commit | af4f3d7683cf5dcd62d45858fb0290a607e66dfe | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Feb 13 01:31:57 2021 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Feb 13 01:31:57 2021 +0100 |
tree | fb3c19b3ca74cb15bff1fcd342b2f1e62b521671 | |
parent | da6a209696baf1102e15c65e4968e8106313a6a5 [diff] |
[SYSTEMDS-2856] Extended multi-threading binary and ternary operations This patch generalized the multi-threading of binary (sparse-unsafe) to binary (sparse-unsafe and sparse-safe matrix) and ternary operations, where the latter often calls binary sparse-safe matrix operations. For an mnist lenet parameter server scenario, this patch improved end-to-end performance from 205s to 168s. It also slightly improved other algorithms like KMeans.
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