| commit | 9f8eae93b67352bb6c770b6fcb62c01a23336044 | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Mon Oct 19 13:21:23 2020 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Mon Oct 19 13:21:38 2020 +0200 |
| tree | 36734188fe4e9afd4bc63da0a27920bdadfff035 | |
| parent | 472d778b4c0713b6df6b3a10bcfd7475ef167cc8 [diff] |
[SYSTEMDS-2688] Performance diag V2M operations (shallow data copy) For diag V2M, we already output the sparse result in a space-efficient CSR representation. However, for the special case of input vectors v with nrow(v)==nnz(v) -- which often happens in practice -- we now use a shallow copy of the data array because the CSR values array is equivalent. Similarly, we now reuse a integer single seq(0,nrow(v)) for both the pointer and column index arrays (by setting the available logical size of the column index array to the nnz, this shared array is logically truncated by the last entry). For a scenario of 1000 diag V2M operations over a (? x 1) input vectors, we got the following improvements: * 1000 times diag(1M x 1): 9.1s -> 3.6s * 1000 times diag(10M x 1): 81.9s -> 22.5s
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
