Bridging Islands of Specialized Code using Macros and Reified Types

Parametric polymorphism in Scala suffers from the usual drawback of erasure on the Java Virtual Machine: primitive values are boxed, leading to indirect access, wasteful use of heap memory and lack of cache locality. For performance-critical parts of the code, the Scala compiler introduces specialization, a transformation that duplicates and adapts the bodies of classes and methods for primitive types. Specializing code can speed up execution by an order of magnitude, but only if the code is called from monomorphic sites or from other specialized code. Still, if these “islands” of specialized code are called from generic code, their performance becomes similar to that of generic code, losing optimality. To address this, our project builds high performance “bridges” between “islands” of specialized code, removing the requirement that full traces need to be specialized: We use macros to delimit performance-critical “gaps” between specialized code, which we also specialize. We then use reified types to dispatch the correct specialized variant, thus recovering performance across the “islands”. Our transformation obtains speedups up to 30x and around 12x in average compared to generic only code, by enabling specialization to completely remove boxing and reach its full potential

Miniboxing: Improving the Speed to Code Size Tradeoff in Parametric Polymorphism Translations

Martin Odersky, Cristian Talau, Vlad Ureche

Parametric polymorphism enables code reuse and type safety. Underneath the uniform interface exposed to programmers, however, its low level implementation has to cope with inherently non-uniform data: value types of different sizes and semantics (bytes, integers, floating point numbers) and reference types (pointers to heap objects). On the Java Virtual Machine, parametric polymorphism is currently translated to bytecode using two competing approaches: homogeneous and heterogeneous. Homogeneous translation requires boxing, and thus introduces indirect access delays. Heterogeneous translation duplicates and adapts code for each value type individually, producing more bytecode. Therefore bytecode speed and size are at odds with each other. This paper proposes a novel translation that significantly reduces the bytecode size without affecting the execution speed. The key insight is that larger value types (such as integers) can hold smaller ones (such as bytes) thus reducing the duplication necessary in heterogeneous translations. In our implementation, on the Scala compiler, we encode all primitive value types in long integers. The resulting bytecode approaches the performance of monomorphic code, matches the performance of the heterogeneous translation and obtains speedups of up to 22x over the homogeneous translation, all with modest increases in size.

2013

Platform-independent profiling in a virtual execution environment

Philippe Moret

Virtual execution environments, such as the Java virtual machine, promote platform-independent software development. However, when it comes to analyzing algorithm complexity and performance bottlenecks, available tools focus on platform-specific metrics, such as the CPU time consumption on a particular system. Other drawbacks of many prevailing profiling tools are high overhead, significant measurement perturbation, as well as reduced portability of profiling tools, which are often implemented in platform-dependent native code. This article presents a novel profiling approach, which is entirely based on program transformation techniques, in order to build a profiling data structure that provides calling-context-sensitive program execution statistics. We explore the use of platform-independent profiling metrics in order to make the instrumentation entirely portable and to generate reproducible profiles. We implemented these ideas within a Java-based profiling tool called JP. A significant novelty is that this tool achieves complete bytecode coverage by statically instrumenting the core runtime libraries and dynamically instrumenting the rest of the code. JP provides a small and flexible API to write customized profiling agents in pure Java, which are periodically activated to process the collected profiling information. Performance measurements point out that, despite the presence of dynamic instrumentation, JP causes significantly less overhead than a prevailing tool for the profiling of Java code.

2008

Improving the Interoperation between Generics Translations

Romain Michel Beguet, Martin Odersky, Milos Stojanovic, Nicolas Alexander Stucki, Vlad Ureche

Generics on the Java platform are compiled using the erasure transformation, which only supports by-reference values. This causes slowdowns when generics operate on primitive types, such as integers, as they have to be transformed into reference-based objects. Project Valhalla is an effort to remedy this problem by specializing classes at load-time so they can efficiently handle primitive values. In its current early prototype, the Valhalla compilation scheme limits the interaction between specialized and erased generics, thus preventing certain useful code patterns from being expressed. Scala has been using compile-time specialization for 6 years and has three generics compilation schemes working side by side. In Scala, programmers are allowed to write code that freely exercises the interaction between the different compilation schemes, at the expense of introducing subtle performance issues. Similar performance issues can affect Valhalla-enabled bytecode, whether the code was written in Java or translated from other JVM languages. In this context we explain how we help programmers avoid these performance regressions in the miniboxing transformation: (1) by issuing actionable performance advisories that steer programmers away from performance regressions and (2) by providing alternatives to the standard library constructs that use the miniboxing encoding, thus avoiding the conversion overhead.

ACM2015