Distributed Programming with Shared Data

Until recently, at least one thing was clear about parallel programming: tightly coupled (shared memory) machines were programmed in a language based on shared variables and loosely coupled (distributed) systems were programmed using message passing. The explosive growth of research on distributed systems and their languages, however, has led to several new methodologies that blur this simple distinction. Operating system primitives (e.g., problem-oriented shared memory, Shared Virtual Memory, the Agora shared memory) and languages (e.g., Concurrent Prolog, Linda, Emerald) for programming distributed systems have been proposed that support the shared variable paradigm without the presence of physical shared memory. In this paper we will look at the reasons for this evolution, the resemblances and differences among these new proposals, and the key issues in their design and implementation. It turns out that many implementations are based on replication of data. We take this idea one step further, and discuss how automatic replication (initiated by the run time system) can be used as a basis for a new model, called the shared data-object model, whose semantics are similar to the shared variable model. Finally, we discuss the design of a new language for distributed programming, Orca, based on the shared data-object model. 1

Programming Languages for Distributed Computing Systems

When distributed systems first appeared, they were programmed in traditional sequential languages, usually with the addition of a few library procedures for sending and receiving messages. As distributed applications became more commonplace and more sophisticated, this ad hoc approach became less satisfactory. Researchers all over the world began designing new programming languages specifically for implementing distributed applications. These languages and their history, their underlying principles, their design, and their use are the subject of this paper. We begin by giving our view of what a distributed system is, illustrating with examples to avoid confusion on this important and controversial point. We then describe the three main characteristics that distinguish distributed programming languages from traditional sequential languages, namely, how they deal with parallelism, communication, and partial failures. Finally, we discuss 15 representative distributed languages to give the flavor of each. These examples include languages based on message passing, rendezvous, remote procedure call, objects, and atomic transactions, as well as functional languages, logic languages, and distributed data structure languages. The paper concludes with a comprehensive bibliography listing over 200 papers on nearly 100 distributed programming languages

Programming a Distributed System Using Shared Objects

Building the hardware for a high-performance distributed computer system is a lot easier than building its software. The authors describe a model for programming distributed systems based on abstract data types that can be replicated on all machines that need them. Read operations are done locally, without requiring network traffic. Writes can be done using a reliable broadcast algorithm if the hardware supports broadcasting; otherwise, a point-to-point protocol is used. The authors have built such a system based on the Amoeba microkernel, and implemented a language, Orca, on top of it. For Orca applications that have a high ratio of reads to writes, they measure good speedups on a system with 16 processors

Orca: A Language for Parallel Programming of Distributed Systems

Orca is a language for implementing parallel applications on loosely coupled distributed systems. Unlike most languages for distributed programming, it allows processes on different machines to share data. Such data are encapsulated in data-objects, which are instances of user-defined abstract data types. The implementation of Orca takes care of the physical distribution of objects among the local memories of the processors. In particular, an implementation may replicate and/or migrate objects in order to decrease access times to objects and increase parallelism. This paper gives a detailed description of the Orca language design and motivates the design choices. Orca is intended for applications programmers rather than systems programmers. This is reflected in its design goals to provide a simple, easy to use language that is type-secure and provides clean semantics. The paper discusses three example parallel applications in Orca, one of which is described in detail. It also describes..

Experience with Distributed Programming in Orca

Orca is a language for programming parallel applications on distributed computing systems. Although processors in such systems communicate only through message passing and not through shared memory, data types and create instances (objects) of these types, which may be shared among processes. All operations on shared objects are executed atomically. Orca’s shared objects are implemented by replicating them in the local memories of the proces-sors. Read operations use the local copies of the object, without doing any interprocess communication. Write operations update all copies using an efficient reliable broadcast protocol. In this paper, we briefly describe the language and its implementation and then report on our ex-periences in using Orca for three parallel applications: the Traveling Salesman Problem, the All-pairs Shortest Paths problem, and Successive Overrelaxation. These applications have different needs for shared data: TSP greatly benefits from the support for shared data; ASP benefits from the use of broad-cast communication, even though it is hidden in the implementation; SOR merely requires point-to-point communication, but still can be implemented in the language by simulating message passing

Parallel Programming Using Shared Objects and Broadcasting

The two major design approaches taken to build distributed and parallel computer systems, multiprocessing and multicomputing, are discussed. A model that combines the best properties of both multiprocessor and multicomputer systems, easy-to-build hardware, and a conceptually simple programming model is presented. Using this model, a programmer defines and invokes operations on shared objects, the runtime system handles reads and writes on these objects, and the reliable broadcast layer implements indivisible updates to objects using the sequencing protocol. The resulting system is easy to program, easy to build, and has acceptable performance on problems with a moderate grain size in which reads are much more common than writes. Orca, a procedural language whose sequential constructs are roughly similar to languages like C or Modula 2 but which also supports parallel processes and shared objects and has been used to develop applications for the prototype system, is described