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Concurrent Data Structure
In computer science, a concurrent data structure is a particular way of storing and organizing data for access by multiple computing threads (or processes) on a computer. Historically, such data structures were used on uniprocessor machines with operating systems that supported multiple computing threads (or processes). The term concurrency captured the multiplexing/interleaving of the threads' operations on the data by the operating system, even though the processors never issued two operations that accessed the data simultaneously. Today, as multiprocessor computer architectures that provide parallelism become the dominant computing platform (through the proliferation of multi-core processors), the term has come to stand mainly for data structures that can be accessed by multiple threads which may actually access the data simultaneously because they run on different processors that communicate with one another. The concurrent data structure (sometimes also called a ''s ...
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Computer Science
Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to Applied science, practical disciplines (including the design and implementation of Computer architecture, hardware and Computer programming, software). Computer science is generally considered an area of research, academic research and distinct from computer programming. Algorithms and data structures are central to computer science. The theory of computation concerns abstract models of computation and general classes of computational problem, problems that can be solved using them. The fields of cryptography and computer security involve studying the means for secure communication and for preventing Vulnerability (computing), security vulnerabilities. Computer graphics (computer science), Computer graphics and computational geometry address the generation of images. Progr ...
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Java Concurrency
The Java programming language and the Java virtual machine (JVM) have been designed to support concurrent programming, and all execution takes place in the context of threads. Objects and resources can be accessed by many separate threads; each thread has its own path of execution but can potentially access any object in the program. The programmer must ensure read and write access to objects is properly coordinated (or " synchronized") between threads. Thread synchronization ensures that objects are modified by only one thread at a time and that threads are prevented from accessing partially updated objects during modification by another thread. The Java language has built-in constructs to support this coordination. Processes and threads Most implementations of the Java virtual machine run as a single process and in the Java programming language, concurrent programming is mostly concerned with threads (also called lightweight processes). Multiple processes can only be realize ...
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Nancy Lynch
Nancy Ann Lynch (born January 19, 1948) is a mathematician, a theorist, and a professor at the Massachusetts Institute of Technology. She is the NEC Professor of Software Science and Engineering in the EECS department and heads the "Theory of Distributed Systems" research group at MIT's Computer Science and Artificial Intelligence Laboratory. Education and early life Lynch was born in Brooklyn, and her academic training was in mathematics. She attended Brooklyn College and MIT, where she received her Ph.D. in 1972 under the supervision of Albert R. Meyer. Work She served on the math and computer science faculty at several other universities, including Tufts University, the University of Southern California, Florida International University, and the Georgia Institute of Technology (Georgia Tech), prior to joining the MIT faculty in 1982. Since then, she has been working on applying mathematics to the tasks of understanding and constructing complex distributed systems. Her 1985 ...
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Java ConcurrentMap
The Java programming language's Java Collections Framework version 1.5 and later defines and implements the original regular single-threaded Maps, and also new thread-safe Maps implementing the interface among other concurrent interfaces. In Java 1.6, the interface was added, extending , and the interface was added as a subinterface combination. Java Map Interfaces The version 1.8 Map interface diagram has the shape below. Sets can be considered sub-cases of corresponding Maps in which the values are always a particular constant which can be ignored, although the Set API uses corresponding but differently named methods. At the bottom is the java.util.concurrent.ConcurrentNavigableMap, which is a multiple-inheritance. * ** *** **** ***** *** **** Implementations ConcurrentHashMap For unordered access as defined in the java.util.Map interface, the java.util.concurrent.ConcurrentHashMap implements java.util.concurrent.ConcurrentMap. The mechanism is a hash access to ...
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Cache Coherence
In computer architecture, cache coherence is the uniformity of shared resource data that ends up stored in multiple local caches. When clients in a system maintain caches of a common memory resource, problems may arise with incoherent data, which is particularly the case with CPUs in a multiprocessing system. In the illustration on the right, consider both the clients have a cached copy of a particular memory block from a previous read. Suppose the client on the bottom updates/changes that memory block, the client on the top could be left with an invalid cache of memory without any notification of the change. Cache coherence is intended to manage such conflicts by maintaining a coherent view of the data values in multiple caches. Overview In a shared memory multiprocessor system with a separate cache memory for each processor, it is possible to have many copies of shared data: one copy in the main memory and one in the local cache of each processor that requested it. When on ...
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Gustafson's Law
In computer architecture, Gustafson's law (or Gustafson–Barsis's law) gives the speedup in the execution time of a task that theoretically gains from parallel computing, using a hypothetical run of ''the task'' on a single-core machine as the baseline. To put it another way, it is the theoretical "slowdown" of an ''already parallelized'' task if running on a serial machine. It is named after computer scientist John L. Gustafson and his colleague Edwin H. Barsis, and was presented in the article ''Reevaluating Amdahl's Law'' in 1988. Definition Gustafson estimated the speedup S of a program gained by using parallel computing as follows: : \begin S &= s + p \times N \\ &= s + (1 - s) \times N \\ &= N + (1 - N) \times s \end where * S is the theoretical speedup of the program with parallelism (scaled speedup); *N is the number of processors; * s and p are the fractions of time spent executing the serial parts and the parallel parts of the program on the ''parallel'' ...
