Scheduling in Parallel and Distributed Computing Systems
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Data-Level Parallelism
Fall 2015 :: CSE 610 – Parallel Computer Architectures Data-Level Parallelism Nima Honarmand Fall 2015 :: CSE 610 – Parallel Computer Architectures Overview • Data Parallelism vs. Control Parallelism – Data Parallelism: parallelism arises from executing essentially the same code on a large number of objects – Control Parallelism: parallelism arises from executing different threads of control concurrently • Hypothesis: applications that use massively parallel machines will mostly exploit data parallelism – Common in the Scientific Computing domain • DLP originally linked with SIMD machines; now SIMT is more common – SIMD: Single Instruction Multiple Data – SIMT: Single Instruction Multiple Threads Fall 2015 :: CSE 610 – Parallel Computer Architectures Overview • Many incarnations of DLP architectures over decades – Old vector processors • Cray processors: Cray-1, Cray-2, …, Cray X1 – SIMD extensions • Intel SSE and AVX units • Alpha Tarantula (didn’t see light of day ) – Old massively parallel computers • Connection Machines • MasPar machines – Modern GPUs • NVIDIA, AMD, Qualcomm, … • Focus of throughput rather than latency Vector Processors 4 SCALAR VECTOR (1 operation) (N operations) r1 r2 v1 v2 + + r3 v3 vector length add r3, r1, r2 vadd.vv v3, v1, v2 Scalar processors operate on single numbers (scalars) Vector processors operate on linear sequences of numbers (vectors) 6.888 Spring 2013 - Sanchez and Emer - L14 What’s in a Vector Processor? 5 A scalar processor (e.g. a MIPS processor) Scalar register file (32 registers) Scalar functional units (arithmetic, load/store, etc) A vector register file (a 2D register array) Each register is an array of elements E.g. 32 registers with 32 64-bit elements per register MVL = maximum vector length = max # of elements per register A set of vector functional units Integer, FP, load/store, etc Some times vector and scalar units are combined (share ALUs) 6.888 Spring 2013 - Sanchez and Emer - L14 Example of Simple Vector Processor 6 6.888 Spring 2013 - Sanchez and Emer - L14 Basic Vector ISA 7 Instr. -
High Performance Computing Through Parallel and Distributed Processing
Yadav S. et al., J. Harmoniz. Res. Eng., 2013, 1(2), 54-64 Journal Of Harmonized Research (JOHR) Journal Of Harmonized Research in Engineering 1(2), 2013, 54-64 ISSN 2347 – 7393 Original Research Article High Performance Computing through Parallel and Distributed Processing Shikha Yadav, Preeti Dhanda, Nisha Yadav Department of Computer Science and Engineering, Dronacharya College of Engineering, Khentawas, Farukhnagar, Gurgaon, India Abstract : There is a very high need of High Performance Computing (HPC) in many applications like space science to Artificial Intelligence. HPC shall be attained through Parallel and Distributed Computing. In this paper, Parallel and Distributed algorithms are discussed based on Parallel and Distributed Processors to achieve HPC. The Programming concepts like threads, fork and sockets are discussed with some simple examples for HPC. Keywords: High Performance Computing, Parallel and Distributed processing, Computer Architecture Introduction time to solve large problems like weather Computer Architecture and Programming play forecasting, Tsunami, Remote Sensing, a significant role for High Performance National calamities, Defence, Mineral computing (HPC) in large applications Space exploration, Finite-element, Cloud science to Artificial Intelligence. The Computing, and Expert Systems etc. The Algorithms are problem solving procedures Algorithms are Non-Recursive Algorithms, and later these algorithms transform in to Recursive Algorithms, Parallel Algorithms particular Programming language for HPC. and Distributed Algorithms. There is need to study algorithms for High The Algorithms must be supported the Performance Computing. These Algorithms Computer Architecture. The Computer are to be designed to computer in reasonable Architecture is characterized with Flynn’s Classification SISD, SIMD, MIMD, and For Correspondence: MISD. Most of the Computer Architectures preeti.dhanda01ATgmail.com are supported with SIMD (Single Instruction Received on: October 2013 Multiple Data Streams). -
Distributed & Cloud Computing: Lecture 1
