Linearizability: a Correctness Condition for Concurrent Objects
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Concurrency Control Basics
Outline l Introduction/problems, l definitions Introduction/ (transaction, history, conflict, equivalence, Problems serializability, ...), Definitions l locking. Chapter 2: Locking Concurrency Control Basics Klemens Böhm Distributed Data Management: Concurrency Control Basics – 1 Klemens Böhm Distributed Data Management: Concurrency Control Basics – 2 Atomicity, Isolation Synchronisation, Distributed (1) l Transactional guarantees – l Essential feature of databases: in particular, atomicity and isolation. Many users can access the same data concurrently – be it read, be it write. Introduction/ l Atomicity Introduction/ Problems Problems u Example, „bank scenario“: l Consistency must be guaranteed – Definitions Definitions task of synchronization component. Locking Number Person Balance Locking Klemens 5000 l Multi-user mode shall be hidden from users as far as possible: concurrent processing Gunter 200 of requests shall be transparent, u Money transfer – two elementary operations. ‚illusion‘ of being the only user. – debit(Klemens, 500), – credit(Gunter, 500). l Isolation – can be explained with this example, too. l Transactions. Klemens Böhm Distributed Data Management: Concurrency Control Basics – 3 Klemens Böhm Distributed Data Management: Concurrency Control Basics – 4 Synchronisation, Distributed (2) Synchronization in General l Serial execution of application programs Uncontrolled non-serial execution u achieves that illusion leads to other problems, notably inconsistency: l Introduction/ without any synchronization effort, Introduction/ -
Concurrent Objects and Linearizability Concurrent Computaton
Concurrent Objects and Concurrent Computaton Linearizability memory Nir Shavit Subing for N. Lynch object Fall 2003 object © 2003 Herlihy and Shavit 2 Objectivism FIFO Queue: Enqueue Method • What is a concurrent object? q.enq( ) – How do we describe one? – How do we implement one? – How do we tell if we’re right? © 2003 Herlihy and Shavit 3 © 2003 Herlihy and Shavit 4 FIFO Queue: Dequeue Method Sequential Objects q.deq()/ • Each object has a state – Usually given by a set of fields – Queue example: sequence of items • Each object has a set of methods – Only way to manipulate state – Queue example: enq and deq methods © 2003 Herlihy and Shavit 5 © 2003 Herlihy and Shavit 6 1 Pre and PostConditions for Sequential Specifications Dequeue • If (precondition) – the object is in such-and-such a state • Precondition: – before you call the method, – Queue is non-empty • Then (postcondition) • Postcondition: – the method will return a particular value – Returns first item in queue – or throw a particular exception. • Postcondition: • and (postcondition, con’t) – Removes first item in queue – the object will be in some other state • You got a problem with that? – when the method returns, © 2003 Herlihy and Shavit 7 © 2003 Herlihy and Shavit 8 Pre and PostConditions for Why Sequential Specifications Dequeue Totally Rock • Precondition: • Documentation size linear in number –Queue is empty of methods • Postcondition: – Each method described in isolation – Throws Empty exception • Interactions among methods captured • Postcondition: by side-effects -
Computer Science 322 Operating Systems Topic Notes: Process
Computer Science 322 Operating Systems Mount Holyoke College Spring 2008 Topic Notes: Process Synchronization Cooperating Processes An Independent process is not affected by other running processes. Cooperating processes may affect each other, hopefully in some controlled way. Why cooperating processes? • information sharing • computational speedup • modularity or convenience It’s hard to find a computer system where processes do not cooperate. Consider the commands you type at the Unix command line. Your shell process and the process that executes your com- mand must cooperate. If you use a pipe to hook up two commands, you have even more process cooperation (See the shell lab later this semester). For the processes to cooperate, they must have a way to communicate with each other. Two com- mon methods: • shared variables – some segment of memory accessible to both processes • message passing – a process sends an explicit message that is received by another For now, we will consider shared-memory communication. We saw that threads, for example, share their global context, so that is one way to get two processes (threads) to share a variable. Producer-Consumer Problem The classic example for studying cooperating processes is the Producer-Consumer problem. Producer Consumer Buffer CS322 OperatingSystems Spring2008 One or more produces processes is “producing” data. This data is stored in a buffer to be “con- sumed” by one or more consumer processes. The buffer may be: • unbounded – We assume that the producer can continue producing items and storing them in the buffer at all times. However, the consumer must wait for an item to be inserted into the buffer before it can take one out for consumption. -
Cache Serializability: Reducing Inconsistency in Edge Transactions
