Download Free Task Scheduling For Parallel Systems Book in PDF and EPUB Free Download. You can read online Task Scheduling For Parallel Systems and write the review.

A new model for task scheduling that dramatically improves the efficiency of parallel systems Task scheduling for parallel systems can become a quagmire of heuristics, models, and methods that have been developed over the past decades. The author of this innovative text cuts through the confusion and complexity by presenting a consistent and comprehensive theoretical framework along with realistic parallel system models. These new models, based on an investigation of the concepts and principles underlying task scheduling, take into account heterogeneity, contention for communication resources, and the involvement of the processor in communications. For readers who may be new to task scheduling, the first chapters are essential. They serve as an excellent introduction to programming parallel systems, and they place task scheduling within the context of the program parallelization process. The author then reviews the basics of graph theory, discussing the major graph models used to represent parallel programs. Next, the author introduces his task scheduling framework. He carefully explains the theoretical background of this framework and provides several examples to enable readers to fully understand how it greatly simplifies and, at the same time, enhances the ability to schedule. The second half of the text examines both basic and advanced scheduling techniques, offering readers a thorough understanding of the principles underlying scheduling algorithms. The final two chapters address communication contention in scheduling and processor involvement in communications. Each chapter features exercises that help readers put their new skills into practice. An extensive bibliography leads to additional information for further research. Finally, the use of figures and examples helps readers better visualize and understand complex concepts and processes. Researchers and students in distributed and parallel computer systems will find that this text dramatically improves their ability to schedule tasks accurately and efficiently.
Overview and Goals This book is dedicated to scheduling for parallel processing. Presenting a research ?eld as broad as this one poses considerable dif?culties. Scheduling for parallel computing is an interdisciplinary subject joining many ?elds of science and te- nology. Thus, to understand the scheduling problems and the methods of solving them it is necessary to know the limitations in related areas. Another dif?culty is that the subject of scheduling parallel computations is immense. Even simple search in bibliographical databases reveals thousands of publications on this topic. The - versity in understanding scheduling problems is so great that it seems impossible to juxtapose them in one scheduling taxonomy. Therefore, most of the papers on scheduling for parallel processing refer to one scheduling problem resulting from one way of perceiving the reality. Only a few publications attempt to arrange this ?eld of knowledge systematically. In this book we will follow two guidelines. One guideline is a distinction - tween scheduling models which comprise a set of scheduling problems solved by dedicated algorithms. Thus, the aim of this book is to present scheduling models for parallel processing, problems de?ned on the grounds of certain scheduling models, and algorithms solving the scheduling problems. Most of the scheduling problems are combinatorial in nature. Therefore, the second guideline is the methodology of computational complexity theory. Inthisbookwepresentfourexamplesofschedulingmodels. Wewillgodeepinto the models, problems, and algorithms so that after acquiring some understanding of them we will attempt to draw conclusions on their mutual relationships.
This book presents task-scheduling techniques for emerging complex parallel architectures including heterogeneous multi-core architectures, warehouse-scale datacenters, and distributed big data processing systems. The demand for high computational capacity has led to the growing popularity of multicore processors, which have become the mainstream in both the research and real-world settings. Yet to date, there is no book exploring the current task-scheduling techniques for the emerging complex parallel architectures. Addressing this gap, the book discusses state-of-the-art task-scheduling techniques that are optimized for different architectures, and which can be directly applied in real parallel systems. Further, the book provides an overview of the latest advances in task-scheduling policies in parallel architectures, and will help readers understand and overcome current and emerging issues in this field.
Multiple processor systems are an important class of parallel systems. Over the years, several architectures have been proposed to build such systems to satisfy the requirements of high performance computing. These architectures span a wide variety of system types. At the low end of the spectrum, we can build a small, shared-memory parallel system with tens of processors. These systems typically use a bus to interconnect the processors and memory. Such systems, for example, are becoming commonplace in high-performance graph ics workstations. These systems are called uniform memory access (UMA) multiprocessors because they provide uniform access of memory to all pro cessors. These systems provide a single address space, which is preferred by programmers. This architecture, however, cannot be extended even to medium systems with hundreds of processors due to bus bandwidth limitations. To scale systems to medium range i. e. , to hundreds of processors, non-bus interconnection networks have been proposed. These systems, for example, use a multistage dynamic interconnection network. Such systems also provide global, shared memory like the UMA systems. However, they introduce local and remote memories, which lead to non-uniform memory access (NUMA) architecture. Distributed-memory architecture is used for systems with thousands of pro cessors. These systems differ from the shared-memory architectures in that there is no globally accessible shared memory. Instead, they use message pass ing to facilitate communication among the processors. As a result, they do not provide single address space.
