{"doi":"10.1145/3295500.3356222","type":"conference","article_number":"a11","conference":{"name":"SC: Conference for High Performance Computing, Networking, Storage and Analysis","end_date":"2019-11-19","start_date":"2019-11-17","location":"Denver, CO, Unites States"},"author":[{"first_name":"Cedric","last_name":"Renggli","full_name":"Renggli, Cedric"},{"id":"0D0A9058-257B-11EA-A937-9341C3D8BC8A","full_name":"Ashkboos, Saleh","last_name":"Ashkboos","first_name":"Saleh"},{"full_name":"Aghagolzadeh, Mehdi","first_name":"Mehdi","last_name":"Aghagolzadeh"},{"last_name":"Alistarh","first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian"},{"full_name":"Hoefler, Torsten","last_name":"Hoefler","first_name":"Torsten"}],"external_id":{"isi":["000545976800011"],"arxiv":["1802.08021"]},"status":"public","date_updated":"2023-09-06T14:37:55Z","oa_version":"Preprint","year":"2019","ec_funded":1,"month":"11","publication_identifier":{"eissn":["21674337"],"issn":["21674329"],"isbn":["9781450362290"]},"project":[{"call_identifier":"H2020","name":"Elastic Coordination for Scalable Machine Learning","grant_number":"805223","_id":"268A44D6-B435-11E9-9278-68D0E5697425"}],"main_file_link":[{"url":"https://arxiv.org/abs/1802.08021","open_access":"1"}],"quality_controlled":"1","title":"SparCML: High-performance sparse communication for machine learning","date_published":"2019-11-17T00:00:00Z","citation":{"ieee":"C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, and T. Hoefler, “SparCML: High-performance sparse communication for machine learning,” in <i>International Conference for High Performance Computing, Networking, Storage and Analysis, SC</i>, Denver, CO, Unites States, 2019.","ama":"Renggli C, Ashkboos S, Aghagolzadeh M, Alistarh D-A, Hoefler T. SparCML: High-performance sparse communication for machine learning. In: <i>International Conference for High Performance Computing, Networking, Storage and Analysis, SC</i>. ACM; 2019. doi:<a href=\"https://doi.org/10.1145/3295500.3356222\">10.1145/3295500.3356222</a>","mla":"Renggli, Cedric, et al. “SparCML: High-Performance Sparse Communication for Machine Learning.” <i>International Conference for High Performance Computing, Networking, Storage and Analysis, SC</i>, a11, ACM, 2019, doi:<a href=\"https://doi.org/10.1145/3295500.3356222\">10.1145/3295500.3356222</a>.","short":"C. Renggli, S. Ashkboos, M. Aghagolzadeh, D.-A. Alistarh, T. Hoefler, in:, International Conference for High Performance Computing, Networking, Storage and Analysis, SC, ACM, 2019.","apa":"Renggli, C., Ashkboos, S., Aghagolzadeh, M., Alistarh, D.-A., &#38; Hoefler, T. (2019). SparCML: High-performance sparse communication for machine learning. In <i>International Conference for High Performance Computing, Networking, Storage and Analysis, SC</i>. Denver, CO, Unites States: ACM. <a href=\"https://doi.org/10.1145/3295500.3356222\">https://doi.org/10.1145/3295500.3356222</a>","ista":"Renggli C, Ashkboos S, Aghagolzadeh M, Alistarh D-A, Hoefler T. 2019. SparCML: High-performance sparse communication for machine learning. International Conference for High Performance Computing, Networking, Storage and Analysis, SC. SC: Conference for High Performance Computing, Networking, Storage and Analysis, a11.","chicago":"Renggli, Cedric, Saleh Ashkboos, Mehdi Aghagolzadeh, Dan-Adrian Alistarh, and Torsten Hoefler. “SparCML: High-Performance Sparse Communication for Machine Learning.” In <i>International Conference for High Performance Computing, Networking, Storage and Analysis, SC</i>. ACM, 2019. <a href=\"https://doi.org/10.1145/3295500.3356222\">https://doi.org/10.1145/3295500.3356222</a>."},"_id":"7201","scopus_import":"1","oa":1,"language":[{"iso":"eng"}],"publication":"International Conference for High Performance Computing, Networking, Storage and Analysis, SC","publication_status":"published","date_created":"2019-12-22T23:00:42Z","abstract":[{"text":"Applying machine learning techniques to the quickly growing data in science and industry requires highly-scalable algorithms. Large datasets are most commonly processed \"data parallel\" distributed across many nodes. Each node's contribution to the overall gradient is summed using a global allreduce. This allreduce is the single communication and thus scalability bottleneck for most machine learning workloads. We observe that frequently, many gradient values are (close to) zero, leading to sparse of sparsifyable communications. To exploit this insight, we analyze, design, and implement a set of communication-efficient protocols for sparse input data, in conjunction with efficient machine learning algorithms which can leverage these primitives. Our communication protocols generalize standard collective operations, by allowing processes to contribute arbitrary sparse input data vectors. Our generic communication library, SparCML1, extends MPI to support additional features, such as non-blocking (asynchronous) operations and low-precision data representations. As such, SparCML and its techniques will form the basis of future highly-scalable machine learning frameworks.","lang":"eng"}],"article_processing_charge":"No","isi":1,"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","department":[{"_id":"DaAl"}],"day":"17","publisher":"ACM"}