A concurrent object is a data object shared by concurrent processes. Linearizability is a correctness condition for concurrent objects that exploits the semantics of abstract data types. It permits a high degree of concurrency, yet it permits programmers to specify and reason about concurrent objects using known techniques from the sequential domain. Linearizability provides the illusion that each operation applied by concurrent processes takes effect instantaneously at some point between its invocation and its response, implying that the meaning of a concurrent object’s operations can be given by pre- and post-conditions. This paper defines linearizability, compares it to other correctness conditions, presents and demonstrates a method for proving the correctness of implementations, and shows how to reason about concurrent objects, given they are linearizable.

A wait-free implementation of a concurrent data object is one that guarantees that any process can complete any operation in a finite number of steps, regardless of the execution speeds of the other processes. The problem of constructing a wait-free implementation of one data object from another lies at the heart of much recent work in concurrent algorithms, concurrent data structures, and multiprocessor architectures. In the first part of this paper, we introduce a simple and general technique, based on reduction to a consensus protocol, for proving statements of the form "there is no wait-free implementation of X by Y ." We derive a hierarchy of objects such that no object at one level has a wait-free implementation in terms of objects at lower levels. In particular, we show that atomic read/write registers, which have been the focus of much recent attention, are at the bottom of the hierarchy: they cannot be used to construct wait-free implementations of many simple and familiar da...

Multilisp is a version of the Lisp dialect Scheme extended with constructs for parallel execution. Like Scheme, Multilisp is oriented toward symbolic computation. Unlike some parallel programming languages, Multilisp incorporates constructs for causing side effects and for explicitly introducing parallelism. The potential complexity of dealing with side effects in a parallel context is mitigated by the nature of the parallelism constructs and by support for abstract data types: a recommended Multilisp programming style is presented which, if followed, should lead to highly parallel, easily understandable programs. Multilisp is being implemented on the 32-processor Concert multiprocessor; however, it is ulti-mately intended for use on larger multiprocessors. The current implementation, called Concert Multilisp, is complete enough to run the Multilisp compiler itself and has been run on Concert prototypes including up to eight processors. Concert Multilisp uses novel techniques for task scheduling and garbage collection. The task scheduler helps control excessive resource utilization by means of an unfair scheduling policy; the garbage collector uses a multiprocessor algorithm based on the incremental garbage collector of Baker.

A con.curren.t object is a data structure shared by concurrent processes. Conventional techniques for implementing concurrent objects typically rely on criticaI sections: ensuring that only one process at a time can operate on the object. Nevertheless, critical sections are poorly suited for asynchronous systems: if one process is halted or delayed in a critical section, other, non-faulty processes will be unable to progress. By contrast, a concurrent object implementation is non-blocking if it always guarantees that some process will complete an operation in a finite number of steps, and it is wait-free if it guarantees that each process will complete an operation in a finite number of steps. This paper proposes a new methodology for constructing non-blocking aud wait-free implementations of concurrent objects. The object’s representation and operations are written as st,ylized sequential programs, with no explicit synchronization. Each sequential operation is automatically transformed into a non-blocking or wait-free operation usiug novel synchronization and memory management algorithms. These algorithms are presented for a multiple instruction/multiple data (MIM D) architecture in which n processes communicate by applying read, write, and comparekYswa,p operations to a shared memory. 1

In most parallel random-access machine (P-RAM) models, memory references are assumed to take unit time. In practice, and in theory, certain scan operations, also known as prefix computations, can executed in no more time than these parallel memory references. This paper outline an extensive study of the effect of including in the P-RAM models, such scan operations as unit-time primitives. The study concludes that the primitives improve the asymptotic running time of many algorithms by an O(lg n) factor, greatly simplify the description of many algorithms, and are significantly easier to implement than memory references. We therefore argue that the algorithm designer should feel free to use these operations as if they were as cheap as a memory reference. This paper describes five algorithms that clearly illustrate how the scan primitives can be used in algorithm design: a radix-sort algorithm, a quicksort algorithm, a minimumspanning -tree algorithm, a line-drawing algorithm and a mergi...

