The Standard ML of New Jersey compiler has been under development for five years now. We have developed a robust and complete environment for Standard ML that supports the implementation of large software systems and generates efficient code. The compiler has also served as a laboratory for developing novel implementation techniques for a sophisticated type and module system, continuation based code generation, efficient pattern matching, and concurrent programming features.

We implemented a continuation-passing style (CPS) code generator for ML. Our CPS language is represented as an ML datatype in which all functions are named and most kinds of ill-formed expressions are impossible. We separate the code generation into phases that rewrite this representation into ever-simpler forms. Closures are represented explicitly as records, so that closure strategies can be communicated from one phase to another. No stack is used. Our benchmark data shows that the new method is an improvement over our previous, abstract-machine based code generator.

Many modern compilers implement function calls (or returns) in two steps: first, a closure environment is properly installed to provide access for free variables in the target program fragment; second, the control is transferred to the target by a "jump with arguments (or results)." Closure conversion, which decides where and how to represent closures at runtime, is a crucial step in compilation of functional languages. We have a new algorithm that exploits the use of compile-time control and data flow information to optimize closure representations. By extensive closure sharing and allocating as many closures in registers as possible, our new closure conversion algorithm reduces heap allocation by 36% and memory fetches for local/global variables by 43%; and improves the already-efficient code generated by the Standard ML of New Jersey compiler by about 17% on a DECstation 5000. Moreover, unlike most other approaches, our new closure allocation scheme satisfies the strong "safe for sp...

The runtime data structures of the Standard ML of New Jersey compiler are simple yet general. As a result, code generators are easy to implement, programs execute quickly, garbage collectors are easy to implement and work efficiently, and a variety of runtime facilities can be provided with ease.

Many language theoreticians have taken great efforts in designing higher-level programming languages that are more elegant and more expressive than conventional languages. However, few of these new languages have been implemented very efficiently. The result is that most software engineers still prefer to use conventional languages, even though the new higherlevel languages offer a better and simpler programming model. This dissertation concentrates on improving the performance of programs written in Standard ML (SML)---a statically typed functional language---on today's RISC machines. SML poses tough challenges to efficient implementations: very frequent function calls, polymorphic types, recursive data structures, higher-order functions, and first-class continuations. This dissertation presents the design and evaluation of several new compilation techniques that meet these challenges by taking advantage of some of the higher-level language features in SML. Type-directed compilation ...

Modern compilers often implement function calls (or returns) in two steps: first, a “closure ” environment is properly installed to provide access for free variables in the target program fragment; second, the control is transferred to the target by a “jump with arguments (or results). ” Closure conversion—which decides where and how to represent closures at runtime—is a crucial step in the compilation of functional languages. This paper presents a new algorithm that exploits the use of compile-time control and data flow information to optimize function calls. By extensive closure sharing and allocating as many closures in registers as possible, our new closure-conversion algorithm reduces heap allocation by 36 % and memory fetches for local and global variables by 43%; and improves the already efficient code generated by an earlier version of the Standard ML of New Jersey compiler by about 17 % on a DECstation 5000. Moreover, unlike most other approaches, our new closure-allocation scheme satisfies the strong safe-for-space-complexity rule, thus achieving good asymptotic space usage.