commit | 225d46859fbd0850dd0e8f65a867180110b74c60 | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Thu Jan 14 23:14:58 2021 +0100 |
committer | baunsgaard <baunsgaard@tugraz.at> | Fri Jan 15 10:34:01 2021 +0100 |
tree | c147c9bc019042103b27bdca76b38a323c7b7921 | |
parent | 67d876a0ca19c808a51d8e8db8b407aeced23250 [diff] |
[SYSTEMDS-2614] Compressed Right Matrix Multiplication This commit revisits 2614 that introduced right matrix multiplication. with the minor changes in this commit to correctly use threadLocal memory, and better blocks for decompression in a right matrix multiplication the performance (depending on data characteristics and system) becomes 2-8x faster for the compressed operation, meaning that if there is co-coding of columns the operation is equal or up to 6x faster than our default right matrix multiplication.
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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Documentation: SystemDS Documentation
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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