Language-supported synchronization is a source of serious performance problems in many Java programs. Even singlethreaded applications may spend up to half their time performing useless synchronization due to the thread-safe nature of the Java libraries. We solve this performance problem with a new algorithm that allows lock and unlock operations to be performed with only a few machine instructions in the most common cases. Our locks only require a partial word per object, and were implemented without increasing object size. We present measurements from our implementation in the JDK 1.1.2 for AIX, demonstrating speedups of up to a factor of 5 in micro-benchmarks and up to a factor of 1.7 in real programs. 1 Introduction Monitors [5] are a language-level construct for providing mutually exclusive access to shared data structures in a multithreaded environment. However, the overhead required by the necessary locking has generally restricted their use to relatively "heavy-weight" object...

Most multiprocessors are multiprogrammed in order to achieve acceptable response time and to increase their uti-lization. Unfortunately, inopportune preemption may significantly degrade the performance of synchronized parallel applications. To address this problem, researchers have developed two principal strategies for concurrent, atomic update of shared data structures: (1) preemption-safe locking and (2) non-blocking (lock-free) algorithms. Preemption-safe locking requires kernel support. Non-blocking algorithms generally require a universal atomic primitive such as compare-and-swap orload-linked/store-conditional, and are widely regarded as inefficient. We evaluate the performance of preemption-safe lock-based and non-blocking implementations of important data structures—queues, stacks, heaps, and counters—including non-blocking and lock-based queue algorithms of our own, in micro-benchmarks and real applications on a 12-processor SGI Challenge multiprocessor. Our results indicate that our non-blocking queue consistently outperforms the best known alternatives, and that data-structure-specific non-blocking algorithms, which exist for queues, stacks, and counters, can work extremely well. Not only do they outperform preemption-safe lock-based algorithms on multiprogrammed machines, they also outperform ordinary locks on dedicated machines. At the same time, since general-purpose non-blocking techniques do not yet appear to be practical, preemption-safe locks remain the preferred alternative for complex data structures: they outperform

In this paper we prove time and space bounds for the implementation of the programming language NESL on various parallel machine models. NESL is a sugared typed J-calculus with a set of array primitives and an explicit parallel map over arrays. Our results extend previous work on provable implementation bounds for functional languages by considering space and by including arrays. For modeling the cost of NESL we augment a standard call-by-value operational semantics to return two cost measures: a DAG representing the sequential dependence in the computation, and a measure of the space taken by a sequential implementation. We show that a NESL program with w work (nodes in the DAG), d depth (levels in the DAG), and s sequential space can be implemented on a p processor butterfly network, hypercube, or CRCW PRAM usin O(w/p + d log p) time and 0(s + dp logp) reachable space. For programs with sufficient parallelism these bounds are optimal in that they give linew speedup and use space within a constant factor of the sequential space. 1

Most complexitymeasures for concurrent algorithms for asynchronous sharedmemory architectures focus on process steps and memory consumption. In practice, however, performance of multiprocessor algorithms is heavily influenced by contention, the extent to which processes access the same location at the same time. Nevertheless, even though contention is one of the principal considerations affecting the performance of real algorithms on real multiprocessors, there are no formal tools for analyzing the contention of asynchronous shared-memory algorithms. This paper introduces the first formal complexity model for contention in multiprocessors. We focus on the standard multiprocessor architecture in which n asynchronous processes communicate by applying read, write, and read-modify-write operations to a shared memory. We use our model to derive two kinds of results: (1) lower bounds on contention for well known basic problems such as agreement and mutual exclusion, and (2) trade-offs betwe...

Shared counters are among the most basic coordination structures in multiprocessor computation, with applications ranging from barrier synchronization to concurrent-data-structure design. This article introduces diffracting trees, novel data structures for shared counting and load balancing in a distributed/parallel environment. Empirical evidence, collected on a simulated distributed shared-memory machine and several simulated message-passing architectures, shows that diffracting trees scale better and are more robust than both combining trees and counting networks, currently the most effective known methods for implementing concurrent counters in software. The use of a randomized coordination method together with a combinatorial data structure overcomes the resiliency drawbacks of combining trees. Our simulations show that to handle the same load, diffracting trees and counting networks should have a similar width w, yet the depth of a diffracting tree is O(log w), whereas counting networks have depth O(log 2 w). Diffracting trees have already been used to implement highly efficient producer/consumer queues, and we believe diffraction will prove to be an effective alternative paradigm to combining and queue-locking in the design of many concurrent data structures.