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In computer science, software is typically divided into two types: high-level end-user applications software (such as word processors, databases, video games, etc.), and low-level systems software (such as operating systems, hardware drivers, firmwares, etc.). As such, high-level applications typically rely on low-level applications to function.
Python is a high-level, general-purpose programming language.Its design philosophy emphasizes code readability with the use of significant indentation. [33]Python is dynamically type-checked and garbage-collected.
A high-level programming language is a programming language with strong abstraction from the details of the computer.In contrast to low-level programming languages, it may use natural language elements, be easier to use, or may automate (or even hide entirely) significant areas of computing systems (e.g. memory management), making the process of developing a program simpler and more ...
A programming paradigm is a relatively high-level way to conceptualize and structure the implementation of a computer program. A programming language can be classified as supporting one or more paradigms. [1] Paradigms are separated along and described by different dimensions of programming.
Machine code is the form in which code that can be directly executed is stored on a computer. It consists of machine language instructions, stored in memory, that perform operations such as moving values in and out of memory locations, arithmetic and Boolean logic, and testing values and, based on the test, either executing the next instruction in memory or executing an instruction at another ...
High-level synthesis (HLS), sometimes referred to as C synthesis, electronic system-level (ESL) synthesis, algorithmic synthesis, or behavioral synthesis, is an automated design process that takes an abstract behavioral specification of a digital system and finds a register-transfer level structure that realizes the given behavior.
The Computer Language Benchmarks Game site warns against over-generalizing from benchmark data, but contains a large number of micro-benchmarks of reader-contributed code snippets, with an interface that generates various charts and tables comparing specific programming languages and types of tests.
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. It also exposes low-level APIs that help programmers run custom algorithms in parallel.
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