MALT

Hao Li, Asim Kadav, Erik Kruus, Cristian Ungureanu
2015 Proceedings of the Tenth European Conference on Computer Systems - EuroSys '15  
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
more » ... ed RDMA, limiting data movement costs during incremental model updates. MALT allows machine learning developers to specify the dataflow and apply communication and representation optimizations. Through its general-purpose API, MALT can be used to provide data-parallelism to existing ML applications written in C++ and Lua and based on SVM, matrix factorization and neural networks. In our results, we show MALT provides fault tolerance, network efficiency and speedup to these applications.
doi:10.1145/2741948.2741965 dblp:conf/eurosys/LiKKU15 fatcat:vczbxlmkm5gtdlp6gisf5ingby