commit | 461ee22559106130ead12f8b5fc1fb2aa0c4f6f8 | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Sat Sep 12 00:38:43 2020 +0200 |
committer | baunsgaard <baunsgaard@tugraz.at> | Sun Oct 18 19:13:18 2020 +0200 |
tree | 97814cc0f63836c3714d94d0005060880aeee9ab | |
parent | 0ea51487153fc7bfa64d7fcd77c16fa300222030 [diff] |
[SYSTEMDS-2613-2614] Sparse & dense compressed MM This commit adds the sparse left and dense right matrix multiplication for CLA and LCLA. - Fix OLE and don't use skip indexes - Static Cost Partitioner
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