Dining Philosophers Theory and Concept in Operating System Scheduling
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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 -
Deadlock: Why Does It Happen? CS 537 Andrea C
UNIVERSITY of WISCONSIN-MADISON Computer Sciences Department Deadlock: Why does it happen? CS 537 Andrea C. Arpaci-Dusseau Introduction to Operating Systems Remzi H. Arpaci-Dusseau Informal: Every entity is waiting for resource held by another entity; none release until it gets what it is Deadlock waiting for Questions answered in this lecture: What are the four necessary conditions for deadlock? How can deadlock be prevented? How can deadlock be avoided? How can deadlock be detected and recovered from? Deadlock Example Deadlock Example Two threads access two shared variables, A and B int A, B; Variable A is protected by lock x, variable B by lock y lock_t x, y; How to add lock and unlock statements? Thread 1 Thread 2 int A, B; lock(x); lock(y); A += 10; B += 10; lock(y); lock(x); Thread 1 Thread 2 B += 20; A += 20; A += 10; B += 10; A += B; A += B; B += 20; A += 20; unlock(y); unlock(x); A += B; A += B; A += 30; B += 30; A += 30; B += 30; unlock(x); unlock(y); What can go wrong?? 1 Representing Deadlock Conditions for Deadlock Two common ways of representing deadlock Mutual exclusion • Vertices: • Resource can not be shared – Threads (or processes) in system – Resources (anything of value, including locks and semaphores) • Requests are delayed until resource is released • Edges: Indicate thread is waiting for the other Hold-and-wait Wait-For Graph Resource-Allocation Graph • Thread holds one resource while waits for another No preemption “waiting for” wants y held by • Resources are released voluntarily after completion T1 T2 T1 T2 Circular -
The Dining Philosophers Problem Cache Memory
The Dining Philosophers Problem Cache Memory 254 The dining philosophers problem: definition It is an artificial problem widely used to illustrate the problems linked to resource sharing in concurrent programming. The problem is usually described as follows. • A given number of philosopher are seated at a round table. • Each of the philosophers shares his time between two activities: thinking and eating. • To think, a philosopher does not need any resources; to eat he needs two pieces of silverware. 255 • However, the table is set in a very peculiar way: between every pair of adjacent plates, there is only one fork. • A philosopher being clumsy, he needs two forks to eat: the one on his right and the one on his left. • It is thus impossible for a philosopher to eat at the same time as one of his neighbors: the forks are a shared resource for which the philosophers are competing. • The problem is to organize access to these shared resources in such a way that everything proceeds smoothly. 256 The dining philosophers problem: illustration f4 P4 f0 P3 f3 P0 P2 P1 f1 f2 257 The dining philosophers problem: a first solution • This first solution uses a semaphore to model each fork. • Taking a fork is then done by executing a operation wait on the semaphore, which suspends the process if the fork is not available. • Freeing a fork is naturally done with a signal operation. 258 /* Definitions and global initializations */ #define N = ? /* number of philosophers */ semaphore fork[N]; /* semaphores modeling the forks */ int j; for (j=0, j < N, j++) fork[j]=1; Each philosopher (0 to N-1) corresponds to a process executing the following procedure, where i is the number of the philosopher. -
CSC 553 Operating Systems Multiple Processes
CSC 553 Operating Systems Lecture 4 - Concurrency: Mutual Exclusion and Synchronization Multiple Processes • Operating System design is concerned with the management of processes and threads: • Multiprogramming • Multiprocessing • Distributed Processing Concurrency Arises in Three Different Contexts: • Multiple Applications – invented to allow processing time to be shared among active applications • Structured Applications – extension of modular design and structured programming • Operating System Structure – OS themselves implemented as a set of processes or threads Key Terms Related to Concurrency Principles of Concurrency • Interleaving and overlapping • can be viewed as examples of concurrent processing • both present the same problems • Uniprocessor – the relative speed of execution of processes cannot be predicted • depends on activities of other processes • the way the OS handles interrupts • scheduling policies of the OS Difficulties of Concurrency • Sharing of global resources • Difficult for the OS to manage the allocation of resources optimally • Difficult to locate programming errors as results are not deterministic and reproducible Race Condition • Occurs when multiple processes or threads read and write data items • The final result depends on the order of execution – the “loser” of the race is the process that updates last and will determine the final value of the variable Operating System Concerns • Design and management issues raised by the existence of concurrency: • The OS must: – be able to keep track of various processes -
