commit | 74200c66ad45b9b8f7e178c28eb3266354d351c4 | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Wed Nov 25 09:10:58 2020 +0100 |
committer | baunsgaard <baunsgaard@tugraz.at> | Tue Dec 01 16:42:28 2020 +0100 |
tree | 8ed00994642857296134e45c2ebe05445bf56aec | |
parent | 9641173dd54ba9d43e4869483006bfc4fc66897c [diff] |
[SYSTEMDS-2695 + 2743] CLA Row parallel left %*% This PR contains re-enabling parallel left multiplication for sparse matrices, plus row based parallelization of dense. Furthermore, it also contains optimization of Binary and scalar divide, that does not accidentally decompress anymore. Closes #1118
Overview: SystemDS is a versatile system for the end-to-end data science lifecycle from data integration, cleaning, and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this end, we aim to provide a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark. In contrast to existing systems - that either provide homogeneous tensors or 2D Datasets - and in order to serve the entire data science lifecycle, the underlying data model are DataTensors, i.e., tensors (multi-dimensional arrays) whose first dimension may have a heterogeneous and nested schema.
Quick Start Install, Quick Start and Hello World
Documentation: SystemDS Documentation
Python Documentation Python SystemDS Documentation
Issue Tracker Jira Dashboard
Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source