We describe a new compiler for Standard ML called TIL, that is based on four technologies: intensional polymorphism, tag-free garbage collection, conventional functional language optimization, and loop optimization. We use intensional polymorphism and tag-free garbage collection to provide specialized representations, even though SML is a polymorphic language. We use conventional functional language optimization to reduce the cost of intensional polymorphism, and loop optimization to generate good code for recursive functions. We present an example of TIL compiling an SML function to machine code, and compare the performance of TIL code against that of a widely used compiler, Standard ML of New Jersey.

Generational garbage collection algorithms achieve efficiency because newer records point to older records; the only way an older record can point to a newer record is by a store operation to a previously-created record, and such operations are rare in many languages. A garbage collector that concentrates just on recently allocated records can take advantage of this fact. Such a garbage collector can be so efficient that the allocation of records costs more than their disposal. A scheme for quick record allocation attacks this bottleneck. Many garbage-collected environments don't know when to ask the operating system for more memory. A robust heuristic solves this problem. This paper presents a simple, efficient, low-overhead version of generational garbage collection with fast allocation, suitable for implementation in a Unix environment.

The views and conclusions contained in this document are those of the authors and should not be interpreted as representing o cial policies, either expressed or implied, of the Advanced Research Projects Agency or the U.S. Government. Any opinions, ndings, and conclusions or recommendations expressed in this material are those of the We study the typing properties of closure conversion for simply-typed and polymorphic-calculi. Unlike most accounts of closure conversion, which only treat the untyped-calculus, we translate well-typed source programs to well-typed target programs. This allows later compiler phases to take advantage of types for representation analysis and tag-free garbage collection, and it facilitates correctness proofs. Our account of closure conversion for the simply-typed language takes advantage of a simple model of objects by mapping closures to existentials. Closure conversion for the polymorphic language requires additional type machinery, namely translucency in the style of Harper and Lillibridge's module calculus, to express the type of a closure.

Compilers for monomorphic languages, such as C and Pascal, take advantage of types to determine data representations, alignment, calling conventions, and register selection. However, these languages lack important features including polymorphism, abstract datatypes, and garbage collection. In contrast, modern programming languages such as Standard ML (SML), provide all of these features, but existing implementations fail to take full advantage of types. The result is that performance of SML code is quite bad when compared to C. In this thesis, I provide a general framework, called type-directed compilation, that allows compiler writers to take advantage of types at all stages in compilation. In the framework, types are used not only to determine efficient representations and calling conventions, but also to prove the correctness of the compiler. A key property of typedirected compilation is that all but the lowest levels of the compiler use typed intermediate languages. An advantage of this approach is that it provides a means for automatically checking the integrity of the resulting code. An important

Most specifications of garbage collectors concentrate on the low-level algorithmic details of how to find and preserve accessible objects. Often, they focus on bit-level manipulations such as "scanning stack frames," "marking objects," "tagging data," etc. While these details are important in some contexts, they often obscure the more fundamental aspects of memory management: what objects are garbage and why? We develop a series of calculi that are just low-level enough that we can express allocation and garbage collection, yet are sufficiently abstract that we may formally prove the correctness of various memory management strategies. By making the heap of a program syntactically apparent, we can specify memory actions as rewriting rules that allocate values on the heap and automatically dereference pointers to such objects when needed. This formulation permits the specification of garbage collection as a relation that removes portions of the heap without affecting the outcome of the evaluation. Our high-level approach allows us to specify in a compact manner a wide variety of memory management techniques, including standard trace-based garbage collection (i.e., the family of copying and mark/sweep collection algorithms), generational collection, and type-based, tag-free collection. Furthermore, since the definition of garbage is based on the semantics of the underlying language instead of the conservative approximation of inaccessibility, we are able to specify and prove the idea that type inference can be used to collect some objects that are accessible but never used.

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 modern programming environments use tag bits at runtime to distinguish objects of different types. This is particularly common in systems with garbage collection, since the garbage collector must be able to distinguish pointers from non-pointers, and to learn the length of records pointed to. The use of tag bits leads to inefficiency. In addition to the obvious space overhead (tag bits and record descriptors occupy memory space), there is a time overhead: tag bits must be stripped off of data before arithmetic operations are performed, and re-attached to the data when it is stored into memory. This takes either extra instructions at runtime, or special tag-handling hardware, or both. This paper shows how the use of tag bits, record descriptor words, explicit type parameters, and the like can be avoided in languages (like ML) with static polymorphic typechecking. Though a form of tag will still be required for user-defined variant records, all other type information ...

Automatic storage management, or garbage collection, is a feature usually associated with languages oriented toward ‘‘symbolic processing,’’ such as Lisp or Prolog; it is seldom associated with ‘‘systems’’ languages, such as C and C++. This report surveys techniques for performing garbage collection for languages such as C and C++, and presents an implementation of a concurrent copying collector for C++. The report includes performance measurements on both a uniprocessor and a multiprocessor.

Abstract The trends in software development are towards larger programs, more complex programs, and more use of programs as "component software. " These trends mean that the features of modern programming languages are becoming more important than ever before. Programming languages need to have features such as strong typing, a module system, polymorphism, automatic storage management, and higher-order functions. In short, modern programming languages are becoming more important than ever before.

The views and conclusions contained in this document arethose of the authors and should not be interpreted as representing o cial policies, either expressed or implied, of the Advanced We present a static and dynamic semantics for an abstract machine that evaluates expressions of a polymorphic programming language. Unlike traditional semantics, our abstract machine exposes many important issues of memory management, such as value sharing and control representation. We prove the soundness of the static semantics with respect to the dynamic semantics using traditional techniques. We then show how these same techniques may be used to establish the soundness of various memory management strategies, including type-based, tag-free garbage collection� tail-call elimination � and environment strengthening. Keywords: management Type theory and operational semantics are remarkably e ective tools for programming