commit | 045f9e0f62f5f9df0d997d8bbd8496eea5d141f4 | [log] [tgz] |
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author | Matthias Boehm <mboehm7@gmail.com> | Wed Apr 03 19:50:35 2024 +0200 |
committer | Matthias Boehm <mboehm7@gmail.com> | Wed Apr 03 19:53:28 2024 +0200 |
tree | f87a845d6c299485030fa6cc1da12cd3dbf80e7c | |
parent | e910c718d9dd520cbbc510b8228336d6fad893ed [diff] |
[SYSTEMDS-3691] Multi-threaded dot-product matrix multiplication So far, dense dot-product where always executed in a single-threaded manner despite going through the multi-threaded code path because only a single task was created (single row in lhs matrix). We now use the existing logic for parallelizing over the common dimension instead. For pageRank on the europe_osm road network graph, the involved dot product (1x50912018 mmult 50912018x1) improved from ~50ms to 7ms on a machine with 24 pcores / 48 vcores.
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