| commit | 0e369bd760528bec89be8a520a07345d108008df | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Wed Jul 01 22:13:57 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Wed Jul 01 22:13:57 2020 +0200 |
| tree | 43de0b8327023bde95f357df533286ff87160656 | |
| parent | f5789183bcfa0bf1edaa7bfc5e69e88d3fb9b49d [diff] |
[SYSTEMDS-418] Performance lineage tracing w/ probing (better hashing) This patch fixes an interesting performance bug caused by the recursive hash computation of lineage items. Due to repeated operation sequences (from loop iterations) and integer overflows during the hash computation, there were systematic hash sequence within one lineage DAG. This in turn lead to less pruning power on recursive equals computations, and collisions in the lineage cache, leading to even more recursive equals comparisons. The fix is simple. We now handle such overflows on hash aggregation (e.g., hash(int,int)) with a long instead of int hash function on demand. On the following test scenario for(i in 1:1000) X = ((X + X) * 2 - X) / 3 the previous runtime was 162s while with this patch it reduced to 0.244s. Even with 10K iterations, the runtime is still 1.1s, which suggests that any super-linear behavior has been eliminated.
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.
