| commit | ea2d971ec4ad3a0cf93fe78224a6f14176a6235b | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Sun May 31 18:26:55 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Sun May 31 18:26:55 2020 +0200 |
| tree | ae4fa176b7e55458203b654d6969e592aa968f96 | |
| parent | 405462e84ad1192e447bb09c03fe20d112bf6afb [diff] |
[SYSTEMDS-274] Fix compressed colMins/colMaxs w/ shared dictionary This patch fixes remaining issues of incorrect results for colMins and colMaxs over compressed matrix blocks with shared DDC1 dictionaries. Specifically, if the individual column groups have only partial overlap, the shared dictionary contains a superset of column group distinct values. Since aggregation functions like min and max are executed only over the dictionary (without touching the compressed data), it led to incorrect results as we find extreme values that do not actually exist in the column group. Three alternatives approaches could solve this: (1) drop shared dictionaries, (2) execute colMins and colMax over the compressed data, or (3) refactor the double array dictionary into a proper class hierarchy and maintain additional meta data for shared dictionaries. We decided for (3) in order to keep predictable performance, irrespective of shared dictionaries and because this class hierarchy allows for further improvements of shared dictionaries between any subsets of column groups. Additionally, this fix also cleanups incorrect estimates of the individual column groups (because getValueSize was used in the estimates as a number of values, although it gave the size in bytes) as well as some of the Class-layout size estimation tests. Closes #927.
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
Status and Build: SystemDS is still in pre-alpha status. The original code base was forked from Apache SystemML 1.2 in September 2018. We will continue to support linear algebra programs over matrices, while replacing the underlying data model and compiler, as well as substantially extending the supported functionalities. Until the first release, you can build your own snapshot via Apache Maven: mvn clean package -P distribution.
