commit | 553ca46c1c4b41944ff6e7252de3af0b18f6c451 | [log] [tgz] |
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author | arnabp <arnab.phani@tugraz.at> | Mon Nov 02 18:40:11 2020 +0100 |
committer | arnabp <arnab.phani@tugraz.at> | Mon Nov 02 18:59:48 2020 +0100 |
tree | c0b9b52fef0c9af6305ec48ca8add11e1574e117 | |
parent | 3a4decdb2a7969cf2d68707675dc96868f197de3 [diff] |
[SYSTEMDS-2713] DAG height based eviction policy for lineage cache This patch introduces a new eviction policy, DAGHEIGHT, for lineage cache eviction. According to this policy, lineage items farthest from the leaves get evicted first. We store the height of each item on construction. For inputs having different heights, we find the maximum (linear time) and increment it by one for the current node. In addition, this patch updates the Comparator for the eviction queue. Now, entries with same score are ordered by cost, size ratio in LRU and DAGHEIGHT policies, and by timestamp in COSTNSIZE policy. For HYBRID, entries with same score are still ordered by Ids.
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