Supervision 1: Semaphores, Generalised Producer-Consumer, and Priorities
Concurrent and Distributed Systems - 2015–2016 Supervision 1: Semaphores, generalised producer-consumer, and priorities Q0 Semaphores (a) Counting semaphores are initialised to a value — 0, 1, or some arbitrary n. For each case, list one situation in which that initialisation would make sense. (b) Write down two fragments of pseudo-code, to be run in two different threads, that experience deadlock as a result of poor use of mutual exclusion. (c) Deadlock is not limited to mutual exclusion; it can occur any time its preconditions (especially hold-and-wait, cyclic dependence) occur. Describe a situation in which two threads making use of semaphores for condition synchronisation (e.g., in producer-consumer) can deadlock. int buffer[N]; int in = 0, out = 0; spaces = new Semaphore(N); items = new Semaphore(0); guard = new Semaphore(1); // for mutual exclusion // producer threads while(true) { item = produce(); wait(spaces); wait(guard); buffer[in] = item; in = (in + 1) % N; signal(guard); signal(items); } // consumer threads while(true) { wait(items); wait(guard); item = buffer[out]; out =(out+1) % N; signal(guard); signal(spaces); consume(item); } Figure 1: Pseudo-code for a producer-consumer queue using semaphores. 1 (d) In Figure 1, items and spaces are used for condition synchronisation, and guard is used for mutual exclusion. Why will this implementation become unsafe in the presence of multiple consumer threads or multiple producer threads, if we remove guard? (e) Semaphores are introduced in part to improve efficiency under contention around critical sections by preferring blocking to spinning. Describe a situation in which this might not be the case; more generally, under what circumstances will semaphores hurt, rather than help, performance? (f) The implementation of semaphores themselves depends on two classes of operations: in- crement/decrement of an integer, and blocking/waking up threads. -
Q1. Multiple Producers and Consumers
CS39002: Operating Systems Lab. Assignment 5 Floating date: 29/2/2016 Due date: 14/3/2016 Q1. Multiple Producers and Consumers Problem Definition: You have to implement a system which ensures synchronisation in a producer-consumer scenario. You also have to demonstrate deadlock condition and provide solutions for avoiding deadlock. In this system a main process creates 5 producer processes and 5 consumer processes who share 2 resources (queues). The producer's job is to generate a piece of data, put it into the queue and repeat. At the same time, the consumer process consumes the data i.e., removes it from the queue. In the implementation, you are asked to ensure synchronization and mutual exclusion. For instance, the producer should be stopped if the buffer is full and that the consumer should be blocked if the buffer is empty. You also have to enforce mutual exclusion while the processes are trying to acquire the resources. Manager (manager.c): It is the main process that creates the producers and consumer processes (5 each). After that it periodically checks whether the system is in deadlock. Deadlock refers to a specific condition when two or more processes are each waiting for another to release a resource, or more than two processes are waiting for resources in a circular chain. Implementation : The manager process (manager.c) does the following : i. It creates a file matrix.txt which holds a matrix with 2 rows (number of resources) and 10 columns (ID of producer and consumer processes). Each entry (i, j) of that matrix can have three values: ● 0 => process i has not requested for queue j or released queue j ● 1 => process i requested for queue j ● 2 => process i acquired lock of queue j. -
Towards Gradually Typed Capabilities in the Pi-Calculus
Towards Gradually Typed Capabilities in the Pi-Calculus Matteo Cimini University of Massachusetts Lowell Lowell, MA, USA matteo [email protected] Gradual typing is an approach to integrating static and dynamic typing within the same language, and puts the programmer in control of which regions of code are type checked at compile-time and which are type checked at run-time. In this paper, we focus on the π-calculus equipped with types for the modeling of input-output capabilities of channels. We present our preliminary work towards a gradually typed version of this calculus. We present a type system, a cast insertion procedure that automatically inserts run-time checks, and an operational semantics of a π-calculus that handles casts on channels. Although we do not claim any theoretical results on our formulations, we demonstrate our calculus with an example and discuss our future plans. 1 Introduction This paper presents preliminary work on integrating dynamically typed features into a typed π-calculus. Pierce and Sangiorgi have defined a typed π-calculus in which channels can be assigned types that express input and output capabilities [16]. Channels can be declared to be input-only, output-only, or that can be used for both input and output operations. As a consequence, this type system can detect unintended or malicious channel misuses at compile-time. The static typing nature of the type system prevents the modeling of scenarios in which the input- output discipline of a channel is discovered at run-time. In these scenarios, such a discipline must be enforced with run-time type checks in the style of dynamic typing. -
