41,694 research outputs found
Lock-free Concurrent Data Structures
Concurrent data structures are the data sharing side of parallel programming.
Data structures give the means to the program to store data, but also provide
operations to the program to access and manipulate these data. These operations
are implemented through algorithms that have to be efficient. In the sequential
setting, data structures are crucially important for the performance of the
respective computation. In the parallel programming setting, their importance
becomes more crucial because of the increased use of data and resource sharing
for utilizing parallelism.
The first and main goal of this chapter is to provide a sufficient background
and intuition to help the interested reader to navigate in the complex research
area of lock-free data structures. The second goal is to offer the programmer
familiarity to the subject that will allow her to use truly concurrent methods.Comment: To appear in "Programming Multi-core and Many-core Computing
Systems", eds. S. Pllana and F. Xhafa, Wiley Series on Parallel and
Distributed Computin
Analyzing the Performance of Lock-Free Data Structures: A Conflict-based Model
This paper considers the modeling and the analysis of the performance of
lock-free concurrent data structures. Lock-free designs employ an optimistic
conflict control mechanism, allowing several processes to access the shared
data object at the same time. They guarantee that at least one concurrent
operation finishes in a finite number of its own steps regardless of the state
of the operations. Our analysis considers such lock-free data structures that
can be represented as linear combinations of fixed size retry loops. Our main
contribution is a new way of modeling and analyzing a general class of
lock-free algorithms, achieving predictions of throughput that are close to
what we observe in practice. We emphasize two kinds of conflicts that shape the
performance: (i) hardware conflicts, due to concurrent calls to atomic
primitives; (ii) logical conflicts, caused by simultaneous operations on the
shared data structure. We show how to deal with these hardware and logical
conflicts separately, and how to combine them, so as to calculate the
throughput of lock-free algorithms. We propose also a common framework that
enables a fair comparison between lock-free implementations by covering the
whole contention domain, together with a better understanding of the
performance impacting factors. This part of our analysis comes with a method
for calculating a good back-off strategy to finely tune the performance of a
lock-free algorithm. Our experimental results, based on a set of widely used
concurrent data structures and on abstract lock-free designs, show that our
analysis follows closely the actual code behavior.Comment: Short version to appear in DISC'1
Techniques for Constructing Efficient Lock-free Data Structures
Building a library of concurrent data structures is an essential way to
simplify the difficult task of developing concurrent software. Lock-free data
structures, in which processes can help one another to complete operations,
offer the following progress guarantee: If processes take infinitely many
steps, then infinitely many operations are performed. Handcrafted lock-free
data structures can be very efficient, but are notoriously difficult to
implement. We introduce numerous tools that support the development of
efficient lock-free data structures, and especially trees.Comment: PhD thesis, Univ Toronto (2017
Adaptive Lock-Free Data Structures in Haskell: A General Method for Concurrent Implementation Swapping
A key part of implementing high-level languages is providing built-in and
default data structures. Yet selecting good defaults is hard. A mutable data
structure's workload is not known in advance, and it may shift over its
lifetime - e.g., between read-heavy and write-heavy, or from heavy contention
by multiple threads to single-threaded or low-frequency use. One idea is to
switch implementations adaptively, but it is nontrivial to switch the
implementation of a concurrent data structure at runtime. Performing the
transition requires a concurrent snapshot of data structure contents, which
normally demands special engineering in the data structure's design. However,
in this paper we identify and formalize an relevant property of lock-free
algorithms. Namely, lock-freedom is sufficient to guarantee that freezing
memory locations in an arbitrary order will result in a valid snapshot. Several
functional languages have data structures that freeze and thaw, transitioning
between mutable and immutable, such as Haskell vectors and Clojure transients,
but these enable only single-threaded writers. We generalize this approach to
augment an arbitrary lock-free data structure with the ability to gradually
freeze and optionally transition to a new representation. This augmentation
doesn't require changing the algorithm or code for the data structure, only
replacing its datatype for mutable references with a freezable variant. In this
paper, we present an algorithm for lifting plain to adaptive data and prove
that the resulting hybrid data structure is itself lock-free, linearizable, and
simulates the original. We also perform an empirical case study in the context
of heating up and cooling down concurrent maps.Comment: To be published in ACM SIGPLAN Haskell Symposium 201
Finding linearization violations in lock-free concurrent data structures
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (page 31).Finding bugs in lock-free concurrent programs is hard. This is due in part to the difficulty of reasoning about the correctness of concurrent algorithms and the timing-sensitive nature of concurrent programs. One of the most widely used tools for reasoning about the correctness of concurrent algorithms is the linearization property. This thesis presents a tool for automatic dynamic checking of concurrent programs under the Total-Store-Order (TSO) memory model and a methodology for finding linearization violations automatically with the tool.by Sebastien Alberto Dabdoub.M. Eng
