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Distributed computing. Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers. [1][2] The components of a distributed system communicate and coordinate their actions by passing messages to one another in ...
Dask is an open-source Python library for parallel computing. Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.
Spark Core is the foundation of the overall project. It provides distributed task dispatching, scheduling, and basic I/O functionalities, exposed through an application programming interface (for Java, Python, Scala, .NET [16] and R) centered on the RDD abstraction (the Java API is available for other JVM languages, but is also usable for some other non-JVM languages that can connect to the ...
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.
In computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views streams, or sequences of events in time, as the central input and output objects of computation. Stream processing encompasses dataflow programming, reactive programming ...
MapReduce is a programming model and an associated implementation for processing and generating big data sets with a parallel and distributed algorithm on a cluster. [1] [2] [3]A MapReduce program is composed of a map procedure, which performs filtering and sorting (such as sorting students by first name into queues, one queue for each name), and a reduce method, which performs a summary ...
RPyC (pronounced are-pie-see), or R emote Py thon C all, is a Python library for remote procedure calls (RPC), as well as distributed computing. Unlike regular RPC mechanisms, such as ONC RPC, CORBA or Java RMI, RPyC is transparent, symmetric, and requires no special decoration or definition languages. Moreover, it provides programmatic access ...
In distributed computing, distributed objects[citation needed] are objects (in the sense of object-oriented programming) that are distributed across different address spaces, either in different processes on the same computer, or even in multiple computers connected via a network, but which work together by sharing data and invoking methods.