Analyzing the State Behavior of Programs

It is generally agreed that the unrestricted use of state can make a program hard to understand, hard to compile, and hard to execute, and that these difficulties increase in the presence of parallel hardware. This problem has led some to suggest that constructs that allow state should be banished from programming languages. But state is also a very useful phenomenon: some tasks are extremely difficult to accomplish without it, and sometimes the most perspicuous expression of an algorithm is one that makes use of state. Instead of outlawing state, we should be trying to understand it, so that we can make better use of it. I propose a way of modeling systems in which the phenomenon of state occurs. I propose that systems that exhibit state-like behavior are those systems that must rely on their own nonlocal structure in order to function correctly, and I make this notion of nonlocal structure precise. This characterization offers some new insights into why state seems to cause the problems that it does. I propose to construct a compiler that takes advantage of these insights to achieve some of the benefits normally associated with purely functional programming systems.MIT Artificial Intelligence Laborator

CL1 Manual

CL1 is a prototyping language for programming a Connection Machine. It supports a model of the Connection Machine as a collection of tiny conventional machines (process elements), each with its own independent program counter.MIT Artificial Intelligence Laborator

Implementing Distributed Systems Using Linear Naming

Linear graph reduction is a simple computational model in which the cost of naming things is explicitly represented. The key idea is the notion of "linearity". A name is linear if it is only used once, so with linear naming you cannot create more than one outstanding reference to an entity. As a result, linear naming is cheap to support and easy to reason about. Programs can be translated into the linear graph reduction model such that linear names in the program are implemented directly as linear names in the model. Nonlinear names are supported by constructing them out of linear names. The translation thus exposes those places where the program uses names in expensive, nonlinear ways. Two applications demonstrate the utility of using linear graph reduction: First, in the area of distributed computing, linear naming makes it easy to support cheap cross-network references and highly portable data structures, Linear naming also facilitates demand driven migration of tasks and data around the network without requiring explicit guidance from the programmer. Second, linear graph reduction reveals a new characterization of the phenomenon of state. Systems in which state appears are those which depend on certain -global- system properties. State is not a localizable phenomenon, which suggests that our usual object oriented metaphor for state is flawed

Squid: Type-Safe, Hygienic, and Reusable Quasiquotes

Quasiquotes have been shown to greatly simplify the task of metaprogramming. This is in part because they hide the data structures of the intermediate representation (IR), instead allowing metaprogrammers to use the concrete syntax of the language they manipulate. Scala has had ``syntactic'' quasiquotes for a long time, but still misses a statically-typed version like in MetaOCaml, Haskell and F#. This safer flavor of quasiquotes has been particularly useful for staging and domain-specific languages. In this paper we present Squid, a metaprogramming system for Scala that fills this gap. Squid quasiquotes are novel in three ways: they are the first statically-typed quasiquotes we know that allow code inspection (via pattern matching); they are implemented purely as a macro library, without modifications to the compiler; and they are reusable in the sense that they can manipulate different IRs. Adapting (or binding) a new IR to Squid is done simply by implementing a well-defined interface in the style of object algebras (i.e., tagless-final). We detail how Squid is implemented, leveraging the metaprogramming tools already offered by Scala, and show three application examples: the definition of a binding for a DSL in the style of LMS; a safe ANF conversion; and the introduction of type-safe, hygienic macros as an alternative to the current macro system

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Reification without Evaluation

Constructing self-referential systems, such as Brian Smith's 3-Lisp language, is actually more straightforward than you think. Anyone can build an infinite tower of processors (where each processor implements the processor at the next level below) by employing some common sense and one simple trick. In particular, it is not necessary to re-design quotation, take a stand on the relative merits of evaluation vs. normalization, or treat continuations as meta-level objects. This paper presents a simple programming language interpreter that illustrates how this can be done. By keeping its expression evaluator entirely separate from the mechanisms that implement its infinite tower, this interpreter avoids many troublesome aspects of previous self-referential programming languages. Given these basically straightforward techniques, processor towers might be easily constructed for a wide variety of systems to enable them to manipulate and reason about themselves

Quasiquotation in Lisp

Quasiquotation is the technology commonly used in Lisp to write program-generating programs. In this paper I will review the history and development of this technology, and explain why it works so well in practice