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This PR contains an addition to slice compressed, that allows the slices
not to decompress the entire matrix, but only the sliced parts.
The implementation handle the following cases:

- single value slice, is replaced by a getValue() that is placed into
a new matrix output.
- Row slices, that only decompress the rows selected
- Column slices, that maintain compressed outputs.
- Selective row/col slices that leverage first the column slice followed
 by a decompressing row slice.

A further improvement would be to maintain compression if the Row slice
is large enough to do so. But this would require further work.

Closes #1173

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Apache SystemDS

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

Build Documentation Component Test Application Test Function Test Python Test Federated Python Test