Posts Tagged ‘Invokedynamic’
[DevoxxBE2013] Lambda: A Peek Under the Hood
Brian Goetz, Java Language Architect at Oracle, offers an illuminating dissection of lambda expressions in Java SE 8, transcending syntactic sugar to reveal the sophisticated machinery powering this evolution. Renowned for Java Concurrency in Practice and leadership in JSR 335, Brian demystifies lambdas’ implementation atop invokedynamic from Java SE 7. His session, eschewing introductory fare, probes the VM’s strategies for efficiency, contrasting naive inner-class approaches with optimized bootstrapping and serialization.
Lambdas, Brian asserts, unlock expressive potential for applications and libraries, but their true prowess lies in performance rivaling or surpassing inner classes—without the bloat. Through benchmarks and code dives, he showcases flexibility and future-proofing, underscoring the iterative path to a robust design.
From Syntax to Bytecode: The Bootstrap Process
Brian traces lambdas’ lifecycle: source code desugars to invokedynamic callsites, embedding a “recipe” for instantiation. The bootstrap method, invoked once per callsite, crafts a classfile dynamically, caching for reuse.
This declarative embedding, Brian illustrates, avoids inner classes’ per-instance overhead, yielding leaner bytecode and faster captures—non-capturing lambdas hit 1.5x inner-class speeds in early benchmarks.
Optimization Strategies and Capture Semantics
Capturing lambdas, Brian explains, leverage local variable slots via synthetic fields, minimizing allocations. He contrasts “eager” (immediate class creation) with “lazy” (deferred) strategies, favoring the latter for reduced startup.
Invokedynamic’s dynamic binding enables profile-guided refinements, promising ongoing gains. Brian’s throughput metrics affirm lambdas’ edge, even in capturing scenarios.
Serialization and Bridge Methods
Serializing lambdas invokes writeReplace to a serialized form, preserving semantics without runtime overhead. Brian demos bridge methods for functional interfaces, ensuring compatibility.
Default methods, he notes, extend interfaces safely, avoiding binary breakage—crucial for library evolution.
Lessons from Language Evolution
Brian reflects on Lambda’s odyssey: discarded ideas like inner-class syntactic variants paved the way for invokedynamic’s elegance. This resilience, he posits, exemplifies evolving languages amid obvious-but-flawed intuitions.
Project Lambda’s resources—OpenJDK docs, JCP reviews—invite deeper exploration, with binary builds for experimentation.
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[DevoxxFR2013] Invokedynamic in 45 Minutes: Unlocking Dynamic Language Performance on the JVM
Lecturer
Charles Nutter has spent over a decade as a Java developer and more than six years leading the JRuby project at Red Hat. Co-lead of JRuby, he works to fuse Ruby’s elegance with the JVM’s power while contributing to other JVM languages and educating the community on advanced virtual machine capabilities. A proponent of open standards, he aims to keep the JVM the premier managed runtime through innovations like invokedynamic.
Abstract
Charles Nutter demystifies invokedynamic, the JVM bytecode instruction introduced in Java 7 to optimize dynamic language implementation. He explains its mechanics—bootstrap methods, call sites, and method handles—through progressive examples, culminating in a toy language interpreter. The presentation contrasts invokedynamic with traditional invokevirtual and invokeinterface, benchmarks performance, and illustrates how it enables JRuby and other languages to approach native Java speeds, paving the way for polyglot JVM ecosystems.
The Problem with Traditional Invocation: Static Assumptions in a Dynamic World
Nutter begins with the JVM’s historical bias toward statically-typed languages. The four classic invocation instructions—invokevirtual, invokeinterface, invokestatic, and invokespecial—assume method resolution at class loading or compile time. For dynamic languages like Ruby, Python, or JavaScript, method existence and signatures are determined at runtime, forcing expensive runtime checks or megamorphic call sites.
JRuby, prior to invokedynamic, relied on reflection or generated bytecodes per call, incurring significant overhead. Even interface-based dispatch suffered from inline cache pollution when multiple implementations competed.
