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 highperformance compiler for the Standard ML language. Finally, we evaluate the performance of this implementation on a number of parallel benchmarks.

Competitive and cooperative threading are widely used abstractions in computing. In competitive threading, threads are scheduled preemptively with the goal of minimizing response time, usually of interactive applications. In cooperative threading, threads are scheduled non-preemptively with the goal of maximizing throughput or minimizing the completion time, usually in compute-intensive applications, e.g. scientific computing, machine learning and AI. Although both of these forms of threading rely on the same abstraction of a thread, they have, to date, remained largely separate forms of computing. Motivated by the recent increase in the mainstream use of multicore computers, we propose a threading model that aims to unify competitive and cooperative threading. To this end, we extend the classic graph-based cost model for cooperative threading to allow for competitive threading, and describe how such a cost model may be used in a programming language by presenting a language and a corresponding cost semantics. Finally, we show that the cost model and the semantics are realizable by presenting an operational semantics for the language that specifies the behavior of an implementation, as well as an implementation and a small empirical evaluation. CCS Concepts • Software and its engineering → Parallel programming languages; Concurrent programming languages; Functional languages

Multi-threaded programs have traditionally fallen into one of two domains: cooperative and competitive. These two domains have traditionally remained mostly disjoint, with cooperative threading used for increasing throughput in compute-intensive applications such as scientific workloads and cooperative threading used for increasing responsiveness in interactive applications such as GUIs and games. As multicore hardware becomes increasingly mainstream, there is a need for bridging these two disjoint worlds, because many applications mix interaction and computation and would benefit from both cooperative and competitive threading.In this paper, we present techniques for programming and reasoning about parallel interactive applications that can use both cooperative and competitive threading. Our techniques enable the programmer to write rich parallel interactive programs by creating and synchronizing with threads as needed, and by assigning threads user-defined and partially ordered priorities. To ensure important responsiveness properties, we present a modal type system analogous to S4 modal logic that precludes low-priority threads from delaying high-priority threads, thereby statically preventing a crucial set of priority-inversion bugs. We then present a cost model that allows reasoning about responsiveness and completion time of well-typed programs. The cost model extends the traditional work-span model for cooperative threading to account for competitive scheduling decisions needed to ensure responsiveness. Finally, we show that our proposed techniques are realistic by implementing them as an extension to the Standard ML language.

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 highperformance compiler for the Standard ML language. Finally, we evaluate the performance of this implementation on a number of parallel benchmarks.

We demonstrate that a practical concurrent language can be extended in a natural way with information security mechanisms that provably enforce strong information security guarantees. We extend the X10 concurrent programming language with coarse-grained information-flow control. Central to X10 concurrency abstractions is the notion of a place: a container for data and computation. We associate a security level with each place, and restrict each place to store only data appropriate for that security level. When places interact only with other places at the same security level, then our security mechanisms impose no restrictions. When places of differing security levels interact, our information security analysis prevents potentially dangerous information flows, including information flow through covert scheduling channels. The X10 concurrency mechanisms simplify reasoning about information flow in concurrent programs.We present a static analysis that enforces a noninterference-based extensional information security condition in a calculus that captures the key aspects of X10's place abstraction and async-finish parallelism. We extend this security analysis to support many of X10's language features, and have implemented a prototype compiler for the resulting language.

Research on parallel computing has historically revolved around compute-intensive applications drawn from traditional areas such as high-performance computing. With the growing availability and usage of multicore chips, applications of parallel computing now include interactive parallel applications that mix compute-intensive tasks with interaction, e.g., with the user or more generally with the external world. Recent theoretical work on responsive parallelism presents abstract cost models and type systems for ensuring and reasoning about responsiveness and throughput of such interactive parallel programs.In this paper, we extend prior work by considering a crucial metric: fairness. To express rich interactive parallel programs, we allow programmers to assign priorities to threads and instruct the scheduler to obey a notion of fairness. We then propose the fairly prompt scheduling principle for executing such programs; the principle specifies the schedule for multithreaded programs on multiple processors. For such schedules, we prove theoretical bounds on the execution and response times of jobs of various priorities. In particular, we bound the amount, i.e., stretch, by which a low-priority job can be delayed by higher-priority work. We also present an algorithm designed to approximate the fairly prompt scheduling principle on multicore computers, implement the algorithm by extending the Standard ML language, and present an empirical evaluation.

Competitive and cooperative threading are widely used abstractions in computing. In competitive threading, threads are scheduled preemptively with the goal of minimizing response time, usually of interactive applications. In cooperative threading, threads are scheduled non-preemptively with the goal of maximizing throughput or minimizing the completion time, usually in compute-intensive applications, e.g. scientific computing, machine learning and AI. Although both of these forms of threading rely on the same abstraction of a thread, they have, to date, remained largely separate forms of computing. Motivated by the recent increase in the mainstream use of multicore computers, we propose a threading model that aims to unify competitive and cooperative threading. To this end, we extend the classic graph-based cost model for cooperative threading to allow for competitive threading, and describe how such a cost model may be used in a programming language by presenting a language and a corresponding cost semantics. Finally, we show that the cost model and the semantics are realizable by presenting an operational semantics for the language that specifies the behavior of an implementation, as well as an implementation and a small empirical evaluation. CCS Concepts • Software and its engineering → Parallel programming languages; Concurrent programming languages; Functional languages

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