3,878 research outputs found
Causal Consistency: Beyond Memory
In distributed systems where strong consistency is costly when not
impossible, causal consistency provides a valuable abstraction to represent
program executions as partial orders. In addition to the sequential program
order of each computing entity, causal order also contains the semantic links
between the events that affect the shared objects -- messages emission and
reception in a communication channel , reads and writes on a shared register.
Usual approaches based on semantic links are very difficult to adapt to other
data types such as queues or counters because they require a specific analysis
of causal dependencies for each data type. This paper presents a new approach
to define causal consistency for any abstract data type based on sequential
specifications. It explores, formalizes and studies the differences between
three variations of causal consistency and highlights them in the light of
PRAM, eventual consistency and sequential consistency: weak causal consistency,
that captures the notion of causality preservation when focusing on convergence
; causal convergence that mixes weak causal consistency and convergence; and
causal consistency, that coincides with causal memory when applied to shared
memory.Comment: 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel
Programming, Mar 2016, Barcelone, Spai
Non-blocking Priority Queue based on Skiplists with Relaxed Semantics
Priority queues are data structures that store information in an orderly fashion. They are of tremendous importance because they are an integral part of many applications, like Dijkstra’s shortest path algorithm, MST algorithms, priority schedulers, and so on.
Since priority queues by nature have high contention on the delete_min operation, the design of an efficient priority queue should involve an intelligent choice of the data structure as well as relaxation bounds on the data structure. Lock-free data structures provide higher scalability as well as progress guarantee than a lock-based data structure. That is another factor to be considered in the priority queue design.
We present a relaxed non-blocking priority queue based on skiplists. We address all the design issues mentioned above in our priority queue. Use of skiplists allows multiple threads to concurrently access different parts of the skiplist quickly, whereas relaxing the priority queue delete_min operation distributes contention over the skiplist instead of just at the front. Furthermore, a non-blocking implementation guarantees that the system will make progress even when some process fails.
Our priority queue is internally composed of several priority queues, one for each thread and one shared priority queue common to all threads. Each thread selects the best value from its local priority queue and the shared priority queue and returns the value. In case a thread is unable to delete an item, it tries to spy items from other threads\u27 local priority queues.
We experimentally and theoretically show the correctness of our data structure. We also compare the performance of our data structure with other variations like priority queues based on coarse-grained skiplists for both relaxed and non-relaxed semantics
Concurrent Data Structures Linked in Time
Arguments about correctness of a concurrent data structure are typically
carried out by using the notion of linearizability and specifying the
linearization points of the data structure's procedures. Such arguments are
often cumbersome as the linearization points' position in time can be dynamic
(depend on the interference, run-time values and events from the past, or even
future), non-local (appear in procedures other than the one considered), and
whose position in the execution trace may only be determined after the
considered procedure has already terminated.
In this paper we propose a new method, based on a separation-style logic, for
reasoning about concurrent objects with such linearization points. We embrace
the dynamic nature of linearization points, and encode it as part of the data
structure's auxiliary state, so that it can be dynamically modified in place by
auxiliary code, as needed when some appropriate run-time event occurs. We name
the idea linking-in-time, because it reduces temporal reasoning to spatial
reasoning. For example, modifying a temporal position of a linearization point
can be modeled similarly to a pointer update in separation logic. Furthermore,
the auxiliary state provides a convenient way to concisely express the
properties essential for reasoning about clients of such concurrent objects. We
illustrate the method by verifying (mechanically in Coq) an intricate optimal
snapshot algorithm due to Jayanti, as well as some clients
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