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Amdahl's Law
In computer architecture, Amdahl's law (or Amdahl's argument) is a formula which gives the theoretical speedup in latency of the execution of a task at fixed workload that can be expected of a system whose resources are improved. It states that "the overall performance improvement gained by optimizing a single part of a system is limited by the fraction of time that the improved part is actually used". It is named after computer scientist Gene Amdahl, and was presented at the American Federation of Information Processing Societies (AFIPS) Spring Joint Computer Conference in 1967. Amdahl's law is often used in parallel computing to predict the theoretical speedup when using multiple processors. For example, if a program needs 20 hours to complete using a single thread, but a one-hour portion of the program cannot be parallelized, therefore only the remaining 19 hours' () execution time can be parallelized, then regardless of how many threads are devoted to a parallelized execution ...
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Speedup
In computer architecture, speedup is a number that measures the relative performance of two systems processing the same problem. More technically, it is the improvement in speed of execution of a task executed on two similar architectures with different resources. The notion of speedup was established by Amdahl's law, which was particularly focused on parallel processing. However, speedup can be used more generally to show the effect on performance after any resource enhancement. Definitions Speedup can be defined for two different types of quantities: '' latency'' and ''throughput''. ''Latency'' of an architecture is the reciprocal of the execution speed of a task: : L = \frac = \frac, where * ''v'' is the execution speed of the task; * ''T'' is the execution time of the task; * ''W'' is the execution workload of the task. ''Throughput'' of an architecture is the execution rate of a task: : Q = \rho vA = \frac = \frac, where * ''ρ'' is the execution density (e.g., the number ...
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Spinlock
In software engineering, a spinlock is a lock that causes a thread trying to acquire it to simply wait in a loop ("spin") while repeatedly checking whether the lock is available. Since the thread remains active but is not performing a useful task, the use of such a lock is a kind of busy waiting. Once acquired, spinlocks will usually be held until they are explicitly released, although in some implementations they may be automatically released if the thread being waited on (the one that holds the lock) blocks or "goes to sleep". Because they avoid overhead from operating system process rescheduling or context switching, spinlocks are efficient if threads are likely to be blocked for only short periods. For this reason, operating-system kernels often use spinlocks. However, spinlocks become wasteful if held for longer durations, as they may prevent other threads from running and require rescheduling. The longer a thread holds a lock, the greater the risk that the thread will be i ...
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Multiprocessing
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, mostly as a function of how CPUs are defined ( multiple cores on one die, multiple dies in one package, multiple packages in one system unit, etc.). According to some on-line dictionaries, a multiprocessor is a computer system having two or more processing units (multiple processors) each sharing main memory and peripherals, in order to simultaneously process programs. A 2009 textbook defined multiprocessor system similarly, but noting that the processors may share "some or all of the system’s memory and I/O facilities"; it also gave tightly coupled system as a synonymous term. At the operating system level, ''multiprocessing'' is som ...
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Synchronization (computer Science)
In computer science, synchronization refers to one of two distinct but related concepts: synchronization of processes, and synchronization of data. ''Process synchronization'' refers to the idea that multiple processes are to join up or handshake at a certain point, in order to reach an agreement or commit to a certain sequence of action. ''Data synchronization'' refers to the idea of keeping multiple copies of a dataset in coherence with one another, or to maintain data integrity. Process synchronization primitives are commonly used to implement data synchronization. The need for synchronization The need for synchronization does not arise merely in multi-processor systems but for any kind of concurrent processes; even in single processor systems. Mentioned below are some of the main needs for synchronization: '' Forks and Joins:'' When a job arrives at a fork point, it is split into N sub-jobs which are then serviced by n tasks. After being serviced, each sub-job waits until al ...
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Consensus (computer Science)
A fundamental problem in distributed computing and multi-agent systems is to achieve overall system reliability in the presence of a number of faulty processes. This often requires coordinating processes to reach consensus, or agree on some data value that is needed during computation. Example applications of consensus include agreeing on what transactions to commit to a database in which order, state machine replication, and atomic broadcasts. Real-world applications often requiring consensus include cloud computing, clock synchronization, PageRank, opinion formation, smart power grids, state estimation, control of UAVs (and multiple robots/agents in general), load balancing, blockchain, and others. Problem description The consensus problem requires agreement among a number of processes (or agents) for a single data value. Some of the processes (agents) may fail or be unreliable in other ways, so consensus protocols must be fault tolerant or resilient. The processes must someho ...
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