Distributed & cloud computing: Lecture 1 Chokchai “Box” Leangsuksun SWECO Endowed Professor, Computer Science Louisiana Tech University [email protected] CTO, PB Tech International Inc. [email protected] Class Info ■" Class Hours: noon-1:50 pm T-Th ! ■" Dr. Box’s & Contact Info: ●"www.latech.edu/~box ●"Phone 318-257-3291 ●"Email: [email protected] Class Info ■" Main Text: ! ●" 1) Distributed Systems: Concepts and Design By Coulouris, Dollimore, Kindberg and Blair Edition 5, © Addison-Wesley 2012 ●" 2) related publications & online literatures Class Info ■" Objective: ! ●"the theory, design and implementation of distributed systems ●"Discussion on abstractions, Concepts and current systems/applications ●"Best practice on Research on DS & cloud computing Course Contents ! •" The Characterization of distributed computing and cloud computing. •" System Models. •" Networking and inter-process communication. •" OS supports and Virtualization. •" RAS, Performance & Reliability Modeling. Security. !! Course Contents •" Introduction to Cloud Computing ! •" Various models and applications. •" Deployment models •" Service models (SaaS, PaaS, IaaS, Xaas) •" Public Cloud: Amazon AWS •" Private Cloud: openStack. •" Cost models ( between cloud vs host your own) ! •" ! !!! Course Contents case studies! ! •" Introduction to HPC! •" Multicore & openMP! •" Manycore, GPGPU & CUDA! •" Cloud-based EKG system! •" Distributed Object & Web Services (if time allows)! •" Distributed File system (if time allows)! •" Replication & Disaster Recovery Preparedness! !!!!!! Important Dates ! ■" Apr 10: Mid Term exam ■" Apr 22: term paper & presentation due ■" May 15: Final exam Evaluations ! ■" 20% Lab Exercises/Quizzes ■" 20% Programming Assignments ■" 10% term paper ■" 20% Midterm Exam ■" 30% Final Exam Intro to Distributed Computing •" Distributed System Definitions. •" Distributed Systems Examples: –" The Internet. –" Intranets. –" Mobile Computing –" Cloud Computing •" Resource Sharing. •" The World Wide Web. •" Distributed Systems Challenges. Based on Mostafa’s lecture 10 Distributed Systems 0. -
Task Scheduling Balancing User Experience and Resource Utilization on Cloud
Rochester Institute of Technology RIT Scholar Works Theses 7-2019 Task Scheduling Balancing User Experience and Resource Utilization on Cloud Sultan Mira [email protected] Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Mira, Sultan, "Task Scheduling Balancing User Experience and Resource Utilization on Cloud" (2019). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. Task Scheduling Balancing User Experience and Resource Utilization on Cloud by Sultan Mira A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science in Software Engineering Supervised by Dr. Yi Wang Department of Software Engineering B. Thomas Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester, New York July 2019 ii The thesis “Task Scheduling Balancing User Experience and Resource Utilization on Cloud” by Sultan Mira has been examined and approved by the following Examination Committee: Dr. Yi Wang Assistant Professor Thesis Committee Chair Dr. Pradeep Murukannaiah Assistant Professor Dr. Christian Newman Assistant Professor iii Dedication I dedicate this work to my family, loved ones, and everyone who has helped me get to where I am. iv Abstract Task Scheduling Balancing User Experience and Resource Utilization on Cloud Sultan Mira Supervising Professor: Dr. Yi Wang Cloud computing has been gaining undeniable popularity over the last few years. Among many techniques enabling cloud computing, task scheduling plays a critical role in both efficient resource utilization for cloud service providers and providing an excellent user ex- perience to the clients. -
Distributed Algorithms with Theoretic Scalability Analysis of Radial and Looped Load flows for Power Distribution Systems
Electric Power Systems Research 65 (2003) 169Á/177 www.elsevier.com/locate/epsr Distributed algorithms with theoretic scalability analysis of radial and looped load flows for power distribution systems Fangxing Li *, Robert P. Broadwater ECE Department Virginia Tech, Blacksburg, VA 24060, USA Received 15 April 2002; received in revised form 14 December 2002; accepted 16 December 2002 Abstract This paper presents distributed algorithms for both radial and looped load flows for unbalanced, multi-phase power distribution systems. The distributed algorithms are developed from tree-based sequential algorithms. Formulas of scalability for the distributed algorithms are presented. It is shown that computation time dominates communication time in the distributed computing model. This provides benefits to real-time load flow calculations, network reconfigurations, and optimization studies that rely on load flow calculations. Also, test results match the predictions of derived formulas. This shows the formulas can be used to predict the computation time when additional processors are involved. