Hierarchical memory management for mutable state

It is well known that modern functional programming languages are naturally amenable to parallel programming. Achieving efficient parallelism using functional languages, however, remains difficult. Perhaps the most important reason for this is their lack of support for efficient in-place updates, i.e., mutation, which is important for the implementation of both parallel algorithms and the run-time system services (e.g., schedulers and synchronization primitives) used to execute them. In this paper, we propose techniques for efficient mutation in parallel functional languages. To this end, we couple the memory manager with the thread scheduler to make reading and updating data allocated by nested threads efficient. We describe the key algorithms behind our technique, implement them in the MLton Standard ML compiler, and present an empirical evaluation. Our experiments show that the approach performs well, significantly improving efficiency over existing functional language implementations.

An important aspect of building robust systems that execute on dedicated hardware and perhaps in constrained environments is to control and manage the effects performed by program code. We present ReML, a higher-order statically-typed functional language, which allows programmers to be explicit about the effects performed by program code and in particular effects related to memory management Allowing programmers to be explicit about effects, the regions in which values reside, and the constraints under which code execute, makes programs robust to changes in the program source code and to compiler updates, including compiler optimisations. ReML is integrated with a polymorphic inference system that builds on top of region-inference, as it is implemented in the MLKit, a Standard ML compiler that uses region-based memory management as its primary memory management scheme.

An important feature of functional programs is that they are parallel by default. Implementing an efficient parallel functional language, however, is a major challenge, in part because the high rate of allocation and freeing associated with functional programs requires an efficient and scalable memory manager. In this paper, we present a technique for parallel memory management for strict functional languages with nested parallelism. At the highest level of abstraction, the approach consists of a technique to organize memory as a hierarchy of heaps, and an algorithm for performing automatic memory reclamation by taking advantage of a disentanglement property of parallel functional programs. More specifically, the idea is to assign to each parallel task its own heap in memory and organize the heaps in a hierarchy/tree that mirrors the hierarchy of tasks. We present a nested-parallel calculus that specifies hierarchical heaps and prove in this calculus a disentanglement property, which prohibits a task from accessing objects allocated by another task that might execute in parallel. Leveraging the disentanglement property, we present a garbage collection technique that can operate on any subtree in the memory hierarchy concurrently as other tasks (and/or other collections) proceed in parallel. We prove the safety of this collector by formalizing it in the context of our parallel calculus. In addition, we describe how the proposed techniques can be implemented on modern shared-memory machines and present a prototype implementation as an extension to MLton, a high-performance compiler for the Standard ML language. Finally, we evaluate the performance of this implementation on a number of parallel benchmarks.

Concurrent Clean is an experimental, lazy, higher-order parallel functional programming language based on term graph rewriting. An important difference with other languages is that in Clean graphs are manipulated and not terms. This can be used by the programmer to control communication and sharing of computation. Cyclic structures can be defined. Concurrent Clean furthermore allows to control the (parallel) order of evaluation to make efficient evaluation possible. With help of sequential annotations the default lazy evaluation can be locally changed into eager evaluation. The language enables the definition of partially strict data structures which make a whole new class of algorithms feasible in a functional language. A powerful and fast strictness analyser is incorporated in the system. The quality of the code generated by the Clean compiler has been greatly improved such that it is one of the best code generators for a lazy functional language. Two very powerful parallel annotations enable the programmer to define concurrent functional programs with arbitrary process topologies. Concurrent Clean is set up in such a way that the efficiency achieved for the sequential case can largely be maintained for a parallel implementation on loosely coupled parallel machine architectures.

