Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Shared-memory parallelism

Weak memory models

Participants : Luc Maranget, Jade Alglave [University College London–Microsoft Research, UK] , Patrick Cousot [New York University] , Andrea Parri [Sant'Anna School of Advanced Studies, Pisa, Italy] .

Modern multi-core and multi-processor computers do not follow the intuitive “Sequential Consistency” model that would define a concurrent execution as the interleaving of the executions of its constituent threads and that would command instantaneous writes to the shared memory. This situation is due both to in-core optimisations such as speculative and out-of-order execution of instructions, and to the presence of sophisticated (and cooperating) caching devices between processors and memory. Luc Maranget took part in an international research effort to define the semantics of the computers of the multi-core era, and more generally of shared-memory parallel devices or languages, with a clear focus on devices.

More precisely, in 2016, Luc Maranget pursued his collaboration with Jade Alglave and Patrick Cousot to extend “Cats”, a domain-specific language for defining and executing weak memory models. Last year, a long article that presents a precise semantics for “Cats” and a study and formalisation of the HSA memory model was submitted. (The Heterogeneous System Architecture foundation is an industry standards body targeting heterogeneous computing devices.) As this article was rejected, a new paper, focused on the “Cats” semantics, was submitted this year, while the definition of the HSA memory model was made available on the web site of the HSA foundation (

This year, our team hosted Andrea Parri, a Ph.D. student (supervised by Mauro Marinoni at Sant'Anna School of Advanced Studies, Pisa, Italy), for six months. Luc Maranget and Andrea Parri collaborated with Paul McKenney (IBM), Alan Stern (Harvard University) and Jade Alglave on the definition of a memory model for the Linux kernel. A preliminary version of this work was presented by Paul McKenney at the 2016 Linux Conference Europe. While invited at the Dagstuhl seminar “Concurrency with Weak Memory Models...”, Luc Maranget demonstrated the Diy toolsuite and the “Cats” language. It is worth noting that Cats models are being used independently of us by other researchers, most notably by Yatin Manerkar and Caroline J. Trippel (Princeton University) who discovered an anomaly in the published compilation scheme of the C11 language down to the Power architecture.

Luc Maranget also co-authored a paper that will be presented at POPL 2017 [23]. This work describes memory-model-aware “mixed-size” semantics for the ARMv8 architecture and for the C11 and Sequential Consistency models. A mixed-size semantics accounts for the behaviour of systems that access memory at different granularity levels (bytes, words, etc.) This is joint work with many researchers, including Shaked Flur and other members of Peter Sewell's team (University of Cambridge) as well as Mark Batty (University of Kent).

Algorithms and data structures for parallel computing

Participants : Umut Acar, Vitalii Aksenov, Arthur Charguéraud, Adrien Guatto, Michael Rainey, Filip Sieczkowski.

The ERC Deepsea project, with principal investigator Umut Acar, started in June 2013 and is hosted by the Gallium team. This project aims at developing techniques for parallel and self-adjusting computation in the context of shared-memory multiprocessors (i.e., multicore platforms). The project is continuing work that began at Max Planck Institute for Software Systems between 2010 and 2013. As part of this project, we are developing a C++ library, called PASL, for programming parallel computations at a high level of abstraction. We use this library to evaluate new algorithms and data structures. We obtained four main results this year.

Our first result is a calculus for parallel computing on hardware shared-memory computers such as modern multicores. Many languages for writing parallel programs have been developed. These languages offer several distinct abstractions for parallelism, such as fork-join, async-finish, futures, etc. While they may seem similar, these abstractions lead to different semantics, language design and implementation decisions. In this project, we consider the question of whether it would be possible to unify these approaches to parallelism. To this end, we propose a calculus, called the DAG-calculus, which can encode existing approaches to parallelism based on fork-join, async-finish, and futures, and possibly others. We have shown that the approach is realistic by presenting an implementation in C++ and by performing an empirical evaluation. This work was presented at ICFP 2016 [18].

Our second result is a concurrent data structure that may be used to efficiently determine when a concurrently-updated counter reaches the value zero. Our data structure extends an existing data structure called SNZI [44]. While the latter imposes a fixed number of threads, our structure is able to dynamically grow in response to the increasing degree of concurrency in the system. We use our dynamic non-zero indicator data structure to derive an efficient runtime representation of async/finish programs. The async/finish paradigm for expressing parallelism is one that, in the past decade, has become a part of many research-language implementations (e.g. X10) and is now gaining traction in a number of mainstream languages, most notably Java. The implementation of async/finish is challenging because the finish-block mechanism permits, and even encourages, computations in which a large number of threads are required to synchronize on shared barriers, and this number is not statically known. We present an implementation of async/finish and prove that, in a model that takes contention into account, the cost of synchronization of the async-ed threads is amortized constant time, regardless of the number of threads. We also present experimental evaluation suggesting that the approach performs well in practice. This work has been accepted for publication at PPoPP [17].

Our third result is an extended, polished presentation of our prior work on granularity control for parallel algorithms using user-provided complexity functions. Granularity control denotes the problem of controlling the size of parallel threads created in implicitly parallel programs. If small threads are executed in parallel, the overheads due to thread creation can overwhelm the benefits of parallelism. If large threads are executed sequentially, processors may spin idle. In our work, we show that, if we have an oracle able to approximately predict the execution time of every sub-task, then there exists a strategy that delivers provably good performance. Moreover, we present empirical results showing that, for simple recursive divide-and-conquer programs, we are able to implement such an oracle simply by requiring the user to annotate functions with their asymptotic complexity. The idea is to estimate the constant factors that apply by conducting measures at runtime. This work is described in depth in an article published in the Journal of Functional Programming (JFP) [13].

Our fourth result is an extension of our aforementioned granularity control approach, with three major additions. First, we have developed an algorithm that ensures convergence of the estimators associated with the constant factors for all fork-join programs, and not just for a small class of programs. Second, we have built a theoretical analysis establishing bounds for the overall overheads of the convergence phase. Third, we have developed a C++ implementation accompanied with an extensive experimental study covering several benchmarks from the Problem Based Benchmark Suite (PBBS), a collection of high-quality parallel algorithms that delivers state-of-the-art performance. Even though our approach does not leverage a specific compiler and does not require any magic constant to be hard-coded in the source programs, our code either matches or exceeds the performance of the authors' original, hand-tuned codes. An article describing this work is in preparation.