Abstract not found

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.

Many parallel algorithms are naturally expressed at a fine level of granularity, often finer than a MIMD parallel system can exploit efficiently. Most builders of parallel systems have looked to either the programmer or a parallelizing compiler to increase the granularity of such algorithms. In this paper we explore a third approach to the granularity problem by analyzing two strategies for combining parallel tasks dynamically at run-time. We reject the simpler load-based inlining method, where tasks are combined based on dynamic load level, in favor of the safer and more robust lazy task creation method, where tasks are created only retroactively as processing resources become available. These strategies grew out of work on Mul-T [15], an efficient parallel implementation of Scheme, but could be used with other languages as well. We describe our Mul-T implementations of lazy task creation for two contrasting machines, and present performance statistics which show the method's effectiveness. Lazy task creation allows efficient execution of naturally expressed algorithms of a substantially finer grain than possible with previous parallel Lisp systems.

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.

The Alewife multiprocessor project focuses on the architecture and design of a large-scale parallel machine. The machine uses a low-dimensional direct interconnection network to provide scalable communication bandwidth, while allowing the exploitation of locality. Despite its distributed-memory architecture, Alewife allows efficient shared-memory programming through a multilayered approach to locality management. A new scalable cache-coherence scheme called LimitLESS directories allows the use of caches for reducing communication latency and network bandwidth requirements. Alewife also employs run-time and compile-time methods for partitioning and placement of data and processes to enhance communication locality. While the above methods attempt to minimize communication latency, communication with distant processors cannot be completely avoided. Alewife's processor, Sparcle, is designed to tolerate these latencies by rapidly switching between threads of computation. This paper describe...

Today's type-safe low-level languages rely on garbage collection to recycle heap-allocated objects safely. We present LTAL, a safe, low-level, yet simple language that "stands on its own": it guarantees safe execution within a fixed memory space, without relying on external run-time support. We demonstrate the expressiveness of LTAL by giving a type-preserving compiler for the functional core of ML. But this independence comes at a steep price: LTAL's type system imposes a draconian discipline of linearity that ensures that memory can be reused safely, but prohibits any useful kind of sharing. We present the results of experiments with a prototype LTAL system that show just how high the price of linearity can be.

Languages that support abstraction and modular structure, such as Standard ML, Modula, Ada, and (more or less) C++, may have deeply nested dependency hierarchies among source files. In ML the problem is particularly severe because ML's powerful parameterized module (functor) facility entails dependencies among implementation modules, not just among interfaces.

An important function of any register allocator is to target registers so as to eliminate copy instructions. Graph-coloring register allocation is an elegant approach to this problem. If the source and destination of a move instruction do not interfere, then their nodes can be coalesced in the interference graph. Chaitin's coalescing heuristic could make a graph uncolorable (i.e., introduce spills); Briggs et al. demonstrated a conservative coalescing heuristic that preserves colorability. But Briggs's algorithm is too conservative, and leaves too many move instructions in our programs. We show how to interleave coloring reductions with Briggs's coalescing heuristic, leading to an algorithm that is safe but much more aggressive. 1 Introduction Graph coloring is a powerful approach to register allocation and can have a significant impact on the execution of compiled code. A good register allocator does copy propagation, eliminating many move instructions by "coloring" the source tempor...

Proof-carrying code is a framework for proving the safety of machine-language programs with a machinecheckable proof. Such proofs have previously defined type-checking rules as part of the logic. We show a universal type framework for proof-carrying code that will allow a code producer to choose a programming language, prove the type rules for that language as lemmas in higher-order logic, then use those lemmas to prove the safety of a particular program. We show how to handle traversal, allocation, and initialization of values in a wide variety of types, including functions, records, unions, existentials, and covariant recursive types. 1

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...