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It is crucial for performance to fit the data into single-node or distributed main memory and enable very fast matrix-vector operations on in-memory data. Generalpurpose, heavy- and lightweight compression techniques struggle to achieve both good compression ratios and fast decompression speed to enable block-wise uncompressed operations. Compressed linear algebra (CLA) avoids these problems by applying lightweight lossless database compression techniques to matrices and then executing linear algebra operations such as matrix-vector multiplication directly on the compressed representations. The key ingredients are effective column compression schemes, cache-conscious operations, and an efficient sampling-based compression algorithm. Experiments on an initial implementation in SystemML show in-memory operations performance close to the uncompressed case and good compression ratios.We thereby obtain significant end-to-end performance improvements up to 26x or reduced memory requirements.<\/jats:p>","DOI":"10.1145\/3093754.3093765","type":"journal-article","created":{"date-parts":[[2017,5,15]],"date-time":"2017-05-15T12:13:58Z","timestamp":1494850438000},"page":"42-49","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Scaling Machine Learning via Compressed Linear Algebra"],"prefix":"10.1145","volume":"46","author":[{"given":"Ahmed","family":"Elgohary","sequence":"first","affiliation":[{"name":"University of Maryland, College Park, MD, USA"}]},{"given":"Matthias","family":"Boehm","sequence":"additional","affiliation":[{"name":"IBM Research - Almaden; San Jose, CA, USA"}]},{"given":"Peter J.","family":"Haas","sequence":"additional","affiliation":[{"name":"IBM Research - Almaden; San Jose, CA, USA"}]},{"given":"Frederick R.","family":"Reiss","sequence":"additional","affiliation":[{"name":"IBM Research - Almaden; San Jose, CA, USA"}]},{"given":"Berthold","family":"Reinwald","sequence":"additional","affiliation":[{"name":"IBM Research - Almaden; San Jose, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2017,5,12]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"OSDI","author":"Abadi M.","year":"2016","unstructured":"M. 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