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The Computer Language Benchmarks Game (formerly called The Great Computer Language Shootout) is a free software project for comparing how a given subset of simple algorithms can be implemented in various popular programming languages. The project consists of: A set of very simple algorithmic problems
In applied mathematics, test functions, known as artificial landscapes, are useful to evaluate characteristics of optimization algorithms, such as convergence rate, precision, robustness and general performance.
Combo Benchmark Compare to Compete Online Benchmarking web-based database This web-based database is suitable for groups of competitors to benchmark individual performance against group performance. All process and performance benchmarks can be processed in this software, providing interesting analysis tools and complete benchmarking report ...
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. [55]
Performance profiler (sampled or instrumented) and analyzer, focused on game development. Proprietary Systemtap: Linux Programmable system tracing/probing tool; may be scripted to generate time- or performance-counter- or function-based profiles of the kernel and/or its userspace. Open source Valgrind: Linux, macOS, Solaris, Android
Given a system transforming a set of inputs to output values, described by a mathematical function f, optimization refers to the generation and selection of the best solution from some set of available alternatives, [1] by systematically choosing input values from within an allowed set, computing the value of the function, and recording the best value found during the process.
A convenient thin wrapper to Python is available via the highspy PyPI package. Although generally single-threaded, some solver components can utilize multi-core architectures. HiGHS is designed to solve large-scale models and exploits problem sparsity .
Given a transformation between input and output values, described by a mathematical function, optimization deals with generating and selecting the best solution from some set of available alternatives, by systematically choosing input values from within an allowed set, computing the output of the function and recording the best output values found during the process.