Cache Serializability: Reducing Inconsistency in Edge Transactions Ittay Eyal Ken Birman Robbert van Renesse Cornell University tributed databases. Until recently, technical chal- Abstract—Read-only caches are widely used in cloud lenges have forced such large-system operators infrastructures to reduce access latency and load on to forgo transactional consistency, providing per- backend databases. Operators view coherent caches as object consistency instead, often with some form of impractical at genuinely large scale and many client- facing caches are updated in an asynchronous manner eventual consistency. In contrast, backend systems with best-effort pipelines. Existing solutions that support often support transactions with guarantees such as cache consistency are inapplicable to this scenario since snapshot isolation and even full transactional atom- they require a round trip to the database on every cache icity [9], [4], [11], [10]. transaction. Our work begins with the observation that it can Existing incoherent cache technologies are oblivious to be difficult for client-tier applications to leverage transactional data access, even if the backend database supports transactions. We propose T-Cache, a novel the transactions that the databases provide: trans- caching policy for read-only transactions in which incon- actional reads satisfied primarily from edge caches sistency is tolerable (won’t cause safety violations) but cannot guarantee coherency. Yet, by running from undesirable (has a cost). T-Cache improves cache consis- cache, client-tier transactions shield the backend tency despite asynchronous and unreliable communication database from excessive load, and because caches between the cache and the database. We define cache- are typically placed close to the clients, response serializability, a variant of serializability that is suitable latency can be improved. -
4. Process Synchronization
Lecture Notes for CS347: Operating Systems Mythili Vutukuru, Department of Computer Science and Engineering, IIT Bombay 4. Process Synchronization 4.1 Race conditions and Locks • Multiprogramming and concurrency bring in the problem of race conditions, where multiple processes executing concurrently on shared data may leave the shared data in an undesirable, inconsistent state due to concurrent execution. Note that race conditions can happen even on a single processor system, if processes are context switched out by the scheduler, or are inter- rupted otherwise, while updating shared data structures. • Consider a simple example of two threads of a process incrementing a shared variable. Now, if the increments happen in parallel, it is possible that the threads will overwrite each other’s result, and the counter will not be incremented twice as expected. That is, a line of code that increments a variable is not atomic, and can be executed concurrently by different threads. • Pieces of code that must be accessed in a mutually exclusive atomic manner by the contending threads are referred to as critical sections. Critical sections must be protected with locks to guarantee the property of mutual exclusion. The code to update a shared counter is a simple example of a critical section. Code that adds a new node to a linked list is another example. A critical section performs a certain operation on a shared data structure, that may temporar- ily leave the data structure in an inconsistent state in the middle of the operation. Therefore, in order to maintain consistency and preserve the invariants of shared data structures, critical sections must always execute in a mutually exclusive fashion. -
Goals of This Lecture How to Increase the Compute Power?
Goals of this lecture Motivate you! Design of Parallel and High-Performance Trends Computing High performance computing Fall 2017 Programming models Lecture: Introduction Course overview Instructor: Torsten Hoefler & Markus Püschel TA: Salvatore Di Girolamo 2 © Markus Püschel Computer Science Trends What doubles …? Source: Wikipedia 3 4 How to increase the compute power? Clock Speed! 10000 Sun’s Surface 1000 ) 2 Rocket Nozzle Nuclear Reactor 100 10 8086 Hot Plate 8008 8085 Pentium® Power Density (W/cm Power 4004 286 386 486 Processors 8080 1 1970 1980 1990 2000 2010 Source: Intel Source: Wikipedia 5 6 How to increase the compute power? Evolutions of Processors (Intel) Not an option anymore! Clock Speed! 10000 Sun’s Surface 1000 ) 2 Rocket Nozzle Nuclear Reactor 100 ~3 GHz Pentium 4 Core Nehalem Haswell Pentium III Sandy Bridge Hot Plate 10 8086 Pentium II 8008 free speedup 8085 Pentium® Pentium Pro Power Density (W/cm Power 4004 286 386 486 Processors 8080 Pentium 1 1970 1980 1990 2000 2010 Source: Intel 7 8 Source: Wikipedia/Intel/PCGuide Evolutions of Processors (Intel) Evolutions of Processors (Intel) Cores: 8x ~360 Gflop/s Vector units: 8x parallelism: work required 2 2 4 4 4 8 cores ~3 GHz Pentium 4 Core Nehalem Haswell Pentium III Sandy Bridge Pentium II Pentium Pro free speedup Pentium memory bandwidth (normalized) 9 10 Source: Wikipedia/Intel/PCGuide Source: Wikipedia/Intel/PCGuide A more complete view Source: www.singularity.com Can we do this today? 11 12 © Markus Püschel Computer Science High-Performance Computing (HPC) a.k.a. “Supercomputing” Question: define “Supercomputer”! High-Performance Computing 13 14 High-Performance Computing (HPC) a.k.a. -
The Critical Section Problem Problem Description
