commit | 0bf27eaad3ffb40da8e6bf87d42721f0a333ba19 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 23 18:13:32 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Jan 23 18:13:32 2021 +0100 |
tree | 2f629e3f9813c46b759a607a1c976b882cc0d13f | |
parent | 71385e1556a97e268acdb238709fe86bed5fe8af [diff] |
[SYSTEMDS-2802] Fix parfor handling of negative loop increments * Fix parfor dependency analysis for negative increments (via normalization) * Fix parfor runtime program block (determine number of iterations correctly, to prevent invalid early-abort) * Fix removed local debug flag for parfor dependency analysis * Tests for parfor dependency analysis and runtime predicates
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