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Posted to issues@spark.apache.org by "Apache Spark (Jira)" <ji...@apache.org> on 2021/04/20 14:38:00 UTC
[jira] [Assigned] (SPARK-35150) Accelerate fallback BLAS with
dev.ludovic.netlib
[ https://issues.apache.org/jira/browse/SPARK-35150?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Apache Spark reassigned SPARK-35150:
------------------------------------
Assignee: (was: Apache Spark)
> Accelerate fallback BLAS with dev.ludovic.netlib
> ------------------------------------------------
>
> Key: SPARK-35150
> URL: https://issues.apache.org/jira/browse/SPARK-35150
> Project: Spark
> Issue Type: Improvement
> Components: GraphX, ML, MLlib
> Affects Versions: 3.2.0
> Reporter: Ludovic Henry
> Priority: Major
>
> Following https://github.com/apache/spark/pull/30810, I've continued looking for ways to accelerate the usage of BLAS in Spark. With this PR, I integrate work done in the [{{dev.ludovic.netlib}}|https://github.com/luhenry/netlib/] Maven package.
> The {{dev.ludovic.netlib}} library wraps the original {{com.github.fommil.netlib}} library and focus on accelerating the linear algebra routines in use in Spark. When running the {{org.apache.spark.ml.linalg.BLASBenchmark}}benchmarking suite, I get the results at [1] on an Intel machine. Moreover, this library is thoroughly tested to return the exact same results as the reference implementation.
> Under the hood, it reimplements the necessary algorithms in pure autovectorization-friendly Java 8, as well as takes advantage of the Vector API and Foreign Linker API introduced in JDK 16 when available.
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