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Distributed computing; Scalability analysis; Radial load flow; Looped load flow; Power distribution systems 1. Introduction Also, the method presented in Ref. [10] was tested in radial distribution systems with no more than 528 buses. Parallel and distributed computing has been applied More recent works [11Á/14] presented distributed to many scientific and engineering computations such as implementations for power flows or power flow based weather forecasting and nuclear simulations [1,2]. It also algorithms like optimizations and contingency analysis. has been applied to power system analysis calculations These works also targeted power transmission systems. [3Á/14]. -
Resource Access Control Facility (RACF) General User's Guide
----- --- SC28-1341-3 - -. Resource Access Control Facility -------_.----- - - --- (RACF) General User's Guide I Production of this Book This book was prepared and formatted using the BookMaster® document markup language. Fourth Edition (December, 1988) This is a major revision of, and obsoletes, SC28-1341- 3and Technical Newsletter SN28-l2l8. See the Summary of Changes following the Edition Notice for a summary of the changes made to this manual. Technical changes or additions to the text and illustrations are indicated by a vertical line to the left of the change. This edition applies to Version 1 Releases 8.1 and 8.2 of the program product RACF (Resource Access Control Facility) Program Number 5740-XXH, and to all subsequent versions until otherwise indicated in new editions or Technical Newsletters. Changes are made periodically to the information herein; before using this publication in connection with the operation of IBM systems, consult the latest IBM Systemj370 Bibliography, GC20-0001, for the editions that are applicable and current. References in this publication to IBM products or services do not imply that IBM· intends to make these available in all countries in which IBM operates. Any reference to an IBM product in this publication is not intended to state or imply that only IBM's product may be used. Any functionally equivalent product may be used instead. This statement does not expressly or implicitly waive any intellectual property right IBM may hold in any product mentioned herein. Publications are not stocked at the address given below. Requests for IBM publications should be made to your IBM representative or to the IBM branch office serving your locality. -
3.A Fixed-Priority Scheduling
2017/18 UniPD - T. Vardanega 10/03/2018 Standard notation : Worst-case blocking time for the task (if applicable) 3.a Fixed-Priority Scheduling : Worst-case computation time (WCET) of the task () : Relative deadline of the task : The interference time of the task : Release jitter of the task : Number of tasks in the system Credits to A. Burns and A. Wellings : Priority assigned to the task (if applicable) : Worst-case response time of the task : Minimum time between task releases, or task period () : The utilization of each task ( ⁄) a-Z: The name of a task 2017/18 UniPD – T. Vardanega Real-Time Systems 168 of 515 The simplest workload model Fixed-priority scheduling (FPS) The application is assumed to consist of tasks, for fixed At present, the most widely used approach in industry All tasks are periodic with known periods Each task has a fixed (static) priority determined off-line This defines the periodic workload model In real-time systems, the “priority” of a task is solely derived The tasks are completely independent of each other from its temporal requirements No contention for logical resources; no precedence constraints The task’s relative importance to the correct functioning of All tasks have a deadline equal to their period the system or its integrity is not a driving factor at this level A recent strand of research addresses mixed-criticality systems, with Each task must complete before it is next released scheduling solutions that contemplate criticality attributes All tasks have a single fixed WCET, which can be trusted as The ready tasks (jobs) are dispatched to execution in a safe and tight upper-bound the order determined by their (static) priority Operation modes are not considered Hence, in FPS, scheduling at run time is fully defined by the All system overheads (context-switch times, interrupt handling and so on) are assumed absorbed in the WCETs priority assignment algorithm 2017/18 UniPD – T. -
