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.

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Linearizability: a correctness condition for concurrent objects

Efficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NP-complete in general cases as well as in several restricted cases. Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm. The HEFT algorithm selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which minimizes its earliest finish time with an insertion-based approach. On the other hand, the CPOP algorithm uses the summation of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of both quality and cost of schedules, which are mainly presented with schedule length ratio, speedup, frequency of best results, and average scheduling time metrics.

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Performance-effective and low-complexity task scheduling for heterogeneous computing

Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad application combines computational "vertices" with communication "channels" to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of available computers, communicating as appropriate through flies, TCP pipes, and shared-memory FIFOs.The vertices provided by the application developer are quite simple and are usually written as sequential programs with no thread creation or locking. Concurrency arises from Dryad scheduling vertices to run simultaneously on multiple computers, or on multiple CPU cores within a computer. The application can discover the size and placement of data at run time, and modify the graph as the computation progresses to make efficient use of the available resources.Dryad is designed to scale from powerful multi-core single computers, through small clusters of computers, to data centers with thousands of computers. The Dryad execution engine handles all the difficult problems of creating a large distributed, concurrent application: scheduling the use of computers and their CPUs, recovering from communication or computer failures, and transporting data between vertices.

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Dryad: distributed data-parallel programs from sequential building blocks

Software architectures shift the focus of developers from lines-of-code to coarser-grained architectural elements and their overall interconnection structure. Architecture description languages (ADLs) have been proposed as modeling notations to support architecture-based development. There is, however, little consensus in the research community on what is an ADL, what aspects of an architecture should be modeled in an ADL, and which of several possible ADLs is best suited for a particular problem. Furthermore, the distinction is rarely made between ADLs on one hand and formal specification, module interconnection, simulation and programming languages on the other. This paper attempts to provide an answer to these questions. It motivates and presents a definition and a classification framework for ADLs. The utility of the definition is demonstrated by using it to differentiate ADLs from other modeling notations. The framework is used to classify and compare several existing ADLs, enabling us, in the process, to identify key properties of ADLs. The comparison highlights areas where existing ADLs provide extensive support and those in which they are deficient, suggesting a research agenda for the future.

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A classification and comparison framework for software architecture description languages

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

General approach to mapping of parallel computations upon multiprocessor architectures

Rollback and recovery (RR) is a method of enchancing the reliability of file or data base systems. At certain points in time, called checkpoints, a copy of the data or files is made on tape (or other storage devices). A chronological record is kept of all transactions which modify the data stored by the system; this record is called the audit trail. When an error is detected, the copy of the files or data made at the most recent checkpoint is loaded, and all transactions on the audit trail since this check point are reprocessed in chronological sequence, thus recovering from the error. This paper presents models and techniques which aid in determining optimal times for checkpoints.

Analytic models for rollback and recovery strategies in data base systems

A boundary element method for solving three-dimensional linear elasticity problems that involve a large number of particles embedded in a binder is introduced. The proposed method relies on an iterative solution strategy in which matrix—vector multiplication is performed with the fast multipole method. As a result the method is capable of solving problems with N unknowns using only O(N) memory and O(N) operations. Results are given for problems with hundreds of particles in which N"O(105). ( 1998 John Wiley & Sons, Ltd.

A fast solution method for three‐dimensional many‐particle problems of linear elasticity

CODE 2.0 is a graphical parallel programming system that targets the three goals of ease of use, portability, and production of efficient parallel code. Ease of use is provided by an integrated graphical/textual interface, a powerful dynamic model of parallel computation, and an integrated concept of program component reuse. Portability is approached by the declarative expression of synchronization and communication operators at a high level of abstraction in a manner which cleanly separates overall computation structure from the primitive sequential computations that make up a program. Execution efficiency is approached through a systematic class hierarchy that supports hierarchical translation refinement including special case recognition. This paper reports results obtained through experimental use of a prototype implementation of the CODE 2.0 system.CODE 2.0 represents a major conceptual advance over its predecessor systems (CODE 1.0 and CODE 1.2) in terms of the expressive power of the model of computation which is implemented and in potential for attaining efficiency across a wide spectrum of parallel architectures through the use of class hierarchies as a means of mapping from logical to executable program representations.

The CODE 2.0 graphical parallel programming language

This paper presents a computationally efficient run-time partitioning and load-balancing scheme for the distributed adaptive grid hierarchies that underlie adaptive mesh-refinement methods. The partitioning scheme yields an efficient parallel computational structure that maintains locality to reduce communications. Further, it enables dynamic re-partioning and load balancing of the adaptive grid hierarchy to be performed cost-effectively. The run-time partitioning support presented has been implemented within the framework of a data-management infrastructure supporting dynamic distributed data-structures for parallel adaptive numerical techniques. This infrastructure is the foundational layer of a computational toolkit for the Binary Black-Hole NSF Grand Challenge project.

James C. Browne

Papers

General approach to mapping of parallel computations upon multiprocessor architectures

Analytic models for rollback and recovery strategies in data base systems

A fast solution method for three‐dimensional many‐particle problems of linear elasticity

The CODE 2.0 graphical parallel programming language

On partitioning dynamic adaptive grid hierarchies