commit | 1967f8bb23109b2d3c6b0692fbcbf22324295594 | [log] [tgz] |
---|---|---|
author | arnabp <arnab.phani@tugraz.at> | Thu Nov 05 00:38:08 2020 +0100 |
committer | arnabp <arnab.phani@tugraz.at> | Thu Nov 05 00:47:48 2020 +0100 |
tree | 54903b3f8da8fb82305d0589b7bbac5203c596e6 | |
parent | 0bf553627a7f2a55da05f355ac45d3c10457a822 [diff] |
[MINOR] Improve lineage cache spilling This patch: - adds lineage tracing for frame indexing, - reduces starting computation time for spilling from 100 to 10ms Allowing more entries to be spilled to disk increases peroformance, and makes the difference between the policies smaller.
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