skip to main content

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00 PM ET on Thursday, October 10 until 2:00 AM ET on Friday, October 11 due to maintenance. We apologize for the inconvenience.

Explore Research Products in the NSF-PAR

This content will become publicly available on January 5, 2025

Title: Disentanglement with Futures, State, and Interaction

Recent work has proposed a memory property for parallel programs, called disentanglement, and showed that it is pervasive in a variety of programs, written in different languages, ranging from C/C++ to Parallel ML, and showed that it can be exploited to improve the performance of parallel functional programs. All existing work on disentanglement, however, considers the fork/join model for parallelism and does not apply to futures, the more powerful approach to parallelism. This is not surprising: fork/join parallel programs exhibit a reasonably strict dependency structure (e.g., series-parallel DAGs), which disentanglement exploits. In contrast, with futures, parallel computations become first-class values of the language, and thus can be created, and passed between functions calls or stored in memory, just like other ordinary values, resulting in complex dependency structures, especially in the presence of mutable state. For example, parallel programs with futures can have deadlocks, which is impossible with fork-join parallelism.

In this paper, we are interested in the theoretical question of whether disentanglement may be extended beyond fork/join parallelism, and specifically to futures. We consider a functional language with futures, Input/Output (I/O), and mutable state (references) and show that a broad range of programs written in this language are disentangled. We start by formalizing disentanglement for futures and proving that purely functional programs written in this language are disentangled. We then generalize this result in three directions. First, we consider state (effects) and prove that stateful programs are disentangled if they are race free. Second, we show that race freedom is sufficient but not a necessary condition and non-deterministic programs, e.g. those that use atomic read-modify-operations and some non-deterministic combinators, may also be disentangled. Third, we prove that disentangled task-parallel programs written with futures are free of deadlocks, which arise due to interactions between state and the rich dependencies that can be expressed with futures. Taken together, these results show that disentanglement generalizes to parallel programs with futures and, thus, the benefits of disentanglement may go well beyond fork-join parallelism.

