commit | acd33e166b756cdf5884c89b735089f17ccfb2c6 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Nov 14 22:26:08 2020 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Nov 14 22:27:25 2020 +0100 |
tree | abe088165f907ad84daad98630bc0129db510cde | |
parent | 914b8f8966879c274ca30130a24d502c08f59b6c [diff] |
[SYSTEMDS-2630] Multi-threaded slicing in sliced federated broadcasts For the case of broadcasting a large, potentially sparse, matrix in a sliced manner (where every federated partition only received the needed data) so far we sliced the blocks sequentially. With this patch, we simply do this independent block slicing in a multi-threaded manner in order to avoid unnecessary overhead in case of large broadcasts.
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.
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Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source