Brief Announcement: Parallel Dynamic Tree Contraction via Self-Adjusting Computation

International audienceDynamic algorithms are used to compute a property of some data while the data undergoes changes over time. Many dynamic algorithms have been proposed but nearly all are sequential. In this paper, we present our ongoing work on designing a parallel algorithm for the dynamic trees problem, which requires computing a property of a forest as the forest undergoes changes. Our algorithm allows insertion and/or deletion of both vertices and edges anywhere in the input and performs updates in parallel. We obtain our algorithm by applying a dynamization technique called self-adjusting computation to the classic algorithm of Miller and Reif for tree contraction

Parallel Batch-Dynamic Trees via Change Propagation

The dynamic trees problem is to maintain a forest subject to edge insertions and deletions while facilitating queries such as connectivity, path weights, and subtree weights. Dynamic trees are a fundamental building block of a large number of graph algorithms. Although traditionally studied in the single-update setting, dynamic algorithms capable of supporting batches of updates are increasingly relevant today due to the emergence of rapidly evolving dynamic datasets. Since processing updates on a single processor is often unrealistic for large batches of updates, designing parallel batch-dynamic algorithms that achieve provably low span is important for many applications. In this work, we design the first work-efficient parallel batch-dynamic algorithm for dynamic trees that is capable of supporting both path queries and subtree queries, as well as a variety of nonlocal queries. Previous work-efficient dynamic trees of Tseng et al. were only capable of handling subtree queries [ALENEX\u2719, (2019), pp. 92 - 106]. To achieve this, we propose a framework for algorithmically dynamizing static round-synchronous algorithms to obtain parallel batch-dynamic algorithms. In our framework, the algorithm designer can apply the technique to any suitably defined static algorithm. We then obtain theoretical guarantees for algorithms in our framework by defining the notion of a computation distance between two executions of the underlying algorithm. Our dynamic trees algorithm is obtained by applying our dynamization framework to the parallel tree contraction algorithm of Miller and Reif [FOCS\u2785, (1985), pp. 478 - 489], and then performing a novel analysis of the computation distance of this algorithm under batch updates. We show that k updates can be performed in O(klog(1+n/k)) work in expectation, which matches the algorithm of Tseng et al. while providing support for a substantially larger number of queries and applications

DisLog: A Separation Logic for Disentanglement - Artifact

This is the artifact corresponding to the article entitled "DisLog: A Separation Logic for Disentanglement", and its associated documentation

DisLog: A Separation Logic for Disentanglement

International audienceDisentanglement is a run-time property of parallel programs that facilitates task-local reasoning about the memory footprint of parallel tasks. In particular, it ensures that a task does not access any memory locations allocated by another concurrently executing task. Disentanglement can be exploited, for example, to implement a high-performance parallel memory manager, such as in the MPL (MaPLe) compiler for Parallel ML. Prior research on disentanglement has focused on the design of optimizations, either trusting the programmer to provide a disentangled program or relying on runtime instrumentation for detecting and managing entanglement. This paper provides the first static approach to verify that a program is disentangled: it contributes DisLog, a concurrent separation logic for disentanglement. DisLog enriches concurrent separation logic with the notions necessary for reasoning about the fork-join structure of parallel programs, allowing the verification that memory accesses are effectively disentangled. A large class of programs, including race-free programs, exhibit memory access patterns that are disentangled "by construction". To reason about these patterns, the paper distills from DisLog an almost standard concurrent separation logic, called DisLog+. In this high-level logic, no specific reasoning about memory accesses is needed: functional correctness proofs entail disentanglement. The paper illustrates the use of DisLog and DisLog+ on a range of case studies, including two different implementations of parallel deduplication via concurrent hashing. All our results are mechanized in the Coq proof assistant using Iris