commit | b350c04f53fd249cc4c916093404c5d760055327 | [log] [tgz] |
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author | Mark Dokter <mark@dokter.cc> | Tue Nov 17 00:34:01 2020 +0100 |
committer | arnabp <arnab.phani@tugraz.at> | Tue Nov 17 00:38:31 2020 +0100 |
tree | dd4b25fcbe6aa7aa9a61fbf3089935b89622cb13 | |
parent | 756e4471527281439fc62610cdb30f6c8cfe9301 [diff] |
[SYSTEMDS-2725] NNZ counting for native blas This patch moves nnz count to native blas. This should improve performance as the native nnz computation leverages openmp reduction. In addition, this patch adds: * a lot of code cleanup * more fallback to java mat mult
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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Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source