| commit | 9bd68ffc5d211583a2ebcfe5be514abf4cc29b69 | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Wed Apr 15 21:46:16 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Wed Apr 15 21:47:41 2020 +0200 |
| tree | 7c30503fa85ac268c61ab5c767004f63c8ca32e8 | |
| parent | b9114b90a180d79e4ab6de7647e3a35cd7ce3f78 [diff] |
[SYSTEMDS-233] Fix multi-level lineage caching (parfor, determinism) This patch fixes some issues with multi-level lineage caching in parfor, specifically (1) to allow function reuse despite differently named parfor worker functions, and (2) the check for deterministic function results incorrectly probed too far and thus missing opportunities. However, down the road we should add an IPA pass which determines once for all functions if they are deterministic and pass this information down to the runtime, in order to avoid scenarios where threads are already blocking on placeholders that are later removed due to non-deterministic functions.
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.
Documentation: 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 -DskipTests clean package.
