Much has been learned in the last decade about concurrent programming. This paper identifies the major concepts and describes some of the more important language notations for writing concurrent programs. The roles of processes, communication and syhchronization are discussed from both an operational and an axiomatic viewpoint. Language notations for expressing concurrent execution and for specifying process interaction are surveyed. Synchronization primitives based on shared variables and on message passing are described. Finally, three general classes of concurrent programming languages are identified and compared.

/pdf/concepts-and-notations-for-concurrent-programming-22haa9z7dm.pdf

Concepts and Notations for Concurrent Programming

: This paper considers a fixed (possibly infinite) set of distributed asynchronous processes which at various times are willing to communicate with each other We describe probabilistic distributed algorithms for synchronizing processes so that they can handshake at will The means of synchronization are boolean flag variables, each of which can be written by only one process and read by at most one other process The use of flag variables seems as to require the fewest assumptions possible without considering specific systems A process is considered to be tame over a time interval delta if its speed varies within certain arbitrarily fixed nonzero bounds We show our synchronization algorithms have real time response: If a pair of processes are mutually willing to communicate within a time interval delta of length at least a given constant and the pair are tame on delta, then they establish communication within delta with high likelihood (for the worst case behavior of the system and the expected time for establishment of communication is also constant We feel the term real time is merited, for the actual time needed for establishment of communication is upper bounded by a constant with overwhelming probability; furthermore, violations of this property occur with vanishingly low likelihood We have very few assumptions: (1) Tameness is required of a process only during the interval it is willing to communicate; and (2) the processes may be willing to communicate with a time varying set of processes which are only bounded in number

/pdf/real-time-synchronization-of-interprocess-communications-403f52l6on.pdf

Real-Time Synchronization of Interprocess Communications

This paper introduces probabilistic choice to synchronous parallel machine models; in particular parallel RAMs The power of probabilistic choice in parallel computations is illustrated by parallelizing some known probabilistic sequential algorithms We characterize the computational complexity of time, space, and processor bounded probabilistic prallel RAMs in terms of the computational complexity of probabilistic sequential RAMs We show that parallelism uniformly speeds up time bounded probabilistic sequential RAM computations by nearly a quadratic factor We also show that probabilistic choice can be, eliminated from parallel computations by introducing nonuniformity

On the Power of Probabilistic Choice in Synchronous Parallel Computations

In this paper we consider a resource allocation problem which is local in the sense that the maximum number of users competing for a particular resource at any time instant is bounded and also at any time instant the maximum number of resources that a user is willing to get is bounded. The problem may be viewed as that of achieving matchings in dynamically changing hypergraphs, via a distributed algorithm. We show that this problem is related to the fundamental problem of handshake communication (which can be viewed as achieving matchings in a dynamically changing graph, via distributed algorithms) in that an efficient solution to each of them implies an efficient solution to the other. We provide real-time solutions to the resource allocation problem (that is, we give distributed algorithms with real time response). We make essential use of probabilistic techniques as first used by [Rabin, 80b], where processes are allowed to make independent probabilistic choices. On the other hand, no probability assumptions about the system behavior are made. One of our solutions assumes the existence of an underlying real-time handshake communication system, as described in [Reif, Spirakis, 81]. Our other solution is based on efficient synchronization by flag variables, which are written only by one process and read by at most one other process. The special case of equi-speed processes is first examined. Then we generalize to asynchronous processes. Applications are made to dining philosophers, scheduling and two-phase locking in databases.

/pdf/real-time-resource-allocation-in-distributed-systems-44v2sjmpiu.pdf

Real time resource allocation in distributed systems

Much has been learned in the last decade about concurrent programming. This paper identifies the major concepts of concurrent programming and describes some of the more important language notations for writing concurrent programs. The roles of processes, communication and synchronization are discussed. Language notations for expressing concurrent execution and for specifying process interaction are surveyed. Synchronization primitives based on shared variables and on message passing are described. Finally, three general classes of concurrent programming languages are identified and compared.

Chapter 2 – Concepts and notations for concurrent programming*

This paper concerns the fundamental problem of synchronizing communication between distributed processes whose speeds (steps per real time unit) vary dynamically. Communication must be established in matching pairs, which are mutually willing to communicate. We show how to implement a distributed local scheduler to find these pairs. The only means of synchronization are boolean "flag" variables, each of which can be written by only one process and read by at most one other process.No global bounds in the speeds of processes are assumed. Processes with speed zero are considered dead. However, when their speed is nonzero then they execute their programs correctly. Dead processes do not harm our algorithms' performance with respect to pairs of other running processes. When the rate of change of the ratio of speeds of neighbour processes (i.e., relative acceleration) is bounded, then any two of these processes will establish communication within a constant number of steps of the slowest process with high likelihood. Thus our implementation has the property of achieving relative real time response. We can use our techniques to solve other problems such as resource allocation and implementation of parallel languages such as CSP and ADA. Note that we do not have any probability assumptions about the system behavior, although our algorithms use the technique of probabilistic choice.

/pdf/unbounded-speed-variability-in-distributed-communication-4kli6ddt0s.pdf

Unbounded speed variability in distributed communication systems

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Unbounded speed variability in distributed communication systems