Parallel functional languages often use meta-linguistic annotations to provide control over parallel evaluation. In this paper we explore a flexible mechanism to control when an expression is evaluated: first-class monadic schedules . We discuss the advantages of using such first-class values over traditional annotation-based systems. In particular, it is often desirable to make decisions about the operational behavior of parallel programs depending on the dynamic state of the system. For example, we may want to measure the system load before deciding to evaluate expressions in parallel. For this purpose, we show how monads can be used to access dynamic system parameters in a referentially transparent manner (up to termination).As a mechanism to reason about schedules, we present a set of algebraic properties that any implementation of schedules must satisfy. We also describe an implementation that translates schedules into a dialect of Scheme extended with futures . We prove that this implementation satisfies the given set of algebraic properties, and give performance results for a parallel solution to the n -body problem using the Barnes--Hut method.Although our ideas were developed specifically for nonstrict functional languages such as Haskell, we briefly discuss how they can be used with strict functional languages and imperative languages as well.

In an impure functional language, there are programs whose behaviour is completely functional (in that they behave extensionally on inputs), but the functions they compute cannot be written in the purely functional fragment of the language. That is, the class of programs with functional behaviour is more expressive than the usual class of pure functional programs. In this paper we introduce this extended class of "functional" programs by means of examples in Standard ML, and explore what they might have to offer to programmers and language implementors.After reviewing some theoretical background, we present some examples of functions of the above kind, and discuss how they may be implemented. We then consider two possible programming applications for these functions: the implementation of a search algorithm, and an algorithm for exact real-number integration. We discuss the advantages and limitations of this style of programming relative to other approaches. We also consider the increased scope for compiler optimizations that these functions would offer.

Current (heterogeneous) multi-core environments such as gpGPU architectures are hard to program with normal imperative and object-oriented (OO) languages. There are two basic problems to tackle: (1) it is too easy to program race conditions and dead-locks with the available synchronization primitives, and (2) these environments do not support (or support inefficiently) the instructions required for efficient execution of OO programs, e.g., because function pointers and pointer arithmetic are lacking. We address both problems with a new language that comprises both Functional Programming (FP) and OO programming. We solve problem (1) by auto-parallelization in the functional core where all loops and non-dependent calls can be executed in parallel. FP is to be used to write computational intensive code with safe concurrent memory access. An alternative object model that does neither use pointer arithmetic nor function pointers but smart pointers/proxies (to implement polymorphism) as well as mixins and templates (to implement OO like code reuse) solves problem (2). To cleanly integrate the two language cores, we propose a new integration model that even grants some restricted ways to access state from within FP mode. With the new language and prototype compiler we can transparently parallelize code to target both Cuda and multi-core machines (without annotations from the programmer) and obtain good speedups.

Nowadays, most users own multicore computers, but it is not simple to take advantage of them to speedup the execution of programs. In particular, it is not easy to provide a parallel implementation of a concrete genetic algorithm. In this paper we introduce a parallel skeleton that given a sequential implementation automatically provides a corresponding parallel implementation of it. In order to do it, we use a parallel functional language where skeletons can be defined as higher-order functions. Thus, the parallelizing machinery is defined only once, and it is reused for any concrete application of the skeleton to a concrete problem.KeywordsGenetic AlgorithmFunctional ProgrammingFunctional LanguageProcess AbstractionCoordination LanguageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The higher-order logic found in proof assistants such as Coq and various HOL systems provides a convenient setting for the development and verification of pure functional programs. However, to efficiently run these programs, they must be converted (or "extracted") to functional programs in a programming language such as ML or Haskell. With current techniques, this step, which must be trusted, relates similar looking objects that have very different semantic definitions, such as the set-theoretic model of a logic and the operational semantics of a programming language. In this paper, we show how to increase the trustworthiness of this step with an automated technique. Given a functional program expressed in higher-order logic, our technique provides the corresponding program for a functional language defined with an operational semantics, and it provides a mechanically checked theorem relating the two. This theorem can then be used to transfer verified properties of the logical function to the program. We have implemented our technique in the HOL4 theorem prover, translating functions to a core subset of Standard ML, and have applied it to examples including functional data structures, a parser generator, cryptographic algorithms, and a garbage collector.

