| commit | 64d5022df1c4a75931a072ef7ce73dc322a393f0 | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Sat Aug 15 15:09:35 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Sat Aug 15 15:10:07 2020 +0200 |
| tree | 1d280b3ac9eab78543e232bbf33de8418ce5e5d6 | |
| parent | 6029c07a3ddce313e27d914a9c7dffb53b48b4cc [diff] |
[MINOR] Performance replace operations (w/ pattern, replacement) As more and more pre-processing techniques use replace operations for robustness against NaN and other special values, this patch makes a performance improvement for special cases. So far we always allocated the output and copied values with on-the-fly replacement of the pattern and awareness for NaNs. Now, we first probe with (early abort) if the matrix contains the pattern and only if this is the case allocate and copy the values; otherwise the we simply return the input.
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 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.
