATION IS INTENDED TO, NOR SHALL HAVE THE EFFECT OF, CREATING ANY WARRANTIES OR REPRESENTATIONS FROM IBM (OR ITS SUPPLIERS OR LICENSORS), OR ALTERING THE TERMS AND CONDITIONS OF ANY AGREEMENT OR LICENSE GOVERNING THE USE OF IBM PRODUCTS OR SOFTWARE. © Copyright International Business Machines Corporation 2015. All rights reserved.
  • 4.
    Additional Important Disclaimers THEINFORMATION CONTAINED IN THIS PRESENTATION IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY. WHILST EFFORTS WERE MADE TO VERIFY THE COMPLETENESS AND ACCURACY OF THE INFORMATION CONTAINED IN THIS PRESENTATION, IT IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED. ALL PERFORMANCE DATA INCLUDED IN THIS PRESENTATION HAVE BEEN GATHERED IN A CONTROLLED ENVIRONMENT. YOUR OWN TEST RESULTS MAY VARY BASED ON HARDWARE, SOFTWARE OR INFRASTRUCTURE DIFFERENCES. ALL DATA INCLUDED IN THIS PRESENTATION ARE MEANT TO BE USED ONLY AS A GUIDE. IN ADDITION, THE INFORMATION CONTAINED IN THIS PRESENTATION IS BASED ON IBM’S CURRENT PRODUCT PLANS AND STRATEGY, WHICH ARE SUBJECT TO CHANGE BY IBM, WITHOUT NOTICE. IBM AND ITS AFFILIATED COMPANIES SHALL NOT BE RESPONSIBLE FOR ANY DAMAGES ARISING OUT OF THE USE OF, OR OTHERWISE RELATED TO, THIS PRESENTATION OR ANY OTHER DOCUMENTATION. NOTHING CONTAINED IN THIS PRESENTATION IS INTENDED TO, OR SHALL HAVE THE EFFECT OF: - CREATING ANY WARRANT OR REPRESENTATION FROM IBM, ITS AFFILIATED COMPANIES OR ITS OR THEIR SUPPLIERS AND/OR LICENSORS
  • 5.
    • Announced byMark Stoodley at JVMLS 2015 • See the complete talk here. IBM to open-source a runtime technology toolkit!
  • 6.
    {groan} Oh, great –another common runtime environment! Polyglot VM Community
  • 7.
    {cheer} Woo hoo –a runtime toolkit that integrates with my VM! Polyglot VM Community
  • 8.
    Motivation for aruntime toolkit • Experiment with leveraging investment in J9 Java VM technology in a way that facilitates integration of this technology into other VMs • Compatibility with existing runtimes and their communities • Lots of vibrant language communities can’t tolerate disruptive technologies • Technology should be flexible enough to bend to the community rather than the other way around • Work with all the features that make that language great • Simple consumption into existing runtimes
  • 9.
    Unlock the “VM”from the J9 Java VM for polyglot • Refactor several J9 components to create a language-agnostic toolkit designed for integration into language runtimes (including J9 JVM) • Memory allocator, thread library, platform port library, event hook framework, VM and application level trace engine, garbage collector, JIT compiler • Experiments to bring capabilities from J9 to Ruby MRI, CPython, and CSOM • Integration by specializing toolkit components with runtime details from MRI, CPython, CSOM • Gauge promise of this approach • Not a research project: our JDK product development team aggressively refactoring our VM, GC, and JIT technology • Shipped IBM JDK8 from snapshot of refactored code base • JDK9 development ongoing as we continue to experiment
  • 10.
  • 11.
    Method profiling forRuby MRI: introduce tracepoint to feed call stacks samples to Health Center agent
  • 12.
    Scalable garbage collectorintegration • Integrated GC into Ruby MRI • Type accurate, but used conservatively so extensions work as-is • MRI: can move off-heap native memory into manageable heap • Proof point: verbose GC <cycle-start id="2" type="global" contextid="0" timestamp="2015-08-05T17:21:58.105" intervalms="5066.731" /> <gc-start id="3" type="global" contextid="2" timestamp="2015-08-05T17:21:58.105"> <mem-info id="4" free="596848" total="4194304" percent="14"> <mem type="tenure" free="596848" total="4194304" percent="14" /> </mem-info> </gc-start> <allocation-stats totalBytes="3596216" > <allocated-bytes non-tlh="720016" tlh="2876200" /> </allocation-stats> <gc-op id="5" type="mark" timems="4.881" contextid="2" timestamp="2015-08-05T17:21:58.110"> <trace-info objectcount="8914" scancount="7208" scanbytes="288320" /> </gc-op> <gc-op id="8" type="sweep" timems="0.688" contextid="2" timestamp="2015-08-05T17:21:58.111" /> <gc-end id="9" type="global" contextid="2" durationms="5.896" usertimems="7.999" systemtimems="1.999" timestamp="2015-08-05T17:21:58.111" activeThreads="2"> <mem-info id="10" free="2508160" total="4194304" percent="59"> <mem type="tenure" free="2508160" total="4194304" percent="59" micro-fragmented="297048" macro-fragmented="723458" /> </mem-info> </gc-end> <cycle-end id="11" type="global" contextid="2" timestamp="2015-08-05T17:21:58.111" />
  • 13.