Region Inference is a technique for implementing programming languages that are based on typed call-by-value lambda calculus, such as Standard ML. The mathematical runtime model of region inference uses a stack of regions, each of which can contain an unbounded number of values. This paper is concerned with mapping the mathematical model onto real machines. This is done by composing region inference with Region Representation Inference, which gradually refines region information till it is directly implementable on conventional von Neumann machines. The performance of a new region-based ML compiler is compared to the performance of Standard ML of New Jersey, a state-of-the-art ML compiler.

Standard ML is a strictly typed functional programming language that provides programmers with many powerful features, including parametric polymorphism, pattern matching, and a rich module system. In this paper, we present SMLtoJs, an optimizing compiler that compiles Standard ML programs into JavaScript to be executed in any JavaScript-supported Web browser. SMLtoJs supports all of Standard ML and most of the Standard ML Basis Library, which allows for the SMLtoJs compiler itself to be compiled and executed in a browser. We present the overall structure of SMLtoJs, including the non-JavaScript aware front-end and the JavaScript-specific backend. We also present SMLtoJs's type safe interface for interacting with native JavaScript, such as the DOM api. Finally, we present the details of the JavaScript-specific optimizations, including how SMLtoJs deals with tail recursion, which enable complex Standard ML programs to be compiled into efficient JavaScript.

Finite state automata are of fundamental importance in theory as well as applications in computer science. This dissertation presents several parallel algorithms for processing of them, including minimizing the number of states for a given finite state automaton, finding homing sequences for a given finite state automaton, converting a regular expression into an $\\varepsilon$-free nondeterministic finite automaton. Implementation of some algorithms, as well as their experimental results, are also given. For minimization of deterministic finite automata, we present a parallel algorithm and discuss its implementation on a practical parallel machine using data parallel and message passing paradigms. Its time complexity on a p processor EREW PRAM $(p\\le n)$ for inputs of size n is $O({n\\log\\sp2n\\over p}+\\log n\\ \\log p)$ uniformly on almost all instances. The work done by our algorithm is thus within a factor of $O(\\log n)$ of the best known sequential algorithm. The space used by our algorithm is linear in the input size. The actual resource requirements of our implementations are consistent with these estimates. For finding homing sequences of deterministic finite automata, we present both deterministic and randomized algorithms. Two randomized algorithms of time complexity $O(log\\sp2n)$ are given. One of the randomized algorithms produces a homing sequence of length $O(n\\ log\\sp2n)$ for almost all DFA's with n states. The deterministic algorithm is based on a well known sequential algorithm. The implementation of the deterministic parallel algorithm shows that it can handle randomly generated instances with millions of states, and all DFA's with thousands of states using currently available parallel machines. Finally, we present an efficient parallel algorithm for converting a regular expression into an $\\varepsilon$-free nondeterministic finite automaton. The resulting automaton is natural representation of a regular expression and has some important and practical uses in the document processing area. The time complexity of the algorithm is $O(\\log n)$ and the number of processors used is $O(n\\sp2).$

The Trends in Functional Programming Symposia are an annual event dedicated to promoting new research directions in functional programming and to investigating the relationship between functional programming and other branches of Computer Science. The TFP series started in 1999 and the 2008 meeting marked the first decade of the symposium. In this paper we identify and discuss the trends presented at TFP using a macro-vision lens that looks at topics in the first two volumes of TFP and the two most recent volumes of TFP. The goal is to calibrate how trends have changed over a decade instead of analyzing trends with a micro-vision lens that focuses on changes from year to year. The major trends identified are work on parallel functional programming, types, formal verification, implementation of functional languages, and application development.

Monads, Effects and Transformations

We present a novel counterexample generator for the interactive theorem prover Isabelle based on a compiler that synthesizes test data generators for functional programming languages (e.g. Standard ML, OCaml) from specifications in Isabelle. In contrast to naive type-based test data generators, the smart generators take the preconditions into account and only generate tests that fulfill the preconditions. The smart generators are constructed by a compiler that reformulates the preconditions as logic programs and analyzes them by an enriched mode inference. From this inference, the compiler can construct the desired generators in the functional programming language. These test data generators are applied to find errors in specifications, as we show in a case study of a hotel key card system.