The Critical Section Problem Problem Description Informally, a critical section is a code segment that accesses shared variables and has to be executed as an atomic action. The critical section problem refers to the problem of how to ensure that at most one process is executing its critical section at a given time. Important: Critical sections in different threads are not necessarily the same code segment! Concurrent Software Systems 2 1 Problem Description Formally, the following requirements should be satisfied: Mutual exclusion: When a thread is executing in its critical section, no other threads can be executing in their critical sections. Progress: If no thread is executing in its critical section, and if there are some threads that wish to enter their critical sections, then one of these threads will get into the critical section. Bounded waiting: After a thread makes a request to enter its critical section, there is a bound on the number of times that other threads are allowed to enter their critical sections, before the request is granted. Concurrent Software Systems 3 Problem Description In discussion of the critical section problem, we often assume that each thread is executing the following code. It is also assumed that (1) after a thread enters a critical section, it will eventually exit the critical section; (2) a thread may terminate in the non-critical section. while (true) { entry section critical section exit section non-critical section } Concurrent Software Systems 4 2 Solution 1 In this solution, lock is a global variable initialized to false. A thread sets lock to true to indicate that it is entering the critical section. -
Core Processors
UNIVERSITY OF CALIFORNIA Los Angeles Parallel Algorithms for Medical Informatics on Data-Parallel Many-Core Processors A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Computer Science by Maryam Moazeni 2013 © Copyright by Maryam Moazeni 2013 ABSTRACT OF THE DISSERTATION Parallel Algorithms for Medical Informatics on Data-Parallel Many-Core Processors by Maryam Moazeni Doctor of Philosophy in Computer Science University of California, Los Angeles, 2013 Professor Majid Sarrafzadeh, Chair The extensive use of medical monitoring devices has resulted in the generation of tremendous amounts of data. Storage, retrieval, and analysis of such data require platforms that can scale with data growth and adapt to the various behavior of the analysis and processing algorithms. In recent years, many-core processors and more specifically many-core Graphical Processing Units (GPUs) have become one of the most promising platforms for high performance processing of data, due to the massive parallel processing power they offer. However, many of the algorithms and data structures used in medical and bioinformatics systems do not follow a data-parallel programming paradigm, and hence cannot fully benefit from the parallel processing power of ii data-parallel many-core architectures. In this dissertation, we present three techniques to adapt several non-data parallel applications in different dwarfs to modern many-core GPUs. First, we present a load balancing technique to maximize parallelism in non-serial polyadic Dynamic Programming (DP), which is a family of dynamic programming algorithms with more non-uniform data access pattern. We show that a bottom-up approach to solving the DP problem exploits more parallelism and therefore yields higher performance. -
Rtos Implementation of Non-Linear System Using Multi Tasking, Scheduling and Critical Section
Journal of Computer Science 10 (11): 2349-2357, 2014 ISSN: 1549-3636 © 2014 M. Sujitha et al ., This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license doi:10.3844/jcssp.2014.2349.2357 Published Online 10 (11) 2014 (http://www.thescipub.com/jcs.toc) RTOS IMPLEMENTATION OF NON-LINEAR SYSTEM USING MULTI TASKING, SCHEDULING AND CRITICAL SECTION 1Sujitha, M., 2V. Kannan and 3S. Ravi 1,3 Department of ECE, Dr. M.G.R. Educational and Research Institute University, Chennai, India 2Jeppiaar Institute of Technology, Kanchipuram, India Received 2014-04-21; Revised 2014-07-19; Accepted 2014-12-16 ABSTRACT RTOS based embedded systems are designed with priority based multiple tasks. Inter task communication and data corruptions are major constraints in multi-tasking RTOS system. This study we describe about the solution for these issues with an example Real-time Liquid level control system. Message queue and Mail box are used to perform inter task communication to improve the task execution time and performance of the system. Critical section scheduling is used to eliminate the data corruption. In this application process value monitoring is considered as critical. In critical section the interrupt disable time is the most important specification of a real time kernel. RTOS is used to keep the interrupt disable time to a minimum. The algorithm is studied with respect to task execution time and response of the interrupts. The study also presents the comparative analysis of the system with critical section and without critical section based on the performance metrics. Keywords: Critical Section, RTOS, Scheduling, Resource Sharing, Mailbox, Message Queue 1. -
Concurrent and Parallel Programming
