Efficient and correct execution of parallel programs that share memory

by Dennis Shasha

Publisher: Courant Institute of Mathematical Sciences, New York University in New York

Written in English
Cover of: Efficient and correct execution of parallel programs that share memory | Dennis Shasha
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Edition Notes

Statementby Dennis Shasha, Marc Snir.
SeriesUltracomputer note -- 96
ContributionsSnir, Marc
The Physical Object
Pagination30 p.
Number of Pages30
ID Numbers
Open LibraryOL17979186M

Parallel Programming Environments Introduction. To implement a parallel algorithm you need to construct a parallel program. The environment within which parallel programs are constructed is called the parallel programming mming environments correspond roughly to languages and libraries, as the examples below illustrate -- for example, HPF is a set of extensions to Fortran Programs written on Skandium may take advantage of shared memory to simplify parallel programming. Eden. Eden is a parallel programming language for distributed memory environments, which extends Haskell. Processes are defined explicitly to achieve parallel programming, while their communications remain implicit. Programming Massively Parallel Processors: A Hands-on Approach, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs.. Case studies demonstrate the development process, detailing computational thinking and ending with effective and efficient parallel. Concurrent computing is a form of computing in which several computations are executed concurrently—during overlapping time periods—instead of sequentially, with one completing before the next starts.. This is a property of a system—whether a program, computer, or a network—where there is a separate execution point or "thread of control" for each process.

1. To allow efficient and safe sharing of memory among several programs and to remove the programming burdens of a small, limited amount of main memory [still being used today] 2. To allow a single user program to exceed the size of primary emmory. Operating System Concepts Silberschatz, Galvin and Gagne Operating System Definitions Resource allocator – manages and allocates resources. Control program – controls the execution of user programs and operations of I/O devices. Kernel – the one program running at all times (all else being application programs). Operating System Concepts Silberschatz, Galvin and Gagne   #Pds #pdc #parallelcomputing #distributedsystem #lastmomenttuitions Take the Full Course of Parallel Computing and Distributed System: https://lastmomenttui. Responsive Parallel Computation. Elegant theoretical models and efficient schedulers exist for cooperatively parallel languages, in which threads share a single computational goal and do not compete for different resources. In this project, we extend these models to account for important features of interactive programs such as responsiveness and priority.

In computer science, the reduction operator is a type of operator that is commonly used in parallel programming to reduce the elements of an array into a single result. Reduction operators are associative and often (but not necessarily) commutative. The reduction of sets of elements is an integral part of programming models such as Map Reduce, where a reduction operator is applied to all. Program Execution; The purpose of computer system is to allow the users to execute programs in an efficient manner. The operating system provides an environment where the user can conveniently run these programs. The user does not have to worry about the memory allocation or de-allocation or any other thing because these things are taken care of by the operating system. Multiprocessing is the use of two or more central processing units (CPUs) within a single computer system. The term also refers to the ability of a system to support more than one processor or the ability to allocate tasks between them. There are many variations on this basic theme, and the definition of multiprocessing can vary with context. A computer program is a collection of instructions that can be executed by a computer to perform a specific task. Most computer devices require programs to function properly. A computer program is usually written by a computer programmer in a programming the program in its human-readable form of source code, a compiler or assembler can derive machine code—a form consisting of.

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Efficient and Correct Execution of Parallel Programs That Share Memory (Classic Reprint) Paperback – Febru by Dennis Shasha (Author)Cited by: In this paper we consider an optimization problem that arises in the execution of parallel programs on shared-memory multiple-instruction-stream, multiple-data-stream (MIMD) computers.

A program on such machines consists of many sequential program segments, each executed by a single : ShashaDennis, SnirMarc. Efficient and Correct Execution of Parallel Programs are created for an idealized parallel architecture, where machine instructions are executed atomically (an access to shared memory involves only one word).

Such programs may come from. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In this paper we consider an optimization problem that arises in the execution of parallel programs on Efficient and correct execution of parallel programs that share memory book multiple-instruction-stream, multiple-data-stream (MIMD) computers.

A program on such machines consists of many sequential program segments, each executed by a single processor. Efficient and Correct Programs that Share Execution of Parallel Memory. By Dennis Shasha and Marc Snir. Abstract. In this paper we consider an optimization problem that arises in the execution of parallel programs on shared-memory multiple-instruction-stream, multiple-data-stream (MIMD) computers.

A program on such machines consists of many Author: Dennis Shasha and Marc Snir. It complements today’s parallelizing compilers in that it helps to tune the performance of a compiler-optimized parallel program.

To show its applicability, we present two case studies that utilize this system. By simply following the suggestions of our system, we were able to reduce the execution time of benchmark programs by as much as 39%.Cited by: 4. We get to know about the two most important parallel architectures: distributed memory systems and shared memory systems.

Designing efficient parallel programs requires a lot of experience and we will study a number of typical considerations for this process such as problem partitioning strategies, communication patterns, synchronization, and.

Value numbering assigns distinct numbers to each value computed during run time. Two expressions, e1 and e2, have same value number iff they always compute the same value. We denote the value number of an expression e as V(e). If the value numbers. Sevcik, J. and Aspinall, D. On validity of program transformations in the Java memory model.

In Proceedings of the European Conference on Object-Oriented Programming,Google Scholar Digital Library; Shasha, D and Snir, M. Efficient and correct execution of parallel programs that share memory. This will happen on a single threaded program as well.

Unless you program has something to do while waiting for I/O, parallel threads won't help here. You just cause more work for the RTOS. The Memory Bottleneck / contention In most PCs and embedded applications that have multiple processor cores, the usually share the memory (external to the.