    GC visualization forRuby MRI via existing GC trace points feeding GC events to Health Center agent
  • 14.
    Garbage Collection MemoryVisualizer for Ruby MRI with zero changes to the tool
  • 15.
    JIT integration • RubyMRI and CPython do not have JIT compilers • Both environments are challenging for JIT compilers • Highly dynamic • Unmanaged direct use of internal data structures by extensions • Design choices in the runtimes themselves (e.g., setjmp/longjmp) • Our effort to date has particular emphases • Compile native instructions for methods and blocks • Avoid big changes to how MRI/CPython works (to ease adoption) • Consistent behavior for compiled code vs. interpreted code • No restrictions on native code used by extension modules • No benchmark tuning or specials • Compatibility success story: We can run Rails! • Performance success story: 1.2x + on many Bench9k kernels on 3 architectures without tuning
  • 16.
    Proof point: IBMJDK 8 Health Center enables remote Bluemix performance analysis ORB optimizations can show 3x Improvements in real-World banking scenarios Liberty and tWAS Daytrader3 workload increases (15% on Linux Intel) 2x improvement per core seen with SSL by using SMT vs Java 7.1 on zEC12 Improved IBMJCE crypto performance New instruction exploitation CPACF instructions: AES, 3DES, SHA1, SHA2 etc Up to 50% less CPU to ramp-up to steady state JZOS Toolkit enhancements Public Key improvements with ECC Up to 25% faster Liberty workload deployment 20% better ramp-up using Runtime Instrumentation NVIDIA GPU support IBMJCE crypto improvements on both AES and ECC encryption using hardware exploitation PERFORMANCE ! Java6 SR16 FP4 Java 6.1 SR8 FP4 Java 7 SR9 Java 7.1 SR3 Java 8 SR1 Apache Spark 1.4 Daytrader3 Linux Intel
  • 17.
    Open community • Createan open community of contribution based around a toolkit of components that can be used to build VMs for any language • Efficient place for individuals, communities, and companies to safely collaborate on core VM infrastructure • Enable everyone to focus more energy on innovation, not on building more wheels • Build more robust core technology – Fix bugs once – Tested in many different scenarios • Collection of best practices and shared learning • Lower entry barrier for new languages and VM ideas – Test ideas faster and more reliably
  • 18.
    What this talkis about… • Mark’s JVMLS talk focused on the “whys” and “whats”, this talk will focus on the “hows” • GC experience deep dive • CON7863: What’s in an Object? Java Garbage Collection for the Polyglot (Charlie Gracie) • JIT experience with refactoring the “VM” from the “JVM”
  • 19.
    Are there enoughre-usable components in a Java JIT to build a polyglot toolkit?
  • 20.
    Sampling thread determineswhich methods spend the most time executing Add “hot” methods to compilation queue for asynchronous compilation Translate Java bytecode into compiler IR Choose a method from the queue for compilation Choose a tiered optimization strategy Perform high-level classical, speculative, and Java-specific optimizations Generate code (instructions from IR, register assignment, binary encoding, relocations) Publish method metadata Bind method into VM Direct interpreter and JIT call sites to newly compiled body Recompile at higher optimization level Patch runtime assumption guards, polymorphic inline caches, monitor speculative optimizations
  • 21.
