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Various improvements to documentation
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docs/autotuning_methodology.rst

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autotuning\_methodology.baseline module
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Baseline module
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autotuning\_methodology.caching module
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Caching module
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autotuning\_methodology.curves module
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Curves module
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autotuning\_methodology.experiments module
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Experiments module
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.. automodule:: autotuning_methodology.experiments
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autotuning\_methodology.runner module
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Runner module
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.. automodule:: autotuning_methodology.runner
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autotuning\_methodology.searchspace\_statistics module
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Searchspace statistics module
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autotuning\_methodology.visualize\_experiments module
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Visualize experiments module
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.. inheritance-diagram:: src.autotuning_methodology.visualize_experiments
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docs/index.rst

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.. include:: getting_started.rst
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.. toctree::
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:caption: Contents:
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docs/modules.rst

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getting_started
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autotuning_methodology
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{
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"version": "0.1.2", // schema version
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"name": "Example", // experiment name
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"folder_id": "example", // folder ID to write to (must be unique)
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"kernels_path": "../cached_data_used/kernels", // path to the kernels
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"bruteforced_caches_path": "../cached_data_used/cachefiles", // path to the bruteforced cachefiles
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"visualization_caches_path": "../cached_data_used/visualizations", // path to the folder ID
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"kernels": [
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"convolution"
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], // list of kernels to evaluate with
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"GPUs": [
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"RTX_3090"
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], // list of GPUs to evaluate on
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"cutoff_percentile": 0.96, // percentile of distance between median and optimum at which to stop
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"objective_time_keys": [ // list of keys to use as time objective
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"compilation",
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"benchmark",
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"framework",
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"search_algorithm",
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"validation"
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],
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"objective_performance_keys": [ // list of keys to use as performance objective
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"time"
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],
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"plot": {
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"plot_x_value_types": [
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"aggregated"
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], // list of plot x-axis types
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"plot_y_value_types": [
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"baseline"
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], // list of plot y-axis types
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"confidence_level": 0.95 // confidence level for the error shade
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},
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"strategy_defaults": { // these defaults are applied to all strategies
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"record_data": [
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"time",
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"GFLOP/s"
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] // the data to record
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"stochastic": true, // whether the strategy has stochastic behaviour
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"iterations": 32, // number of times an individual configuration is repeated
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"repeats": 100, // number of times a strategy is repeated
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"minimum_number_of_evaluations": 20, // minimum number of non-error results to count as a single run
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},
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"strategies": [ // the list of strategies or algorithm to evaluate
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{
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"name": "genetic_algorithm", // the name of the strategy (for internal use, must be unique)
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"strategy": "genetic_algorithm", // the strategy as specified by the auto-tuning framework
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"display_name": "Genetic Algorithm" // the name of the strategy used in plots and other visualization
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},
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{
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"name": "ktt_profile_searcher",
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"strategy": "profile_searcher",
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"display_name": "KTT Profile Searcher"
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}
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]
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}

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