commit | c83d2fcaed0c858b450c0fd730c494a2db2bd723 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Thu Feb 11 17:49:02 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Thu Feb 11 17:49:12 2021 +0100 |
tree | a4f9cb6918ca3c367095540abf49c64ec1690a8f | |
parent | 2d48bb5ffab38fba82d90c99dec81378f862eeff [diff] |
[SYSTEMDS-2857] Add missing federated unary matrix operations This patch adds support for unary matrix operations such as isNaN, round, ceil, floor, etc. So far, we only supported specific sub classes of unary instructions but not the general case. The mapping into federated operations is simple and only executes the given operation on all partitions (which assumes that the entire federated matrix is covered).
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.
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Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source