The book first offers information on Fortran, hardware and operating system models, and processes, shared memory, and simple parallel programs. Discussions focus on processes and processors, joining processes, shared memory, time-sharing with multiple processors, hardware, loops, passing arguments in function/subroutine calls, program structure.

term process may be defined as a part of a program that can be run on a processor. In designing a parallel algorithm, it is important to determine the efficiency of its use of available resources. Once a parallel algorithm has been developed, a measurement should be used for evaluating its performance (or efficiency) on a parallel Size: KB.

ADAPTIVE, EFFICIENT PARALLEL EXECUTION OF PARALLEL PROGRAMS by Srinath Sridharan A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Sciences) at the UNIVERSITY OF WISCONSIN–MADISON Date of final oral examination: 09/23/File Size: 2MB.

Memory management is too slow and cumbersome to solve the problem. Static allocation of memory resources is too inflexible and inefficient, as we will see. 4 Goals What do we care about. Fast program execution Efficient memory usage Avoid memory fragmentation Maintain data locality Allow recursive calls Support parallel execution Minimize.

Abstract. Weak memory models determine the behavior of concurrent programs. While they are often understood in terms of reorderings that the hardware or the compiler may perform, their formal definitions are typically given in a very different style—either axiomatic or by: In contrast to message passing systems, shared memory multiprocessors allow for efficient data sharing, and thus are more suitable for execution models that exploit medium grain parallelism.

This dissertation investigates the problem of memory management for a globally shared space in a parallel execution environment. Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously.

Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism.

Shared memory is an efficient means of passing data between processes. In a shared-memory model, parallel processes share a global address space that they read and write to asynchronously. Asynchronous concurrent access can lead to race conditions, and mechanisms such as locks, semaphores and monitors can be used to avoid these.

The power, frequency, and memory wall problems have caused a major shift in mainstream computing by introducing processors that contain multiple low power cores. As multi-core processors are becoming ubiquitous, software trends in both parallel programming languages and dynamic compilation have added new challenges to program compilation for.

Structured Parallel Programming offers the simplest way for developers to learn patterns for high-performance parallel programming. Written by parallel computing experts and industry insiders Michael McCool, Arch Robison, and James Reinders, this book explains how to design and implement maintainable and efficient parallel algorithms using a composable, structured, scalable, and machine Cited by:   Moore’s law will grant computer architects ever more transistors for the foreseeable future, and the challenge is how to use them to deliver efficient performance and flexible programmability.

We propose a many-core architecture, Godson-T, to attack this challenge. On the one hand, Godson-T features a region-based cache coherence protocol, asynchronous data transfer Cited by: When to Implement Parallel Execution. Parallel execution benefits systems with all of the following characteristics: S ymmetric multiprocessors (SMPs), clusters, or massively parallel systems.

Sufficient I/O bandwidth. Underutilized or intermittently used CPUs (for example, systems where CPU usage is typically less than 30%). Sufficient memory to support additional memory-intensive processes. writing correct parallel programs remains a c hallenging task.

First, within a single parallel program, m ultiple tasks ma y b e executed in parallel. It is more di cult for a programmer to k eep trac k of the exact execution paths of a program with m ultiple tasks. Hence, v erifying the results b ecomes more di cult.

Second, data comm. Future parallel processors will be heterogeneous, be increasingly less reliable, and operate in dynamically changing operating conditions. This will result in a constantly varying pool of hardware resources which can greatly complicate the task of efficiently exposing a program's parallelism onto these resources.

Coupled with this uncertainty is the diverse set of efficiency metrics that users Author: Guri Sohi. In-Memory Parallel Execution, enabled by setting the parameter PARALLEL_DEGREE_POLICY is set to AUTO, enables parallel statements to leverage the SGA to cache object Database decides if an object that is accessed using parallel execution would benefit from being cached in.

Some reasons for speedup > p (efficiency > 1) Parallel computer has p times as much RAM so higher fraction of program memory in RAM instead of disk An important reason for using parallel computers Parallel computer is solving slightly different, easier problem, or providing slightly different answer In developing parallel program a better algorithm.

Efficient Portability: Portability is an essential catalyst for the development of reusable parallel software. Charm++ programs run unchanged on MIMD machines with or without a shared memory. The programming model induces better data locality, allowing it to support machine independence without losing efficiency.

It's also the smallest unit of execution that Win32 schedules. A thread consists of a stack, the state of the CPU registers, and an entry in the execution list of the system scheduler.

Each thread shares all the process's resources. A process consists of one or more threads and the code, data, and other resources of a program in memory. The Finding Concurrency and Algorithm Structure design spaces focus on algorithm expression.

At some point, however, algorithms must be translated into programs. The patterns in the Supporting Structures design space address that phase of the parallel program design process, representing an intermediate stage between the problem-oriented patterns of the Algorithm Structure design space and the.

Provably Correct Vectorization of Nested Parallel Programs, December Piecewise Execution of Nested Parallel Programs, August Work-Efficient Nested Data-Parallelism, February Transforming High-Level Data-Parallel Programs into Vector Operations, May. This paper discusses how researchers have produced a set of portable parallel-programming constructs for C, implemented in M4 macros.

These parallel-programming macros are available under the name Parmacs. The Parmacs macros let one write parallel C programs for shared-memory, distributed-memory Cited by: Deadlock Avoidance in Parallel Programs with Futures Since future objects can be freely communicated via shared memory, program execution focus of this paper is twofold: to formally study the relationship between deadlocks on futures and accesses to shared memory, and to propose an efficient runtime techniqueFile Size: KB.A comprehensive overview of OpenMP, the standard application programming interface for shared memory parallel computing—a reference for students and professionals.

"I hope that readers will learn to use the full expressibility and power of OpenMP. This book should provide an excellent introduction to beginners, and the performance section should help those with some experience who want to.