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Software and hardware co-design and optimization of HPC systems has becomeintolerably complex, ad-hoc, time consuming and error prone due to enormousnumber of available design and optimization choices, complex interactionsbetween all software and hardware components, and multiple strict requirementsplaced on performance, power consumption, size, reliability and cost. Wepresent our novel long-term holistic and practical solution to this problembased on customizable, plugin-based, schema-free, heterogeneous, open-sourceCollective Mind repository and infrastructure with unified web interfaces andon-line advise system. This collaborative framework distributes analysis andmulti-objective off-line and on-line auto-tuning of computer systems among manyparticipants while utilizing any available smart phone, tablet, laptop, clusteror data center, and continuously observing, classifying and modeling theirrealistic behavior. Any unexpected behavior is analyzed using shared datamining and predictive modeling plugins or exposed to the community atcTuning.org for collaborative explanation, top-down complexity reduction,incremental problem decomposition and detection of correlating program,architecture or run-time properties (features). Gradually increasingoptimization knowledge helps to continuously improve optimization heuristics ofany compiler, predict optimizations for new programs or suggest efficientrun-time (online) tuning and adaptation strategies depending on end-userrequirements. We decided to share all our past research artifacts includinghundreds of codelets, numerical applications, data sets, models, universalexperimental analysis and auto-tuning pipelines, self-tuning machine learningbased meta compiler, and unified statistical analysis and machine learningplugins in a public repository to initiate systematic, reproducible andcollaborative research, development and experimentation with a new publicationmodel where experiments and techniques are validated, ranked and improved bythe community.

Collective Mind: cleaning up the research and experimentation mess in computer engineering using crowdsourcing, big data and machine learning