A method for implementing lock-free shared data structures
We are interested in implementing data structures on shared memory multiprocessors. A natural model for these machines is an asynchronous parallel machine, in which the processors are subject to arbitrary delays. On such machines, it is desirable for algorithms to be {\em lock-free}, that is, they must allow concurrent access to data without using mutual exclusion. Efficient lock-free implementations are known for some specific data structures, but these algorithms do not generalize well to other structures. For most data structures, the only previously known lock-free algorithm is due to Herlihy. Herlihy presents a simple methodology to create a lock-free implementation of a general data structure, but his approach can be very expensive. We present a technique that provides the semantics of exclusive access to data without using mutual exclusion. Using this technique, we devise the {\em caching method}, a general method of implementing lock-free data structures that is provably better than Herlihy's methodology for many well-known data structures. The cost of one operation using the caching method is proportional to , where is the sequential cost of the operation. Under Herlihy's methodology, the cost is proportional to , where is the time needed to make a logical copy of the data structure. For many data structures, such as arrays and {\em well connected} pointer-based structures (e.g., a doubly linked list), the best known value for is proportional to the size of the structure, making the copying time much larger than the sequential cost of an operation. The new method can also allow {\em concurrent updates} to the data structure; Herlihy's methodology cannot. A correct lock-free implementation can be derived from a correct sequential implementation in a straightforward manner using this method. The method is also flexible; a programmer can change many of the details of the default implementation to optimize for a particular pattern of data structure use
EEMARQ: Efficient Lock-Free Range Queries with Memory Reclamation
Multi-Version Concurrency Control (MVCC) is a common mechanism for achieving linearizable range queries in database systems and concurrent data-structures. The core idea is to keep previous versions of nodes to serve range queries, while still providing atomic reads and updates. Existing concurrent data-structure implementations, that support linearizable range queries, are either slow, use locks, or rely on blocking reclamation schemes. We present EEMARQ, the first scheme that uses MVCC with lock-free memory reclamation to obtain a fully lock-free data-structure supporting linearizable inserts, deletes, contains, and range queries. Evaluation shows that EEMARQ outperforms existing solutions across most workloads, with lower space overhead and while providing full lock freedom
Throughput and energy efficiency of lock-free data structures: Execution Models and Analyses
Concurrent data structures are key program components to harness the available parallelism in multi-core processors. Lock-free algorithmic implementations of concurrent data structures offer high scalability and possess desirable properties such as immunity to deadlocks, convoying and priority inversion. In this thesis, we develop analytical tools to model and analyze the throughput and energy consumption of concurrent lock-free data structures. We start our study with a general class of lock-free data structures. Then, we target more specialized designs for lock-free queues. Finally, we focus on the search data structures that possess different characteristics compared to previously mentioned data structures. Performance of lock-free data structures: This thesis contributes to the problem of making ends meet between theoretical bounds and actual measured throughput. As the first step, we consider a general class of lock-free data structures and propose three analytical frameworks with different flavors. Analyses of this class also cover efficient implementations of a set of fundamental data structures that suffer from inherent sequential bottlenecks. We model the executions and examine the impact of contention on the throughput of these algorithms. Our analyses lead to optimization methods on memory management and back-off strategies. Performance and energy efficiency of lock-free queues: We take a step further to model the throughput of lock-free operations and their interaction. Considering shared queues, as a key paradigm for data sharing, operations (En- queue, Dequeue) access the opposite ends of a queue. Same type of operations might contend with each other on a non-empty queue. However, all types of operations are subject to interaction when the queue is empty. We first decorrelate the throughput of dequeuers’ and enqueuers’ into several uncorrelated basic throughputs, and reconstruct the main throughputs as a function of these basic throughputs. Besides, we model the power dissipation and integrate it with the throughput estimations to extract the energy consumption of applications that utilize lock-free queues. Performance of lock-free search data structures: Lock-free designs that utilize fine-grained synchronization have produced efficient implementations of search data structures. These designs reveal different characteristics compared to the previous set of lock-free data structures with inherent sequential bottlenecks. We introduce a new way of modeling and analyzing the throughput of search data structures under stationary and memoryless access patterns.
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