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We are very grateful to all our colleagues and users for providing valuable feedback or contributing to the project. You are warmly welcome to join this community-driven collaborative effort to help automate code, compiler and architecture design and optimization, boost innovation and research.

Note: cTuning is an ongoing evolving project - please be patient and tolerant to the community and help us with this collaborative effort!

Typical non-trivial distribution of optimization points in the 2D space of speedups vs code size of a susan_corners program on AMD Athlon64 3700+ architecture with GCC 4.2.2 during automatic program optimization using ccc-run-glob-flags-rnd-uniform plugin from CCC framework with uniform random combinations of more than 100 global compiler flags (each flag has 50% probability to be selected for a given combination of optimizations). Similar data for other benchmark, datasets and architectures is available in the Collective Optimization Database. cTuning technology helps users find or predict optimial optimization points in such complex spaces using "one-button" approach.

We are participating in the following collaborative activities:

• Develop common open-source tools with unified APIs (universal compilers adaptable to any heterogeneous multi-core architecture, computer architecture simulators, adaptive run-time systems) to optimize programs and architectures collectively using iterative compilation, statistical and machine learning techniques.
  More information is available at cTools page.
• Share interesting optimization cases from the community for programs/libraries/OS (compiler optimizations/architecture configurations to improve execution time, code size, architecture size, power consumption, etc) in the Collective Optimization Database to help users optimize their systems, enable replicable collaborative research and enable further analysis using statistical and machine learning techniques.
  
  More information is available at cDatabase page.
• Enable collaborative research using cTools to automate and simplify the process of developing and optimizing new computer architectures, compilers, operating systems and programming environments using statistical analysis, machine learning, dynamic adaptation and bio-inspired techniques. We believe that our adaptive approaches are critical to overcome the complexity of computing systems and improve their performance, power consumption, system size and fault-tolerance automatically while reducing their cost and time to market.
  
  More information is available at cResearch page.