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In mathematics, particularly graph theory, and computer science, a directed acyclic graph (DAG) is a directed graph with no directed cycles. That is, it consists of vertices and edges (also called arcs ), with each edge directed from one vertex to another, such that following those directions will never form a closed loop.
Pig Latin script describes a directed acyclic graph (DAG) rather than a pipeline. [11] Pig Latin's ability to include user code at any point in the pipeline is useful for pipeline development. If SQL is used, data must first be imported into the database, and then the cleansing and transformation process can begin. [11]
A directed acyclic graph (DAG) is created on the variable uses and definitions. The DAG specifies a data dependency among assignment statements, as well as a partial order (therefore parallelism among statements). When statement is reached, there is a list of live variable assignments.
A data structure known as a hash table.. In computer science, a data structure is a data organization and storage format that is usually chosen for efficient access to data. [1] [2] [3] More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data, [4] i.e., it is an algebraic structure about data.
The ROSE compiler framework, developed at Lawrence Livermore National Laboratory (LLNL), is an open-source software compiler infrastructure to generate source-to-source analyzers and translators for multiple source languages including C (C89, C99, Unified Parallel C (UPC)), C++ (C++98, C++11), Fortran (77, 95, 2003), OpenMP, Java, Python, and PHP.
Many graph-based data structures are used in computer science and related fields: Graph; Adjacency list; Adjacency matrix; Graph-structured stack; Scene graph; Decision tree. Binary decision diagram; Zero-suppressed decision diagram; And-inverter graph; Directed graph; Directed acyclic graph; Propositional directed acyclic graph; Multigraph ...
The NIST Dictionary of Algorithms and Data Structures [1] is a reference work maintained by the U.S. National Institute of Standards and Technology. It defines a large number of terms relating to algorithms and data structures. For algorithms and data structures not necessarily mentioned here, see list of algorithms and list of data structures.
In general, a Java programmer does not need to understand Java bytecode or even be aware of it. However, as suggested in the IBM developerWorks journal, "Understanding bytecode and what bytecode is likely to be generated by a Java compiler helps the Java programmer in the same way that knowledge of assembly helps the C or C++ programmer."