commit | 52f2b3e27cc012272fe2ead3a3d0cde49983d4fb | [log] [tgz] |
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author | Mark Dokter <mark@dokter.cc> | Sun Nov 08 00:47:15 2020 +0100 |
committer | Mark Dokter <mark@dokter.cc> | Sun Nov 08 00:47:15 2020 +0100 |
tree | 1de459c28c1f9dc792266e1f74c2ed74b9fed95f | |
parent | 22375bbb4c6f2efcbcce43db12de37fdd406b77c [diff] |
[SYSTEMDS-2691, 2692, 2698] Initial SPOOF CUDA; Refactoring of CUDA kernels Changelog from squashed commits: * Refactor CUDA codebase to its own directory * JNI parts for CUDA codegen and more code reorganization * SpoofCompiler CUDA loading and compiler invocation * Cellwise code template and other CPlan templates for operators (*nary/data/...) * SpoofCUDA runtime instruction and also: * Import jitify as submodule * Template loading from JAR file * Configuration tag <sysds.codegen.api> to use CUDA codegen (see SystemDS-config.xml.template) Closes #1092
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