kiszk commented on a change in pull request #29139:
URL: https://github.com/apache/spark/pull/29139#discussion_r456874086



##########
File path: docs/ml-linalg-guide.md
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@@ -0,0 +1,85 @@
+# Spark MLlib Linear Algebra Acceleration Guide
+
+## Introduction
+
+This guide provides necessary information to enable accelerated linear algebra 
processing for Spark MLlib.
+
+Spark MLlib defines Vector and Matrix as basic data types for machine learning 
algorithms. On top of them, 
[BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) and 
[LAPACK](https://en.wikipedia.org/wiki/LAPACK) operations are implemented and 
supported by [netlib-java](https://github.com/fommil/netlib-Java).[^1] 
`netlib-java` can use optimized native linear algebra libraries (refered to as 
"native libraries" or "BLAS libraries" hereafter) for faster numerical 
processing. [Intel 
MKL](https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html)
 and [OpenBLAS](http://www.openblas.net) are two most popular ones.

Review comment:
       nit: `.[^1]` -> `[^1].`




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