Many programming languages support either task parallelism, but few languages provide a uniform framework for writing applications that need both types of parallelism or data parallelism. We present a programming language and system that integrates task and data parallelism using shared objects. Shared objects may be stored on one processor or may be replicated. Objects may also be partitioned and distributed on several processors.Task parallelism is achieved by forking processes remotely and have them communicate and synchronize through objects. Data parallelism is achieved by executing operations on partitioned objects in parallel. Writing task-and data-parallel applications with shared objects has several advantages. Programmers use the objects as if they were stored in a memory common to all processors. On distributed-memory machines, if objects are remote, replicated, or partitioned, the system takes care of many low-level details such as data transfers and consistency semantics. In this article, we show how to write task-and data-parallel programs with our shared object model. We also desribe a portable implementation of the model. To assess the performance of the system, we wrote several applications that use task and data parallelism and excuted them on a collection of Pentium Pros connected by Myrinet. The performance of these applications is also discussed in this article.

Supporting both task and data parallelism in one programming system is useful, since many applications need both types of parallelism. We present a programming model that integrates task and data parallelism using shared objects. The model is a generalization of shared objects in Orca. Orca is a task parallel language that uses shared objects for communication between processes and for storing shared (possibly replicated) data. Our new model also uses shared objects for partitioning of shared data and for distribution of work in a data parallel way. Data parallelism is introduced by executing operations on a partitioned object in parallel. The paper describes the design of the new model, its implementation, and its usage for parallel applications that use mixed task and data parallelism. 1 Introduction Most parallel programming systems are based either on data parallelism or on task parallelism. The advantage of data parallelism is that it is easy to use. The programmer merely specifi...

Many parallel and distributed programming models are based on some form of shared objects, which may be represented in various ways (e.g., single-copy, replicated, and partitioned objects). Also, many different operation execution strategies have been designed for each representation. In programming systems that use multiple representations integrated in a single object model, one way to provide multiple execution strategies is to implement each strategy independently from the others. However, this leads to rigid systems and provides little opportunity for code reuse. Instead, we propose a flexible operation execution model that allows the implementation of many different strategies, which can even be changed at runtime. We present the model and a distributed implementation of it. Also, we describe how various execution strategies can be expressed using the model, and we look at applications that benefit from its flexibility. 1 Introduction Shared objects have become a popular model...

Abstract Two kinds of parallel computers exist: those with shared memory and those without. The former are difficult to build but easy to program. The latter are easy to build but difficult to program. In this paper we present a hybrid model that combines the best properties of each by simulating a restricted object‐based shared memory on machines that do not share physical memory. In this model, objects can be replicated on multiple machines. An operation that does not change an object can then be done locally, without any network traffic. Update operations can be done using the reliable broadcast protocol described in the paper. We have constructed a prototype system, designed and implemented a new programming language for it, and programmed various applications using it. The model, algorithms, language, applications and performance will be discussed.