    Sampling thread determineswhich methods spend the most time executing Add “hot” methods to compilation queue for asynchronous compilation Translate Java bytecode into compiler IR Choose a method from the queue for compilation Choose a tiered optimization strategy Perform high-level classical, speculative, and Java-specific optimizations Generate code (instructions from IR, register assignment, binary encoding, relocations) Publish method metadata Bind method into VM Direct interpreter and JIT call sites to newly compiled body Recompile at higher optimization level Patch runtime assumption guards, polymorphic inline caches, monitor speculative optimizations Choose methods or blocks to compile based on invocation count Chosen method is compiled synchronously on application thread Translate CRuby iseq to compiler IR Choose a fixed optimization strategy Perform high-level classical, speculative, and Ruby-specific optimizations Generate code (instructions from IR, register assignment, binary encoding, relocations) Publish method metadata Bind method into VM Direct interpreter and JIT call sites to newly compiled body Patch runtime assumption guards, polymorphic inline caches, monitor speculative optimizations
  • 22.
    Sampling thread determineswhich methods spend the most time executing Add “hot” methods to compilation queue for asynchronous compilation Translate Java bytecode into compiler IR Choose a method from the queue for compilation Choose a tiered optimization strategy Perform high-level classical, speculative, and Java-specific optimizations Generate code (instructions from IR, register assignment, binary encoding, relocations) Publish method metadata Bind method into VM Direct interpreter and JIT call sites to newly compiled body Recompile at higher optimization level Patch runtime assumption guards, polymorphic inline caches, monitor speculative optimizations Choose methods or blocks to compile based on invocation count Chosen method is compiled synchronously on application thread Translate CRuby iseq to compiler IR Choose a fixed optimization strategy Perform high-level classical, speculative, and Ruby-specific optimizations Generate code (instructions from IR, register assignment, binary encoding, relocations) Publish method metadata Bind method into VM Direct interpreter and JIT call sites to newly compiled body Patch runtime assumption guards, polymorphic inline caches, monitor speculative optimizations Select a method to compile Compile a method with appropriate optimizations Dispatch to compiled body Adapt compiled method to changing environment
  • 23.
    Life Cycle JIT startupand shutdown; initialization and destruction of resources (compilation threads, options processing, memory management, …) Compilation Trigger Logic for determining when a method should be compiled: runtime method sampling at execution, sample processing, count-and-send targets Infrastructure Supporting infrastructure for compilation: data structures (CFGs, blocks, trees, instructions, symbol reference tables, aliasing, code caches), tracing/logging, enabled feature processing VM/JIT Interface API between VM and JIT. Ask/answer questions about environment (e.g., lookup class or method info), language semantics (e.g., float association), available capabilities (e.g., GPU present), object model (e.g., array header size), configuration (e.g., GC policy), runtime helpers Compilation Intermediate representation; data types; optimization frameworks; classical, dynamic, and speculative optimizations; code generation; register assignment; instruction schedulers; binary encoders; code cache management; relocation processing; stack mapping Method Dispatch Dispatch to compiled method from interpreted and JITed call sites; call site fixup Runtime Adaptation Method meta data, runtime assumption managers, profiling, recompilation framework, code patching Components of a compiler toolkit
  • 24.
    IBM creating atoolkit of extensible compiler components • Start with mature J9 Java just-in-time compiler (aka Testarossa, or TR) • Isolate the Java parts from the generic parts • Re-engineer source code to allow specialization • TR technology has already proven to be highly-adaptable to different compilation uses • 8 different compiler technology products or use-cases • Consumption model: clone VM; clone O/S JIT; make
  • 25.
    Testarossa Java compilertechnology • Heritage is a dynamic JIT for embedded Java • Clean room implementation • Mix of C++, C, native assembler • Design goals • Fast startup time • Miserly memory management • Flexible to meet different footprint configurations • Optimizations • Configurable high-level optimization framework • High performance code generation with deep platform exploitation • Dynamic recompilation with profile-directed feedback • Speculative optimizations and supporting runtime framework
  • 26.
    Optimizer Optimizations cold warm hotFSDscorching AOT Java Bytecode … IL Generators x86 POWER z Systems Code Generators Profile Manager Hardware counters Sampling Thread Interpreter Profile Info JIT Profile Info Profiler .. Compile Time Connectors J9 JVM • Connector Options • Object Model • Language Specific Code Generators • C library functions • Threading • Tracing Testarossa Reusable Compilation Components codeMetadataRuntimeRT Connectors RT Helpers very hot profiling
  • 27.
    Specialization of compilercomponents • Not straightforward how to distill generic functionality from core TR components and allow specialization for polyglot • Two main axes of specialization • the kind of compiler you’re trying to build • the processor architecture you’re targeting • Goals • Isolation of compiler features • Minimize impact to key compilation metrics: startup, compile-time, and footprint • Minimize future merge and integration costs of specializations (easy consumption) • Permit future extensibility
  • 28.