Introduction to Multi-Threading and Vectorization Matti Kortelainen Larsoft Workshop 2019 25 June 2019 Outline
Introduction to multi-threading and vectorization Matti Kortelainen LArSoft Workshop 2019 25 June 2019 Outline Broad introductory overview: • Why multithread? • What is a thread? • Some threading models – std::thread – OpenMP (fork-join) – Intel Threading Building Blocks (TBB) (tasks) • Race condition, critical region, mutual exclusion, deadlock • Vectorization (SIMD) 2 6/25/19 Matti Kortelainen | Introduction to multi-threading and vectorization Motivations for multithreading Image courtesy of K. Rupp 3 6/25/19 Matti Kortelainen | Introduction to multi-threading and vectorization Motivations for multithreading • One process on a node: speedups from parallelizing parts of the programs – Any problem can get speedup if the threads can cooperate on • same core (sharing L1 cache) • L2 cache (may be shared among small number of cores) • Fully loaded node: save memory and other resources – Threads can share objects -> N threads can use significantly less memory than N processes • If smallest chunk of data is so big that only one fits in memory at a time, is there any other option? 4 6/25/19 Matti Kortelainen | Introduction to multi-threading and vectorization What is a (software) thread? (in POSIX/Linux) • “Smallest sequence of programmed instructions that can be managed independently by a scheduler” [Wikipedia] • A thread has its own – Program counter – Registers – Stack – Thread-local memory (better to avoid in general) • Threads of a process share everything else, e.g. – Program code, constants – Heap memory – Network connections – File handles -
Parallel Programming
Parallel Programming Parallel Programming Parallel Computing Hardware Shared memory: multiple cpus are attached to the BUS all processors share the same primary memory the same memory address on different CPU’s refer to the same memory location CPU-to-memory connection becomes a bottleneck: shared memory computers cannot scale very well Parallel Programming Parallel Computing Hardware Distributed memory: each processor has its own private memory computational tasks can only operate on local data infinite available memory through adding nodes requires more difficult programming Parallel Programming OpenMP versus MPI OpenMP (Open Multi-Processing): easy to use; loop-level parallelism non-loop-level parallelism is more difficult limited to shared memory computers cannot handle very large problems MPI(Message Passing Interface): require low-level programming; more difficult programming scalable cost/size can handle very large problems Parallel Programming MPI Distributed memory: Each processor can access only the instructions/data stored in its own memory. The machine has an interconnection network that supports passing messages between processors. A user specifies a number of concurrent processes when program begins. Every process executes the same program, though theflow of execution may depend on the processors unique ID number (e.g. “if (my id == 0) then ”). ··· Each process performs computations on its local variables, then communicates with other processes (repeat), to eventually achieve the computed result. In this model, processors pass messages both to send/receive information, and to synchronize with one another. Parallel Programming Introduction to MPI Communicators and Groups: MPI uses objects called communicators and groups to define which collection of processes may communicate with each other. -
Division 01 – General Requirements Section 01 32 16 – Construction Progress Schedule
