commit | 3f04b2fd21f33fd5281bae71bc5ca76298ec5169 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Sat Mar 23 20:01:46 2024 +0100 |
committer | Matthias Boehm <mboehm7@gmail.com> | Sat Mar 23 20:01:46 2024 +0100 |
tree | 3bb8ac679327aa5c6a22c5361b41edb34e9d131d | |
parent | 4750f63923cdff3abe8398c2d7d99192c1b18fbf [diff] |
[SYSTEMDS-3679] Multi-threaded contains-value operations This patch extends the compilation and runtime of contains-value parameterized builtin operations for multi-threading because it is called in a number of algorithms and primitives to ensure valid input data. For an 8GB dense input matrix (100 repetitions, tested on two-socket Xeon Gold 6338 w/ 128 vcore), this patch improved performance from 2.027s to 0.052s (which is > 150GB/s).
Overview: SystemDS is an open source ML 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.
Resource | Links |
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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