commit | 22375bbb4c6f2efcbcce43db12de37fdd406b77c | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Fri Nov 06 14:29:43 2020 +0100 |
committer | baunsgaard <baunsgaard@tugraz.at> | Fri Nov 06 23:46:34 2020 +0100 |
tree | d99329efc06e68ca46ed260e645ec36b3791b711 | |
parent | e2414e039d64df6e0d547e4902a9c9770aed5b5d [diff] |
[SYSTEMDS-2718] Matrix Mult Accelerator Comparison This commit initialize the performance test suite, with a micro benchmark comparing the performance of MKL and default matrix multiplication. The construction allows easy execution on different hardware platforms to check FP-OPS, to see theoretical throughput compared to hardware specification. Closes #1095
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