Multiple processor systems are an important class of parallel systems. Over the years, several architectures have been proposed to build such systems to satisfy the requirements of high performance computing. These architectures span a wide variety of system types. At the low end of the spectrum, we can build a small, shared-memory parallel system with tens of processors. These systems typically use a bus to interconnect the processors and memory. Such systems, for example, are becoming commonplace in high-performance graph ics workstations. These systems are called uniform memory access (UMA) multiprocessors because they provide uniform access of memory to all pro cessors. These systems provide a single address space, which is preferred by programmers. This architecture, however, cannot be extended even to medium systems with hundreds of processors due to bus bandwidth limitations. To scale systems to medium range i. e. , to hundreds of processors, non-bus interconnection networks have been proposed. These systems, for example, use a multistage dynamic interconnection network. Such systems also provide global, shared memory like the UMA systems. However, they introduce local and remote memories, which lead to non-uniform memory access (NUMA) architecture. Distributed-memory architecture is used for systems with thousands of pro cessors. These systems differ from the shared-memory architectures in that there is no globally accessible shared memory. Instead, they use message pass ing to facilitate communication among the processors. As a result, they do not provide single address space.
It is of ever-increasing importance that programs are able to take full advantage of the parallel systems on which they are run. Task scheduling is the problem of producing a schedule for a program, such that the tasks which make up the program are each allocated to a specific processor and in a specific order which minimises the overall run-time. This problem is NP-hard, so that the amount of work required grows exponentially as the number of tasks is increased. Although the NP-hardness of the problem usually discourages optimal solving, an optimal schedule can give a significant advantage in time critical systems or applications where a single schedule is reused many times. Previous research with branch-and-bound for optimal task scheduling has shown promise with small task graphs, being competitive with other methods. The state-space model used in that work has an obvious drawback of allowing many duplicate states to occur in the state-space, which theoretically causes a large amount of additional time and memory to be required. This thesis proposes a new state-space model called Allocation-Ordering (AO), which improves on older models through its carefully designed lack of duplicate states. AO divides the task scheduling problem into two distinct sub-problems (allocation and ordering) which are handled in sequence within the state-space. Experimental evaluation confirms the benefits of the model. The benefits of AO’s lack of duplicate states for other branch and bound algorithms are then explored, specifically variants with interesting properties such as parallelisation and low memory requirements. We then investigate its applicability to more complex task scheduling models: the model is first adapted to allow optimal task scheduling with related heterogeneous processors, and then to allow optimal task scheduling with task duplication. The success of the adaptation of AO shows its flexibility, and suggests it may have wide applicability to variants of the task scheduling problem, and potentially other problems.
This volume contains the papers presented at the 10th Anniversary Workshop on Job Scheduling Strategies for Parallel Processing. The workshop was held in New York City, on June 13, 2004, at Columbia University, in conjunction with the SIGMETRICS 2004 conference. Although it is a workshop, the papers were conference-reviewed, with the full versions being read and evaluated by at least five and usually seven members of the Program Committee. We refer to it as a workshop because of the very fast turnaround time, the intimate nature of the actual presentations, and the ability of the authors to revise their papers after getting feedback from workshop attendees. On the other hand, it was actually a conference in that the papers were accepted solely on their merits as decided upon by the Program Committee. We would like to thank the Program Committee members, Su-Hui Chiang, Walfredo Cirne, Allen Downey, Eitan Frachtenberg, Wolfgang Gentzsch, Allan Gottlieb, Moe Jette, Richard Lagerstrom, Virginia Lo, Reagan Moore, Bill Nitzberg, Mark Squillante, and John Towns, for an excellent job. Thanks are also due to the authors for their submissions, presentations, and final revisions for this volume. Finally, we would like to thank the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), The Hebrew University, and Columbia University for the use of their facilities in the preparation of the workshop and these proceedings.
This book constitutes the thoroughly refereed post-conference proceedings of the 17th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2013, held Boston, MA, USA, in May 2013. The 10 revised papers presented were carefully reviewed and selected from 20 submissions. The papers cover the following topics parallel scheduling for commercial environments, scientific computing, supercomputing and cluster platforms.
Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques advocates the viability of using fuzzy and annealing methods in solving scheduling problems for parallel computing systems. The book proposes new techniques for both static and dynamic scheduling, using emerging paradigms that are inspired by natural phenomena such as fuzzy logic, mean-field annealing, and simulated annealing. Systems that are designed using such techniques are often referred to in the literature as `intelligent' because of their capability to adapt to sudden changes in their environments. Moreover, most of these changes cannot be anticipated in advance or included in the original design of the system. Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques provides results that prove such approaches can become viable alternatives to orthodox solutions to the scheduling problem, which are mostly based on heuristics. Although heuristics are robust and reliable when solving certain instances of the scheduling problem, they do not perform well when one needs to obtain solutions to general forms of the scheduling problem. On the other hand, techniques inspired by natural phenomena have been successfully applied for solving a wide range of combinatorial optimization problems (e.g. traveling salesman, graph partitioning). The success of these methods motivated their use in this book to solve scheduling problems that are known to be formidable combinatorial problems. Scheduling in Parallel Computing Systems: Fuzzy and Annealing Techniques is an excellent reference and may be used for advanced courses on the topic.
Full of practical examples, Introduction to Scheduling presents the basic concepts and methods, fundamental results, and recent developments of scheduling theory. With contributions from highly respected experts, it provides self-contained, easy-to-follow, yet rigorous presentations of the material.The book first classifies scheduling problems and