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<h2 id="notable-usages">Notable usages</h2> <ul><li><a href="/facts/ARM_Holdings/jqqppj7b">ARM</a> uses CK to accelerate computer engineering<a class="footnote-ref" id="fnref:7" href="#fn:7"><sup>7</sup></a></li> <li>Several <a href="/facts/Association_for_Computing_Machinery/abI2atlP">ACM-sponsored</a> conferences use CK to automate the Artifact Evaluation process<a class="footnote-ref" id="fnref:8" href="#fn:8"><sup>8</sup></a><a class="footnote-ref" id="fnref:9" href="#fn:9"><sup>9</sup></a></li> <li><a href="/facts/Imperial_College_London/SoXBRyax">Imperial College (London)</a> uses CK to automate and <a href="/facts/Crowdsourcing/pIjlvb3E">crowdsource</a> <a href="/facts/Compiler/WNfCDFJe">compiler</a> bug detection<a class="footnote-ref" id="fnref:10" href="#fn:10"><sup>10</sup></a></li> <li>Researchers from the <a href="/facts/University_of_Cambridge/0la7aX9o">University of Cambridge</a> used CK to help the community reproduce results of their publication in the International Symposium on Code Generation and Optimization (CGO'17) during Artifact Evaluation<a class="footnote-ref" id="fnref:11" href="#fn:11"><sup>11</sup></a></li> <li><a href="/facts/General_Motors/rBgP3IRK">General Motors (USA)</a> uses CK to crowd-benchmark <a href="/facts/Convolutional_neural_network/kHEdnmGU">convolutional neural network</a> optimizations <a class="footnote-ref" id="fnref:12" href="#fn:12"><sup>12</sup></a><a class="footnote-ref" id="fnref:13" href="#fn:13"><sup>13</sup></a></li> <li>The <a href="/facts/Raspberry_Pi_Foundation/YPMvmFvB">Raspberry Pi Foundation</a> and the <a href="/facts/CTuning_foundation/lGShbR1E">cTuning foundation</a> released a CK workflow with a reproducible "live" paper to enable collaborative research into multi-objective autotuning and machine learning techniques<a class="footnote-ref" id="fnref:14" href="#fn:14"><sup>14</sup></a></li> <li><a href="/facts/IBM/QszA7nwd">IBM</a> uses CK to reproduce <a href="/facts/Quantum_computing/nbbcgmvq">quantum results</a> from nature<a class="footnote-ref" id="fnref:15" href="#fn:15"><sup>15</sup></a></li> <li>CK is used to automate MLPerf benchmark<a class="footnote-ref" id="fnref:16" href="#fn:16"><sup>16</sup></a><a class="footnote-ref" id="fnref:17" href="#fn:17"><sup>17</sup></a></li></ul> <h2 id="portable-package-manager-for-portable-workflows">Portable package manager for portable workflows</h2> <p>CK has an integrated cross-platform package manager with <a href="/facts/Python_(language)/YbuGqofa">Python</a> scripts, <a href="/facts/JSON/bs1rUAhd">JSON</a> API and <a href="/facts/JSON/bs1rUAhd">JSON</a> meta-description to automatically rebuild software environment on a user machine required to run a given research workflow.<a class="footnote-ref" id="fnref:18" href="#fn:18"><sup>18</sup></a> </p> <h2 id="reproducibility-of-experiments">Reproducibility of experiments</h2> <p>CK enables reproducibility of experimental results via community involvement similar to <a href="/facts/Wikipedia/La2xLBFt">Wikipedia</a> and <a href="/facts/Physics/a7aH2y08">physics</a>. Whenever a new workflow with all components is shared via GitHub, anyone can try it on a different machine, with different environment and using slightly different choices (compilers, libraries, data sets). Whenever an unexpected or wrong behavior is encountered, the community explains it, fixes components and shares them back as described in.<a class="footnote-ref" id="fnref:19" href="#fn:19"><sup>19</sup></a> </p> <h2 id="external-links">External links</h2> <ul><li>Development site: <a href="https://github.com/ctuning/ck">[1]</a></li> <li>Documentation: <a href="https://cKnowledge.io/docs">[2]</a></li> <li>Public repository with crowdsourced experiments: <a href="https://cknowledge.io/results">[3]</a></li> <li>International Workshop on Adaptive Self-tuning Computing System (ADAPT) uses CK to enable public reviewing of publications and artifacts via <a href="/facts/Reddit/PAJjm63I">Reddit</a>: <a href="http://adapt-workshop.org/motivation2016.html">[4]</a></li></ul> <h2 id="references">References</h2> <ol> <li id="fn:1"><p>Fursin, Grigori (29 March 2021). Collective Knowledge: organizing research projects as a database of reusable components and portable workflows with common APIs. Philosophical Transactions of the Royal Society. arXiv:2011.01149. doi:10.1098/rsta.2020.0211. <a href="/wiki/Grigori_Fursin" target="_blank">/wiki/Grigori_Fursin</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li> <li id="fn:2"><p>Reusable CK components and actions to automate common research tasks <a href="https://cKnowledge.io/actions" target="_blank">https://cKnowledge.io/actions</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li> <li id="fn:3"><p>Fursin, Grigori (29 March 2021). Collective Knowledge: organizing research projects as a database of reusable components and portable workflows with common APIs. Philosophical Transactions of the Royal Society. arXiv:2011.01149. doi:10.1098/rsta.2020.0211. <a