Deadlock-Free Oblivious Routing for Arbitrary Topologies
Deadlock-Free Oblivious Routing for Arbitrary Topologies Jens Domke Torsten Hoefler Wolfgang E. Nagel Center for Information Services and Blue Waters Directorate Center for Information Services and High Performance Computing National Center for Supercomputing Applications High Performance Computing Technische Universitat¨ Dresden University of Illinois at Urbana-Champaign Technische Universitat¨ Dresden Dresden, Germany Urbana, IL 61801, USA Dresden, Germany [email protected] [email protected] [email protected] Abstract—Efficient deadlock-free routing strategies are cru- network performance without inclusion of the routing al- cial to the performance of large-scale computing systems. There gorithm or the application communication pattern. In reg- are many methods but it remains a challenge to achieve lowest ular operation these values can hardly be achieved due latency and highest bandwidth for irregular or unstructured high-performance networks. We investigate a novel routing to network congestion. The largest gap between real and strategy based on the single-source-shortest-path routing al- idealized performance is often in bisection bandwidth which gorithm and extend it to use virtual channels to guarantee by its definition only considers the topology. The effective deadlock-freedom. We show that this algorithm achieves min- bisection bandwidth [2] is the average bandwidth for routing imal latency and high bandwidth with only a low number messages between random perfect matchings of endpoints of virtual channels and can be implemented in practice. We demonstrate that the problem of finding the minimal number (also known as permutation routing) through the network of virtual channels needed to route a general network deadlock- and thus considers the routing algorithm. -
Scalable Deadlock Detection for Concurrent Programs
Sherlock: Scalable Deadlock Detection for Concurrent Programs Mahdi Eslamimehr Jens Palsberg UCLA, University of California, Los Angeles, USA {mahdi,palsberg}@cs.ucla.edu ABSTRACT One possible schedule of the program lets the first thread We present a new technique to find real deadlocks in con- acquire the lock of A and lets the other thread acquire the current programs that use locks. For 4.5 million lines of lock of B. Now the program is deadlocked: the first thread Java, our technique found almost twice as many real dead- waits for the lock of B, while the second thread waits for locks as four previous techniques combined. Among those, the lock of A. 33 deadlocks happened after more than one million com- Usually a deadlock is a bug and programmers should avoid putation steps, including 27 new deadlocks. We first use a deadlocks. However, programmers may make mistakes so we known technique to find 1275 deadlock candidates and then have a bug-finding problem: provide tool support to find as we determine that 146 of them are real deadlocks. Our tech- many deadlocks as possible in a given program. nique combines previous work on concolic execution with a Researchers have developed many techniques to help find new constraint-based approach that iteratively drives an ex- deadlocks. Some require program annotations that typically ecution towards a deadlock candidate. must be supplied by a programmer; examples include [18, 47, 6, 54, 66, 60, 42, 23, 35]. Other techniques work with unannotated programs and thus they are easier to use. In Categories and Subject Descriptors this paper we focus on techniques that work with unanno- D.2.5 Software Engineering [Testing and Debugging] tated Java programs. -
Enhanced Debugging for Data Races in Parallel