Invokedynamic Mechanics: Bootstrap, Call Sites, and Method Handles
Introduced via JSR-292, invokedynamic defers method linking to a user-defined bootstrap method (BSM). The JVM invokes the BSM once per call site, passing a CallSite object, method name, and type. The BSM returns a MethodHandle—a typed, direct reference to a method—installed into the call site.
Nutter demonstrates a simple BSM:
public static CallSite bootstrap(MethodHandles.Lookup lookup, String name, MethodType type) {
MethodHandle mh = lookup.findStatic(MyClass.class, "target", type);
return new ConstantCallSite(mh);
}
The resulting invokedynamic instruction executes the linked handle directly, bypassing vtable lookups.
Call Site Types and Guarded Invocations
Call sites come in three flavors: ConstantCallSite for immutable linkages, MutableCallSite for dynamic retargeting, and VolatileCallSite for atomic updates. Guarded invocations combine a test (guard) with a target handle:
MethodHandle guard = lookup.findStatic(Guards.class, "isString", MethodType.methodType(boolean.class, Object.class));
MethodHandle target = lookup.findStatic(Handlers.class, "handleString", type);
MethodHandle fallback = lookup.findStatic(Handlers.class, "handleOther", type);
MethodHandle guarded = MethodHandles.guardWithTest(guard, target, fallback);
The JVM inlines the guard, falling back only on failure, enabling polymorphic inline caches.
Building a Toy Language: From Parser to Execution
Nutter constructs a minimal scripting language with arithmetic and print statements. The parser generates invokedynamic instructions with a shared BSM. The BSM resolves operators (+, -, *) to overloaded Java methods based on argument types, caching results per call site.
Execution flows through method handles, achieving near-Java performance. He extends the example to support runtime method missing, emulating Ruby’s method_missing.
Performance Analysis: Benchmarking Invocation Strategies
Nutter presents JMH benchmarks comparing invocation types. invokestatic serves as baseline; invokevirtual adds vtable dispatch; invokeinterface incurs interface check. invokedynamic with ConstantCallSite matches invokestatic, while MutableCallSite aligns with invokevirtual.
Key insight: the JVM’s optimizer treats stable invokedynamic sites as monomorphic, inlining aggressively. JRuby leverages this for core methods, reducing dispatch overhead by 10-100x.
Implications for JVM Languages and Future Evolution
Invokedynamic enables true polyglot JVMs. Nashorn (JavaScript), Dynalink, and Truffle frameworks build upon it. Future enhancements include value types and specialized generics, further reducing boxing.
Nutter concludes that invokedynamic fulfills John Rose’s vision: dynamic dispatch no slower than static, ensuring the JVM’s longevity as a universal runtime.
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[DevoxxBE2012] What’s New in Groovy 2.0?
Guillaume Laforge, the Groovy Project Lead and a key figure in its development since its inception, provided an extensive overview of Groovy’s advancements. Guillaume, employed by the SpringSource division of VMware at the time, highlighted how Groovy enhances developer efficiency and runtime speed with each iteration. He began by recapping essential elements from Groovy 1.8 before delving into the innovations of version 2.0, emphasizing its role as a versatile language on the JVM.
Guillaume underscored Groovy’s appeal as a scripting alternative to Java, offering dynamic capabilities while allowing modular usage for those not requiring full dynamism. He illustrated this with examples of seamless integration, such as embedding Groovy scripts in Java applications for flexible configurations. This approach reduces boilerplate and fosters rapid prototyping without sacrificing compatibility.
Transitioning to performance, Guillaume discussed optimizations in method invocation and arithmetic operations, which contribute to faster execution. He also touched on library enhancements, like improved date handling and JSON support, which streamline common tasks in enterprise environments.
A significant portion focused on modularity in Groovy 2.0, where the core is split into smaller jars, enabling selective inclusion of features like XML processing or SQL support. This granularity aids in lightweight deployments, particularly in constrained settings.
Static Type Checking for Reliability
Guillaume elaborated on static type checking, a flagship feature allowing early error detection without runtime overhead. He demonstrated annotating classes with @TypeChecked to enforce type safety, catching mismatches in assignments or method calls at compile time. This is particularly beneficial for large codebases, where dynamic typing might introduce subtle bugs.