DIVISION 01 – GENERAL REQUIREMENTS SECTION 01 32 16 – CONSTRUCTION PROGRESS SCHEDULE DIVISION 01 – GENERAL REQUIREMENTS SECTION 01 32 16 – CONSTRUCTION PROGRESS SCHEDULE PART 1 – GENERAL 1.01 SECTION INCLUDES A. This Section includes administrative and procedural requirements for the preparation, submittal, and maintenance of the progress schedule, reporting progress of the Work, and Contract Time adjustments, including the following: 1. Format. 2. Content. 3. Revisions to schedules. 4. Submittals. 5. Distribution. B. Refer to the General Conditions and the Agreement for definitions and specific dates of Contract Time. C. The above listed Project schedules shall be used for evaluating all issues related to time for this Contract. The Project schedules shall be updated in accordance with the requirements of this Section to reflect the actual progress of the Work and the Contractor’s current plan for the timely completion of the Work. The Project schedules shall be used by the Owner and Contractor for the following purposes as well as any other purpose where the issue of time is relevant: 1. To communicate to the Owner the Contractor’s current plan for carrying out the Work; 2. To identify work paths that is critical to the timely completion of the Work; 3. To identify upcoming activities on the Critical Path(s); 4. To evaluate the best course of action for mitigating the impact of unforeseen events; 5. As the basis for analyzing the time impact of changes in the Work; 6. As a reference in determining the cost associated with increases or decreases in the Work; 7. To identify when submittals will be submitted; 8. -
Demystifying Parallel and Distributed Deep Learning: an In-Depth Concurrency Analysis
1 Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis TAL BEN-NUN and TORSTEN HOEFLER, ETH Zurich, Switzerland Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed algorithms to low-level circuit design. In this survey, we describe the problem from a theoretical perspective, followed by approaches for its parallelization. We present trends in DNN architectures and the resulting implications on parallelization strategies. We then review and model the different types of concurrency in DNNs: from the single operator, through parallelism in network inference and training, to distributed deep learning. We discuss asynchronous stochastic optimization, distributed system architectures, communication schemes, and neural architecture search. Based on those approaches, we extrapolate potential directions for parallelism in deep learning. CCS Concepts: • General and reference → Surveys and overviews; • Computing methodologies → Neural networks; Parallel computing methodologies; Distributed computing methodologies; Additional Key Words and Phrases: Deep Learning, Distributed Computing, Parallel Algorithms ACM Reference Format: Tal Ben-Nun and Torsten Hoefler. 2018. Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis. 47 pages. 1 INTRODUCTION Machine Learning, and in particular Deep Learning [143], is rapidly taking over a variety of aspects in our daily lives. -
Real-Time Performance During CUDA™ a Demonstration and Analysis of Redhawk™ CUDA RT Optimizations
A Concurrent Real-Time White Paper 2881 Gateway Drive Pompano Beach, FL 33069 (954) 974-1700 www.concurrent-rt.com Real-Time Performance During CUDA™ A Demonstration and Analysis of RedHawk™ CUDA RT Optimizations By: Concurrent Real-Time Linux® Development Team November 2010 Overview There are many challenges to creating a real-time Linux distribution that provides guaranteed low process-dispatch latencies and minimal process run-time jitter. Concurrent Real Time’s RedHawk Linux distribution meets and exceeds these challenges, providing a hard real-time environment on many qualified hardware configurations, even in the presence of a heavy system load. However, there are additional challenges faced when guaranteeing real-time performance of processes while CUDA applications are simultaneously running on the system. The proprietary CUDA driver supplied by NVIDIA® frequently makes demands upon kernel resources that can dramatically impact real-time performance. This paper discusses a demonstration application developed by Concurrent to illustrate that RedHawk Linux kernel optimizations allow hard real-time performance guarantees to be preserved even while demanding CUDA applications are running. The test results will show how RedHawk performance compares to CentOS performance running the same application. The design and implementation details of the demonstration application are also discussed in this paper. Demonstration This demonstration features two selectable real-time test modes: 1. Jitter Mode: measure and graph the run-time jitter of a real-time process 2. PDL Mode: measure and graph the process-dispatch latency of a real-time process While the demonstration is running, it is possible to switch between these different modes at any time.