ENHANCED DEBUGGING FOR DATA RACES IN PARALLEL PROGRAMS USING OPENMP A Thesis Presented to the Faculty of the Department of Computer Science University of Houston In Partial Fulfillment of the Requirements for the Degree Master of Science By Marcus W. Hervey December 2016 ENHANCED DEBUGGING FOR DATA RACES IN PARALLEL PROGRAMS USING OPENMP Marcus W. Hervey APPROVED: Dr. Edgar Gabriel, Chairman Dept. of Computer Science Dr. Shishir Shah Dept. of Computer Science Dr. Barbara Chapman Dept. of Computer Science, Stony Brook University Dean, College of Natural Sciences and Mathematics ii iii Acknowledgements A special thank you to Dr. Barbara Chapman, Ph.D., Dr. Edgar Gabriel, Ph.D., Dr. Shishir Shah, Ph.D., Dr. Lei Huang, Ph.D., Dr. Chunhua Liao, Ph.D., Dr. Laksano Adhianto, Ph.D., and Dr. Oscar Hernandez, Ph.D. for their guidance and support throughout this endeavor. I would also like to thank Van Bui, Deepak Eachempati, James LaGrone, and Cody Addison for their friendship and teamwork, without which this endeavor would have never been accomplished. I dedicate this thesis to my parents (Billy and Olivia Hervey) who have always chal- lenged me to be my best, and to my wife and kids for their love and sacrifice throughout this process. ”Our greatest weakness lies in giving up. The most certain way to succeed is always to try just one more time.” – Thomas Alva Edison iv ENHANCED DEBUGGING FOR DATA RACES IN PARALLEL PROGRAMS USING OPENMP An Abstract of a Thesis Presented to the Faculty of the Department of Computer Science University of Houston In Partial Fulfillment of the Requirements for the Degree Master of Science By Marcus W. -
Routing on the Channel Dependency Graph
TECHNISCHE UNIVERSITÄT DRESDEN FAKULTÄT INFORMATIK INSTITUT FÜR TECHNISCHE INFORMATIK PROFESSUR FÜR RECHNERARCHITEKTUR Routing on the Channel Dependency Graph: A New Approach to Deadlock-Free, Destination-Based, High-Performance Routing for Lossless Interconnection Networks Dissertation zur Erlangung des akademischen Grades Doktor rerum naturalium (Dr. rer. nat.) vorgelegt von Name: Domke Vorname: Jens geboren am: 12.09.1984 in: Bad Muskau Tag der Einreichung: 30.03.2017 Tag der Verteidigung: 16.06.2017 Gutachter: Prof. Dr. rer. nat. Wolfgang E. Nagel, TU Dresden, Germany Professor Tor Skeie, PhD, University of Oslo, Norway Multicast loops are bad since the same multicast packet will go around and around, inevitably creating a black hole that will destroy the Earth in a fiery conflagration. — OpenSM Source Code iii Abstract In the pursuit for ever-increasing compute power, and with Moore’s law slowly coming to an end, high- performance computing started to scale-out to larger systems. Alongside the increasing system size, the interconnection network is growing to accommodate and connect tens of thousands of compute nodes. These networks have a large influence on total cost, application performance, energy consumption, and overall system efficiency of the supercomputer. Unfortunately, state-of-the-art routing algorithms, which define the packet paths through the network, do not utilize this important resource efficiently. Topology- aware routing algorithms become increasingly inapplicable, due to irregular topologies, which either are irregular by design, or most often a result of hardware failures. Exchanging faulty network components potentially requires whole system downtime further increasing the cost of the failure. This management approach becomes more and more impractical due to the scale of today’s networks and the accompanying steady decrease of the mean time between failures. -
Reconciling Real and Stochastic Time: the Need for Probabilistic Refinement
DOI 10.1007/s00165-012-0230-y The Author(s) © 2012. This article is published with open access at Springerlink.com Formal Aspects Formal Aspects of Computing (2012) 24: 497–518 of Computing Reconciling real and stochastic time: the need for probabilistic refinement J. Markovski1,P.R.D’Argenio2,J.C.M.Baeten1,3 and E. P. de Vink1,3 1 Eindhoven University of Technology, Eindhoven, The Netherlands. E-mail: [email protected] 2 FaMAF, Universidad Nacional de Cordoba,´ Cordoba,´ Argentina 3 Centrum Wiskunde & Informatica, Amsterdam, The Netherlands Abstract. We conservatively extend an ACP-style discrete-time process theory with discrete stochastic delays. The semantics of the timed delays relies on time additivity and time determinism, which are properties that enable us to merge subsequent timed delays and to impose their synchronous expiration. Stochastic delays, however, interact with respect to a so-called race condition that determines the set of delays that expire first, which is guided by an (implicit) probabilistic choice. The race condition precludes the property of time additivity as the merger of stochastic delays alters this probabilistic behavior. To this end, we resolve the race condition using conditionally-distributed unit delays. We give a sound and ground-complete axiomatization of the process the- ory comprising the standard set of ACP-style operators. In this generalized setting, the alternative composition is no longer associative, so we have to resort to special normal forms that explicitly resolve the underlying race condition. Our treatment succeeds in the initial challenge to conservatively extend standard time with stochastic time. However, the ‘dissection’ of the stochastic delays to conditionally-distributed unit delays comes at a price, as we can no longer relate the resolved race condition to the original stochastic delays.