This course is the largest of the introductory programming courses and is one of the largest courses at Stanford. Topics focus on the introduction to the engineering of computer applications emphasizing modern software engineering principles: object-oriented design, decomposition, encapsulation, abstraction, and testing. Programming Methodology teaches the widely-used Java programming language along with good software engineering principles. Emphasis is on good programming style and the built-in facilities of the Java language. The course is explicitly designed to appeal to humanists and social scientists as well as hard-core techies. In fact, most Programming Methodology graduates end up majoring outside of the School of Engineering. Prerequisites: The course requires no previous background in programming, but does require considerable dedication and hard work.
Proponents of classifying computer science as a mathematical discipline argue that computer programs are physical realizations of mathematical entities and programs can be deductively reasoned through mathematical formal methods.[47] Computer scientists Edsger W. Dijkstra and Tony Hoare regard instructions for computer programs as mathematical sentences and interpret formal semantics for programming languages as mathematical axiomatic systems.[47]
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A number of computer scientists have argued for the distinction of three separate paradigms in computer science. Peter Wegner argued that those paradigms are science, technology, and mathematics.[48] Peter Denning's working group argued that they are theory, abstraction (modeling), and design.[49] Amnon H. Eden described them as the "rationalist paradigm" (which treats computer science as a branch of mathematics, which is prevalent in theoretical computer science, and mainly employs deductive reasoning), the "technocratic paradigm" (which might be found in engineering approaches, most prominently in software engineering), and the "scientific paradigm" (which approaches computer-related artifacts from the empirical perspective of natural sciences,[50] identifiable in some branches of artificial intelligence).[51]Computer science focuses on methods involved in design, specification, programming, verification, implementation and testing of human-made computing systems.[52]
The first GPUs were designed as graphics accelerators, becoming more programmable over the 90s, culminating in NVIDIA's first GPU in 1999. Researchers and scientists rapidly began to apply the excellent floating point performance of this GPU for general purpose computing. In 2003, a team of researchers led by Ian Buck unveiled Brook, the first widely adopted programming model to extend C with data-parallel constructs. Ian Buck later joined NVIDIA and led the launch of CUDA in 2006, the world's first solution for general-computing on GPUs.
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