A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf
.
Machine learning methods, such as SVM and neural networks, often improve their accuracy by using models with more parameters trained on large numbers of examples. Building such models on a single machine is often impractical because of the large amount of computation required. We introduce MALT, a machine learning library that integrates with existing machine learning software and provides data parallel machine learning. MALT provides abstractions for fine-grained in-memory updates using
doi:10.1145/2741948.2741965
dblp:conf/eurosys/LiKKU15
fatcat:vczbxlmkm5gtdlp6gisf5ingby