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Plow is an HTTP(S) benchmarking tool, written in Golang. It uses excellent fast HTTP instead of Go's default net/http due to its lightning-fast performance. Plow runs at specified connections (option -c) concurrently and real-time records summary statistics, histogram of execution time and calculates percentiles to display on Web UI and terminal. It can run for a set duration( option -d), for a fixed number of requests(option -n), or until Ctrl-C is interrupted. The implementation of...

Likwid is a simple to install and use toolsuite of command line applications and a library for performance oriented programmers. It works for Intel, AMD, ARMv8 and POWER9 processors on the Linux operating system. There is additional support for Nvidia and AMD GPUs. There is support for ARMv7 and POWER8/9 but there is currently no test machine in our hands to test them properly.

The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the...

In 2015, we converted cBench to open Collective Knowledge Format (CK) and moved it here: http://github.com/ctuning/ctuning-programs CK allows you to simplify compilation and execution of these benchmarks across multiple platforms (Windows, Linux, MacOS, Android ...). Collective Benchmark (cBench) is a collection/extension of open-source programs and multiple datasets to enable realistic benchmarking and research on program and architecture optimization.

In 2015, we converted MiDataSets and cDataSets to open Collective Knowledge Format (CK) and moved them here: http://github.com/ctuning/ctuning-datasets-min CK allows you to simplify compilation and execution of these benchmarks across multiple platforms (Windows, Linux, MacOS, Android ...). In this project, we collect multiple datasets for a MiBench benchmark (free, commercially representative embedded benchmark suite) to create the conditions for a more realistic benchmarking of different computing systems.