Object-oriented programming languages promise to improve programmer productivity by supporting abstract data types, inheritance, and message passing directly within the language. Unfortunately, traditional implementations of object-oriented language features, particularly message passing, have been much slower than traditional implementations of their non-object-oriented counterparts: the fastest existing implementation of Smalltalk-80 runs at only a tenth the speed of an optimizing C implementation. The dearth of suitable implementation technology has forced most object-oriented languages to be designed as hybrids with traditional non-object-oriented languages, complicating the languages and making programs harder to extend and reuse. This dissertation describes a collection of implementation techniques that can improve the run-time performance of object-oriented languages, in hopes of reducing the need for hybrid languages and encouraging wider spread of purely object-oriented langu...

Abstract. We have developed and implemented techniques that double the performance of dynamically-typed object-oriented languages. Our SELF implementation runs twice as fast as the fastest Smalltalk implementation, despite SELF’s lack of classes and explicit variables. To compensate for the absence of classes, our system uses implementation-level maps to transparently group objects cloned from the same prototype, providing data type information and eliminating the apparent space overhead for prototype-based systems. To compensate for dynamic typing, user-defined control structures, and the lack of explicit variables, our system dynamically compiles multiple versions of a source method, each customized according to its receiver’s map. Within each version the type of the receiver is fixed, and thus the compiler can statically bind and inline all messages sent to self. Message splitting and type prediction extract and preserve even more static type information, allowing the compiler to inline many other messages. Inlining dramatically improves performance and eliminates the need to hard-wire low-level methods such as +, ==, and ifTrue:. Despite inlining and other optimizations, our system still supports interactive programming environments. The system traverses internal dependency lists to invalidate all compiled methods

Object-oriented programming languages confer many benefits, including abstraction, which lets the programmer hide the details of an object’s implementation from the object’s clients. Unfortunately, crossing abstraction boundaries often incurs a substantial run-time overhead in the form of frequent procedure calls. Thus, pervasive use of abstraction, while desirable from a design standpoint, may be impractical when it leads to inefficient programs. Aggressive compiler optimizations can reduce the overhead of abstraction. However, the long compilation times introduced by optimizing compilers delay the programming environment‘s responses to changes in the program. Furthermore, optimization also conflicts with source-level debugging. Thus, programmers are caught on the horns of two dilemmas: they have to choose between abstraction and efficiency, and between responsive programming environments and efficiency. This dissertation shows how to reconcile these seemingly contradictory goals by performing optimizations lazily. Four new techniques work together to achieve high performance and high responsiveness: • Type feedback achieves high performance by allowing the compiler to inline message sends based on information extracted from the runtime system. On average, programs run 1.5 times faster than the previous SELF system; compared to a commercial Smalltalk implementation, two medium-sized benchmarks run about three times faster. This level of performance is obtained with a compiler that is both simpler and faster than previous SELF compilers. • Adaptive optimization achieves high responsiveness without sacrificing performance by using a fast nonoptimizing compiler to generate initial code while automatically recompiling heavily used parts of the program with an optimizing compiler. On a previous-generation workstation like the SPARCstation-2, fewer than 200 pauses exceeded 200 ms during a 50-minute interaction, and 21 pauses exceeded one second. On a currentgeneration workstation, only 13 pauses exceed 400 ms. • Dynamic deoptimization shields the programmer from the complexity of debugging optimized code by transparently recreating non-optimized code as needed. No matter whether a program is optimized or not, it can always be stopped, inspected, and single-stepped. Compared to previous approaches, deoptimization allows more debugging while placing fewer restrictions on the optimizations that can be performed. • Polymorphic inline caching generates type-case sequences on-the-fly to speed up messages sent from the same call site to several different types of object. More significantly, they collect concrete type information for the optimizing compiler. With better performance yet good interactive behavior, these techniques make exploratory programming possible both for pure object-oriented languages and for application domains requiring higher ultimate performance, reconciling exploratory programming, ubiquitous abstraction, and high performance.

Selector table indexing is a simple technique for method lookup in object-oriented languages, which yields good performance, is well suited to multiple inheritance and dynamic typing, but is generally disregarded for its prohibitive memory consumption. The large memory footprint is caused by keeping a table of methods, indexed by a selectorcode, for each class in the system. These tables are sparsely filled. A sparse array implementation is presented, which reduces the memory consumption by an order of magnitude, while performing retrieval in constant time. This implementation is discussed in the context of a real programming environment, and compared to selector coloring, a different memory-optimizing technique. The method is shown to be complementary to dynamic caching techniques such as inline caching. 1. Introduction Dynamic binding is both the basis of polymorphism and a source of inefficiency in objectoriented languages. The association of a message selector to its implementatio...

Object-oriented code looks different from procedural code. The main difference is the increased frequency of polymorphic calls. A polymorphic call looks like a procedural call, but where a procedural call has only one possible target subroutine, a polymorphic call can result in the execution of one of several different subroutines. The choice is made at run time, and depends on the type of the receiving object (the first argument). Polymorphic calls enable the construction of clean, modular code design. They allow the programmer to invoke operations on an object without knowing its exact type in advance. This flexibility incurs an overhead: in general, polymorphic calls must be resolved at run time. The overhead of this run time polymorphic call resolution can lead a programmer to sacrifice clarity of design for more efficient code, by replacing instances of polymorphic calls by several single-target procedural calls, removing run time polymorphism. This practice typically leads to a m...

Selector table indexing is a simple technique for method lookup in object-oriented languages, which yields good performance, is well suited to multiple inheritance and dynamic typing, but is generally disregarded for its prohibitive memory consumption. The large memory footprint is caused by keeping a table of methods, indexed by a selectorcode, for each class in the system. These tables are sparsely filled. A sparse array implementation is presented, which reduces the memory consumption by an order of magnitude, while performing retrieval in constant time. This implementation is discussed in the context of a real programming environment, and compared to selector coloring, a different memory-optimizing technique. The method is shown to be complementary to dynamic caching techniques such as inline caching. 1.