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

We describe a small set of additions to Scheme to support objectoriented programming, including a form of multiple inheritance. The extensions proposed are in keeping with the spirit of the Scheme language and consequently differ from Lisp-based object systems such as Flavors and the Common Lisp Object System. Our extensions mesh neatly with the underlying Scheme system. We motivate our design with examples, and then describe implementation techniques that yield efficiency comparable to dynamic object-oriented language implementations considered to be high performance. The complete design has an almost-portable implementation, and the core of this design comprises the object system used in T, a dialect of Scheme. The applicative bias of our approach is unusual in object-oriented programming systems. This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is ...