Concurrent and parallel programming Seminars in Advanced Topics in Computer Science Engineering 2019/2020 Romolo Marotta Trend in processor technology Concurrent and parallel programming 2 Blocking synchronization SHARED RESOURCE Concurrent and parallel programming 3 Blocking synchronization …zZz… SHARED RESOURCE Concurrent and parallel programming 4 Blocking synchronization Correctness guaranteed by mutual exclusion …zZz… SHARED RESOURCE Performance might be hampered because of waste of clock cycles Liveness might be impaired due to the arbitration of accesses Concurrent and parallel programming 5 Parallel programming • Ad-hoc concurrent programming languages • Development tools ◦ Compilers ◦ MPI, OpenMP, libraries ◦ Tools to debug parallel code (gdb, valgrind) • Writing parallel code is an art ◦ There are approaches, not prepackaged solutions ◦ Every machine has its own singularities ◦ Every problem to face has different requisites ◦ The most efficient parallel algorithm might not be the most intuitive one Concurrent and parallel programming 6 What do we want from parallel programs? • Safety: nothing wrong happens (Correctness) ◦ parallel versions of our programs should be correct as their sequential implementations • Liveliness: something good happens eventually (Progress) ◦ if a sequential program terminates with a given input, we want that its parallel alternative also completes with the same input • Performance ◦ we want to exploit our parallel hardware Concurrent and parallel programming 7 Correctness conditions Progress conditions -
Shared Memory Programming Models I
Shared Memory Programming Models I Stefan Lang Interdisciplinary Center for Scientific Computing (IWR) University of Heidelberg INF 368, Room 532 D-69120 Heidelberg phone: 06221/54-8264 email: [email protected] WS 14/15 Stefan Lang (IWR) Simulation on High-Performance Computers WS14/15 1/45 Shared Memory Programming Models I Communication by shared memory Critical section Mutual exclusion: Petersons algorithm OpenMP Barriers – Synchronisation of all processes Semaphores Stefan Lang (IWR) Simulation on High-Performance Computers WS14/15 2/45 Critical Section What is a critical section? We consider the following situation: Application consists of P concurrent processes, these are thus executed simultaneously instructions executed by one process are subdivided into interconnected groups ◮ critical sections ◮ uncritical sections Critical section: Sequence of instructions, that perform a read or write access on a shared variable. Instructions of a critical section that may not be performed simultaneously by two or more processes. → it is said only a single process may reside within the critical section. Stefan Lang (IWR) Simulation on High-Performance Computers WS14/15 3/45 Mutual Exclusion I 2 Types of synchronization can be distinguished Conditional synchronisation Mutual exclusion Mutual exclusion consists of an entry protocol and an exit protocol: Programm (Introduction of mutual exclusion) parallel critical-section { process Π[int p ∈{0,..., P − 1}] { while (1) { entry protocol; critical section; exit protocol; uncritical section; } } } Stefan Lang (IWR) Simulation on High-Performance Computers WS14/15 4/45 Mutual Exclusion II The following criteria have to be matched: 1 Mutual exclusion. At most one process executed the critical section at a time. -
A Theory of Global Concurrency Control in Multidatabase Systems
VLDB Journal,2, 331-360 (1993), Michael Carey and Patrick Valduriez, Editors 331 t~)VLDB A Theory of Global Concurrency Control in Multidatabase Systems Aidong Zhang and Ahmed K. Elmagarmid Received December 1, 1992; revised version received February 1, 1992; accepted March 15, 1993. Abstract. This article presents a theoretical basis for global concurrency control to maintain global serializability in multidatabase systems. Three correctness criteria are formulated that utilize the intrinsic characteristics of global transactions to de- termine the serialization order of global subtransactions at each local site. In par- ticular, two new types of serializability, chain-conflicting serializability and shar- ing serializability, are proposed and hybrid serializability, which combines these two basic criteria, is discussed. These criteria offer the advantage of imposing no restrictions on local sites other than local serializability while retaining global se- rializability. The graph testing techniques of the three criteria are provided as guidance for global transaction scheduling. In addition, an optimal property of global transactions for determinating the serialization order of global subtransac- tions at local sites is formulated. This property defines the upper limit on global serializability in multidatabase systems. Key Words. Chain-conflicting serializability, sharing serializability, hybrid serial- izability, optimality. 1. Introduction Centralized databases were predominant during the 1970s, a period which saw the development of diverse database systems based on relational, hierarchical, and network models. The advent of applications involving increased cooperation between systems necessitated the development of methods for integrating these pre-existing database systems. The design of such global database systems must allow unified access to these diverse database systems without subjecting them to conversion or major modifications.