  more » « less
Award ID(s):
2115104 2107241 2119069 2028921 2119352
NSF-PAR ID:
10515497
Author(s) / Creator(s):
; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Proceedings of the ACM on Programming Languages
Volume:
8
Issue:
POPL
ISSN:
2475-1421
Page Range / eLocation ID:
1569 to 1599
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ( , European Conference on Object-Oriented Programming)
    Ali, Karim ; Salvaneschi, Guido (Ed.)
    Much of the past work on dynamic data-race and determinacy-race detection algorithms for task parallelism has focused on structured parallelism with fork-join constructs and, more recently, with future constructs. This paper addresses the problem of dynamic detection of data-races and determinacy-races in task-parallel programs with promises, which are more general than fork-join constructs and futures. The motivation for our work is twofold. First, promises have now become a mainstream synchronization construct, with their inclusion in multiple languages, including C++, JavaScript, and Java. Second, past work on dynamic data-race and determinacy-race detection for task-parallel programs does not apply to programs with promises, thereby identifying a vital need for this work. This paper makes multiple contributions. First, we introduce a featherweight programming language that captures the semantics of task-parallel programs with promises and provides a basis for formally defining determinacy using our semantics. This definition subsumes functional determinacy (same output for same input) and structural determinacy (same computation graph for same input). The main theoretical result shows that the absence of data races is sufficient to guarantee determinacy with both properties. We are unaware of any prior work that established this result for task-parallel programs with promises. Next, we introduce a new Dynamic Race Detector for Promises that we call DRDP. DRDP is the first known race detection algorithm that executes a task-parallel program sequentially without requiring the serial-projection property; this is a critical requirement since programs with promises do not satisfy the serial-projection property in general. Finally, the paper includes experimental results obtained from an implementation of DRDP. The results show that, with some important optimizations introduced in our work, the space and time overheads of DRDP are comparable to those of more restrictive race detection algorithms from past work. To the best of our knowledge, DRDP is the first determinacy race detector for task-parallel programs with promises. 
    more » « less
  2. ( , ACM)
    Deadlocks, in which threads wait on each other in a cyclic fashion and can't make progress, have plagued parallel programs for decades. In recent years, as the parallel programming mechanism known as futures has gained popularity, interest in preventing deadlocks in programs with futures has increased as well. Various static and dynamic algorithms exist to detect and prevent deadlock in programs with futures, generally by constructing some approximation of the dependency graph of the program but, as far as we are aware, all are specialized to a particular programming language. A recent paper introduced graph types, by which one can statically approximate the dependency graphs of a program in a language-independent fashion. By analyzing the graph type directly instead of the source code, a graph-based program analysis, such as one to detect deadlock, can be made language-independent. Indeed, the paper that proposed graph types also proposed a deadlock detection algorithm. Unfortunately, the algorithm was based on an unproven conjecture which we show to be false. In this paper, we present, and prove sound, a type system for finding possible deadlocks in programs that operates over graph types and can therefore be applied to many different languages. As a proof of concept, we have implemented the algorithm over a subset of the OCaml language extended with built-in futures. 
    more » « less
  3. ; ; ( , Proceedings of the ACM on Programming Languages)
    Recent research on parallel functional programming has culminated in a provably efficient (in work and space) parallel memory manager, which has been incorporated into the MPL (MaPLe) compiler for Parallel ML and shown to deliver practical efficiency and scalability. The memory manager exploits a property of parallel programs called disentanglement, which restricts computations from accessing concurrently allocated objects. Disentanglement is closely related to race-freedom, but subtly differs from it. Unlike race-freedom, however, no known techniques exists for ensuring disentanglement, leaving the task entirely to the programmer. This is a challenging task, because it requires reasoning about low-level memory operations (e.g., allocations and accesses), which is especially difficult in functional languages. In this paper, we present techniques for detecting entanglement dynamically, while the program is running. We first present a dynamic semantics for a functional language with references that checks for entanglement by consulting parallel and sequential dependency relations in the program. Notably, the semantics requires checks for mutable objects only. We prove the soundness of the dynamic semantics and present several techniques for realizing it efficiently, in particular by pruning away a large number of entanglement checks. We also provide bounds on the work and space of our techniques. We show that the entanglement detection techniques are practical by implementing them in the MPL compiler for Parallel ML. Considering a variety of benchmarks, we present an evaluation and measure time and space overheads of less than 5% on average with up to 72 cores. These results show that entanglement detection has negligible cost and can therefore remain deployed with little or no impact on efficiency, scalability, and space. 
    more » « less
  4. ; ; ; ( , Proceedings of the 28th ACM Symposium on Parallelism in Algorithms and Architectures)
    If a parallel program has determinacy race(s), different schedules can result in memory accesses that observe different values --- various race-detection tools have been designed to find such bugs. A key component of race detectors is an algorithm for series-parallel (SP) maintenance, which identifies whether two accesses are logically parallel. This paper describes an asymptotically optimal algorithm, called WSP-Order, for performing SP maintenance in programs with fork-join (or nested) parallelism. Given a fork-join program with T1 work and T∞ span, WSP-Order executes it while also maintaining SP relationships in O(T1/P + T∞) time on P processors, which is asymptotically optimal. At the heart of WSP-Order is a work-stealing scheduler designed specifically for SP maintenance. We also implemented C-RACER, a race-detector based on WSP-Order within the Cilk Plus runtime system, and evaluated its performance on five benchmarks. Empirical results demonstrate that when run sequentially, it performs almost as well as previous best sequential race detectors. More importantly, when run in parallel, it achieves almost as much speedup as the original program without race-detection. 
    more » « less
  5. ; ; ; ; ; ; ; ( , ASPLOS '24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems)
    Modern programming languages offer special syntax and semantics for logical fork-join parallelism in the form of parallel loops, allowing them to be nested, e.g., a parallel loop within another parallel loop. This expressiveness comes at a price, however: on modern multicore systems, realizing logical parallelism results in overheads due to the creation and management of parallel tasks, which can wipe out the benefits of parallelism. Today, we expect application programmers to cope with it by manually tuning and optimizing their code. Such tuning requires programmers to reason about architectural factors hidden behind layers of software abstractions, such as task scheduling and load balancing. Managing these factors is particularly challenging when workloads are irregular because their performance is input-sensitive. This paper presents HBC, the first compiler that translates C/C++ programs with high-level, fork-join constructs (e.g., OpenMP) to binaries capable of automatically controlling the cost of parallelism and dealing with irregular, input-sensitive workloads. The basis of our approach is Heartbeat Scheduling, a recent proposal for automatic granularity control, which is backed by formal guarantees on performance. HBC binaries outperform OpenMP binaries for workloads for which even entirely manual solutions struggle to find the right balance between parallelism and its costs. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email