| commit | 30d5c408b900b1aa4c8ddeb2f1264b830f460a05 | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Thu May 14 18:20:17 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Thu May 14 18:20:17 2020 +0200 |
| tree | 8710559314c414ba2997f2ab0696c676361cbbc8 | |
| parent | 996f61281c45a428986a89bc14c982ad41af0382 [diff] |
[MINOR] Avoid unnecessary overhead in createvar instructions This patch makes a minor performance improvement to the createvar instruction execution (which happens for every non-scalar operator). In detail, the need for creating unique file names (from one instruction), led to unnecessary string concatenation and thus object allocation. We now reuse the existing thread-local string builders as used for instruction generation. On a special-case scenario with ~1M loop iterations over tiny data (100 values), this patch improved the createvar overhead from 22.1s to 5.6s (and overall from 49s to 33s).
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
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.