href="/wiki/Grigori_Fursin" target="_blank">/wiki/Grigori_Fursin</a> <a href="#fnref:3" class="footnote-back-ref">↩</a></p></li> <li id="fn:4"><p>Live paper with reproducible experiments to enable collaborative research into multi-objective autotuning and machine learning techniques <a href="https://cknowledge.io/report/rpi3-crowd-tuning-2017-interactive" target="_blank">https://cknowledge.io/report/rpi3-crowd-tuning-2017-interactive</a> <a href="#fnref:4" class="footnote-back-ref">↩</a></p></li> <li id="fn:5"><p>Online repository with reproduced results <a href="https://cKnowledge.io" target="_blank">https://cKnowledge.io</a> <a href="#fnref:5" class="footnote-back-ref">↩</a></p></li> <li id="fn:6"><p>Index of reproduced papers <a href="https://cKnowledge.io/reproduced-papers" target="_blank">https://cKnowledge.io/reproduced-papers</a> <a href="#fnref:6" class="footnote-back-ref">↩</a></p></li> <li id="fn:7"><p>Ed Plowman; Grigori Fursin, ARM TechCon'16 presentation "Know Your Workloads: Design more efficient systems!" <a href="https://github.com/ctuning/ck/wiki/Demo-ARM-TechCon'16" target="_blank">https://github.com/ctuning/ck/wiki/Demo-ARM-TechCon'16</a> <a href="#fnref:7" class="footnote-back-ref">↩</a></p></li> <li id="fn:8"><p>Artifact Evaluation for systems and machine learning conferences <a href="http://cTuning.org/ae" target="_blank">http://cTuning.org/ae</a> <a href="#fnref:8" class="footnote-back-ref">↩</a></p></li> <li id="fn:9"><p>ACM TechTalk about reproducing 150 research papers and testing them in the real world <a href="https://learning.acm.org/techtalks/reproducibility" target="_blank">https://learning.acm.org/techtalks/reproducibility</a> <a href="#fnref:9" class="footnote-back-ref">↩</a></p></li> <li id="fn:10"><p>EU TETRACOM project to combine CK and CLSmith (PDF), archived from the original (PDF) on 2017-03-05, retrieved 2016-09-15 <a href="https://web.archive.org/web/20170305003204/http://es.iet.unipi.it/tetracom/content/uploads/Posters/TTP35.pdf" target="_blank">https://web.archive.org/web/20170305003204/http://es.iet.unipi.it/tetracom/content/uploads/Posters/TTP35.pdf</a> <a href="#fnref:10" class="footnote-back-ref">↩</a></p></li> <li id="fn:11"><p>Artifact Evaluation Reproduction for "Software Prefetching for Indirect Memory Accesses", CGO 2017, using CK, 16 October 2022 <a href="https://github.com/SamAinsworth/reproduce-cgo2017-paper" target="_blank">https://github.com/SamAinsworth/reproduce-cgo2017-paper</a> <a href="#fnref:11" class="footnote-back-ref">↩</a></p></li> <li id="fn:12"><p>GitHub development website for CK-powered Caffe, 11 October 2022 <a href="https://github.com/dividiti/ck-caffe" target="_blank">https://github.com/dividiti/ck-caffe</a> <a href="#fnref:12" class="footnote-back-ref">↩</a></p></li> <li id="fn:13"><p>Open-source Android application to let the community participate in collaborative benchmarking and optimization of various DNN libraries and models <a href="http://cknowledge.org/android-apps.html" target="_blank">http://cknowledge.org/android-apps.html</a> <a href="#fnref:13" class="footnote-back-ref">↩</a></p></li> <li id="fn:14"><p>Live paper with reproducible experiments to enable collaborative research into multi-objective autotuning and machine learning techniques <a href="https://cknowledge.io/report/rpi3-crowd-tuning-2017-interactive" target="_blank">https://cknowledge.io/report/rpi3-crowd-tuning-2017-interactive</a> <a href="#fnref:14" class="footnote-back-ref">↩</a></p></li> <li id="fn:15"><p>Reproducing quantum results from nature – how hard could it be? <a href="https://www.linkedin.com/pulse/reproducing-quantum-results-from-nature-how-hard-could-lickorish" target="_blank">https://www.linkedin.com/pulse/reproducing-quantum-results-from-nature-how-hard-could-lickorish</a> <a href="#fnref:15" class="footnote-back-ref">↩</a></p></li> <li id="fn:16"><p>MLPerf crowd-benchmarking <a href="https://cknowledge.io/c/solution/demo-obj-detection-coco-tf-cpu-benchmark-linux-portable-workflows" target="_blank">https://cknowledge.io/c/solution/demo-obj-detection-coco-tf-cpu-benchmark-linux-portable-workflows</a> <a href="#fnref:16" class="footnote-back-ref">↩</a></p></li> <li id="fn:17"><p>MLPerf inference benchmark automation guide, 17 October 2022 <a href="https://github.com/mlcommons/ck/tree/master/ck/docs/mlperf-automation" target="_blank">https://github.com/mlcommons/ck/tree/master/ck/docs/mlperf-automation</a> <a href="#fnref:17" class="footnote-back-ref">↩</a></p></li> <li id="fn:18"><p>List of shared CK packages <a href="https://cKnowledge.io/packages" target="_blank">https://cKnowledge.io/packages</a> <a href="#fnref:18" class="footnote-back-ref">↩</a></p></li> <li id="fn:19"><p>Live paper with reproducible experiments to enable collaborative research into multi-objective autotuning and machine learning techniques <a href="https://cknowledge.io/report/rpi3-crowd-tuning-2017-interactive" target="_blank">https://cknowledge.io/report/rpi3-crowd-tuning-2017-interactive</a> <a href="#fnref:19" class="footnote-back-ref">↩</a></p></li> </ol>