    Engineering for extensibility •Some reorganization is necessary to facilitate building an extensible model • Code and source files organized into an ordered hierarchy of “projects” each of which contains some specialization of compiler functionality • Refactor core compiler technology classes into “extensible” C++ classes • Follows a single-inheritance, composition model for specialization • Static polymorphism for efficiency • Makefile and include path determine which specializations to use
  • 29.
    Compiler Sprocket (Opensource project) Power Extension X86 Extension i386 Ext Compiler Sprocket (AwesomeVM Project) X86 Ext ARMv7 Ext Z Ext Compiler Sprocket (MyOrg’s Multi-Language Runtime Project) X86 Power Ext Compiler Sprocket (MyOrg’s C++ Compiler Project) Power Ext X86 Ext ARMv7 Ext
  • 30.
    Effective Sprocket compositions CompilerSprocket (Open Source Project) Compiler Sprocket X86 Extension (O/S) Compiler Sprocket i386 Extension (O/S) Compiler Sprocket (MyOrg MLR Project) Compiler Sprocket (MyOrg C++ Project) Compiler Sprocket X86 Extension (MyOrg C++) Compiler Sprocket (Open Source Project) Compiler Sprocket for i386 MyOrg C++ Build Compiler Sprocket for Power MyOrg C++ Build Compiler Sprocket Power Extension (O/S) Compiler Sprocket (MyOrg MLR Project) Compiler Sprocket Power Ext (MyOrg MLR) Compiler Sprocket (MyOrg C++ Project) Compiler Sprocket Power Ext (MyOrg C++) Compiler Sprocket Compiler Sprocket
  • 31.
    Testarossa intermediate language •Internal node opcodes and datatypes can be extended for different projects and architectures iload a iload b isub bipush 2 imul istore a istore atreetop iconst 2 imul iload a iload b isub Java Bytecode • TR uses a tree-based intermediate representation, where the “tree” represents a single expression or statement
  • 32.
    IL generators • ProduceIL that can be consumed by JIT technology • Highly specialized to the environment VM • Depend largely on the input: bytecodes, instructions, etc. • Consume and represent symbol information • Each are generally independent, but shares IL construction utilities for tree, node, and block creation
  • 33.
    High-level IL optimizer •Complete suite of classical and Java-specific optimizations • Platform-neutral, each optimization consumes and produces IL • Flexible configuration allows optimization strategies to be constructed • Spend compile-time where and when it makes sense for each VM • Most challenging to adapt for polyglot because some optimizations provide VM- or architecture-specific specializations that are entwined with analysis and transformation phases • Make it easier to adapt by separating policy from mechanism in each optimization, and then specializing both as necessary
  • 34.
    Minimal compiler toolkitconsumption model 1. Clone your favorite VM 2. Clone the open-source compiler toolkit 3. Implement a VM-specific extension to the compilation trigger and method dispatch interface 4. Implement an IL generator for your VM, perhaps on a subset of all possible “bytecodes” or “instructions” 5. Implement VM-specific extensions as needed to • Core technology (e.g., IL nodes, opcodes, code generation, instructions) • VM <-> JIT interface (e.g., Q&A about object model, name lookup, bytecode info) 6. Modify VM makefiles and include paths to integrate JIT technology 7. make Implement richer support for all inputs and VM features once basic hookup is completed!
  • 35.
    Longer term challenges •Must optimize at a higher semantic level for maximum performance • Optimizing for compatibility has a point of diminishing returns • Can’t just optimize the connective tissue in the interpreter • e.g., arithmetic operations • Increase use of method meta data • Don’t waste execution time maintaining interpreter state • Leverage on-stack replacement to focus on what matters • Build in fork tolerance • Forking is the means by which some VMs achieve parallelism • Difficult to manage compilation efficiently across multiple processes
  • 36.
    The road toopen-source • Early results are very promising for all our technologies • Next steps • Some components in the toolkit are ready (Port, Thread, Trace, GC) • Compiler technology needs more time • Balance refactoring work against developing proof points • Engage with runtime communities and partners • I invite your feedback on our open proposal, our compiler toolkit, or your interest in becoming involved Daryl Maier O/S JIT Lead maier@ca.ibm.com John Duimovich CTO, IBM Runtime Technologies John_Duimovich@ca.ibm.com Mark Stoodley O/S Project Lead mstoodle@ca.ibm.com
  • 37.
    Acknowledgements • Clip arton slides 6 and 7 was sourced from • https://openclipart.org/image/2400px/svg_to_png/92065/paro-AL- calling.png