He addressed extensions for domain-specific languages, ensuring type inference works even in complex scenarios like builder patterns. Guillaume showed how this integrates with IDEs for better code completion and refactoring support.
Static Compilation for Performance
Another cornerstone, static compilation via @CompileStatic, generates bytecode akin to Java’s, bypassing dynamic dispatch for speed gains. Guillaume benchmarked scenarios where this yields up to tenfold improvements, ideal for performance-critical sections.
He clarified that dynamic features remain available selectively, allowing hybrid approaches. This flexibility positions Groovy as a bridge between scripting ease and compiled efficiency.
InvokeDynamic Integration and Future Directions
Guillaume explored JDK7’s invokedynamic support, optimizing dynamic calls for better throughput. He presented metrics showing substantial gains in invocation-heavy code, aligning Groovy closer to Java’s performance.
Looking ahead, he previewed Groovy 2.1 enhancements, including refined type checking for DSLs and complete invokedynamic coverage. For Groovy 3.0, a revamped meta-object protocol and Java 8 lambda compatibility were on the horizon, with Groovy 4.0 adopting ANTLR4 for parsing.
In Q&A, Guillaume addressed migration paths and community contributions, reinforcing Groovy’s evolution as responsive to user needs.
His session portrayed Groovy as maturing into a robust, adaptable toolset for modern JVM development, balancing dynamism with rigor.
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[DevoxxBE2012] On the Road to JDK 8: Lambda, Parallel Libraries, and More
Joseph Darcy, a key figure in Oracle’s JDK engineering team, presented an insightful overview of JDK 8 developments. With extensive experience in language evolution, including leading Project Coin for JDK 7, Joseph outlined the platform’s future directions, balancing innovation with compatibility.
He began by contextualizing JDK 8’s major features, particularly lambda expressions and default methods, set for release in September 2013. Joseph polled the audience on JDK usage, noting the impending end of public updates for JDK 6 and urging transitions to newer versions.
Emphasizing a quantitative approach to compatibility, Joseph described experiments analyzing millions of lines of code to inform decisions, such as lambda conversions from inner classes.
Evolving the Language with Compatibility in Mind
Joseph elaborated on the JDK’s evolution policy, prioritizing binary compatibility while allowing measured source and behavioral changes. He illustrated this with diagrams showing compatibility spaces for different release types, from updates to full platforms.
A core challenge, he explained, is evolving interfaces compatibly. Unlike classes, interfaces cannot add methods without breaking implementations. To address this, JDK 8 introduces default methods, enabling API evolution without user burden.
This ties into lambda support, where functional interfaces facilitate closures. Joseph contrasted this with past changes like generics, which preserved migration compatibility through erasure, avoiding VM modifications.
Lambda Expressions and Implementation Techniques
Diving into lambdas, Joseph defined them as anonymous methods capturing enclosing scope values. He traced their long journey into Java, noting their ubiquity in modern languages.
For implementation, Joseph rejected simple inner class translations due to class explosion and performance overhead. Instead, JDK 8 leverages invokedynamic from JDK 7, allowing runtime strategies like class spinning or method handles.
This indirection decouples binary representation from implementation, enabling optimizations. Joseph shared benchmarks showing non-capturing lambdas outperforming inner classes, especially multithreaded.
Serialization posed challenges, resolved via indirection to reconstruct lambdas independently of runtime details.
Parallel Libraries and Bulk Operations
Joseph highlighted how lambdas enable powerful libraries, abstracting behavior as generics abstract types. Streams introduce pipeline operations—filter, map, reduce—with laziness and fork-join parallelism.
Using the Fork/Join Framework from JDK 7, these libraries handle load balancing implicitly, encapsulating complexity. Joseph demonstrated conversions from collections to streams, facilitating scalable concurrent applications.
Broader JDK 8 Features and Future Considerations
Beyond lambdas, Joseph mentioned annotations on types and repeating annotations, enhancing expressiveness. He stressed deferring decisions to avoid constraining future evolutions, like potential method reference enhancements.
In summary, Joseph portrayed JDK 8 as a coordinated update across language, libraries, and VM, inviting community evaluation through available builds.