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It may not always be visible to the IT users, but developing and optimizing current and emerging computing systems using available technology is too time consuming and costly. cTuning.org is a community-driven collaborative wiki-based portal that brings together academia, industry and end-users to develop intelligent collective tuning technology that automates and simplifies compiler, program and architecture design and optimization. This technology minimizes repetitive time consuming tasks and human intervention using collective optimization, run-time adaptation, statistical and machine learning techniques. It can already help to improve performance, power consumption, reliability and other important characteristics of the available computing systems automatically (ranging from supercomputers to embedded systems) and should eventually enable development of the emerging intelligent self-tuning adaptive computing systems.

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 CTools: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). 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.

You are warmly welcome to use cTools for your R&D, browse and update cDatabase, provide feedback or join cTuning initiative to actively participate in this collaborative effort, extend cTools, cResearch and propose your new collaborative R&D projects for the cTuning community. We hope that this community effort will simplify and automate code and architecture design and optimization and boost innovation and research in these areas, provide novel adaptive mechanisms for heterogeneous, reconfigurable multi-core systems and emerging technologies such as cloud computing, reduce system development and optimization costs and and will eventually have a positive effect on science and industries that demand ever-increasing computing resources while placing strict requirements on systems.

General cTuning R&D directions are modestly moderated by our steering committee.

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