Hi,
Thank you for the feedback. I’ll work on the profile-based approach to
selectively compile this VectorBLAS class in. As for the run-time, I haven’t
used specifically a reflection-based approach but a more simple `try { new
VectorBLAS() } catch (NoClassDefFoundError) { new F2jBLAS() }`. I’ll submit a
PR against gitHub.com/apache/spark with this change. Should I also fill up a
bug inside the Jira as well?
On a side note, I worked yesterday on extracting this code into a standalone
project [1]. It’s not so much so that Spark can depend on that (even though it
could be possible), but it is to make it easier to develop, test, and benchmark
new implementations on my end.
Thank you,
Ludovic
[1] https://github.com/luhenry/blas
From: Erik Krogen<mailto:xkro...@apache.org>
Sent: Tuesday, 15 December 2020 17:33
To: Sean Owen<mailto:sro...@gmail.com>
Cc: Ludovic Henry<mailto:luhe...@microsoft.com>;
dev@spark.apache.org<mailto:dev@spark.apache.org>; Bernhard
Urban-Forster<mailto:beu...@microsoft.com>
Subject: Re: Usage of JDK Vector API in ML/MLLib
Regarding selective compilation, you can hide sources behind a Maven profile
such as `-Pvectorized`. Check out what we do to switch between the `hive-1.2`
and `hive-2.3` profiles where different source directories are grabbed at
compile-time (the hive-1.2 profile was recently removed so you might have to go
back a little in git history). This won't do it automatically based on JDK
version, but it's probably good enough. At runtime you can more easily do a JDK
version check -- I agree with Sean on loading via reflection.
Personally, I see no reason not to start adding this support in preparation for
broader adoption of JDK 16, provided that it is properly protected behind
flags. This could be a big win for installations which haven't gone through the
process of installing native BLAS libs.
On Tue, Dec 15, 2020 at 7:10 AM Sean Owen
<sro...@gmail.com<mailto:sro...@gmail.com>> wrote:
Yes it's intriguing, though as you say not readily available in the wild yet.
I would also expect native BLAS to outperform f2j also, so yeah that's the
interesting question, whether this is a win over native code or not.
I suppose the upside is eventually, we may expect this API to be available in
all JVMs, not just those with native libraries added at runtime.
I wonder if a short-term goal would be to ensure that these calls are simply
abstracted away, which they should already me, so it's easy to plug in this new
'BLAS' implementation. I'm sure it's possible to load this selectively via
reflection, as that's what the current libraries do.
And there may be additional code paths that could benefit from these operations
that don't already.
On Tue, Dec 15, 2020 at 8:30 AM Ludovic Henry <luhe...@microsoft.com.invalid>
wrote:
Hello,
I’ve, over the past few days, looked into using the new Vector API [1] to
accelerate some BLAS operations straight from Java. You can find a gist at [2]
containing most of the changes in
mllib-local/src/main/scala/org/apache/spark/ml/linalg/BLAS.scala.
To measure performance, I’ve added a BLASBenchmark.scala [3] at
mllib-local/src/test/scala/org/apache/spark/ml/linalg/BLASBenchmark.scala. I do
see some promising speedups, especially compared to F2jBLAS. I’ve unfortunately
not been able to install OpenBLAS locally and compare performance to native,
but I would still expect native to be faster, especially on large inputs. See
[4] for some f2j vs vector performance comparison.
The primary blocker is that the Vector API is only available in incubator mode,
starting with JDK 16. We can have an easy run-time check whether we can use the
Vectorized BLAS. But, to compile the Vectorized BLAS class, we need JDK 16+.
Spark 3.0+ does compile with JDK 16 (it works locally), but I don’t know how to
selectively compile sources based on the JDK version used at compile-time.
But much more importantly, I want to get your feedback before I keep exploring
this idea further. Technically, it is feasible, and we’ll observe speed up
whenever the native BLAS is not installed. Moreover, I am solely focusing on
ML/MLLib for now. However, there is still graphx (I haven’t checked if there is
anything vectorizable) and even supporting more explicit use of the Vector API
in catalyst, which is a much bigger project.
Thank you,
Ludovic Henry
[1]
https://openjdk.java.net/jeps/338<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fopenjdk.java.net%2Fjeps%2F338&data=04%7C01%7Cluhenry%40microsoft.com%7C0529612745ad4559cf0608d8a1172a0d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637436468156914676%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=QpoFg2EPrkCsbFHGUvK26opwpbVruQOwCde70o%2FE50s%3D&reserved=0>
[2]
https://gist.github.com/luhenry/6b24ac146a110143ad31736caf7250e6#file-blas-scala<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgist.github.com%2Fluhenry%2F6b24ac146a110143ad31736caf7250e6%23file-blas-scala&data=04%7C01%7Cluhenry%40microsoft.com%7C0529612745ad4559cf0608d8a1172a0d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637436468156924670%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=M%2Bir7vVGDxDamrXvwvrtqzhOEQ6TD7oJT3sf5fJ1Ovk%3D&reserved=0>
[3]
https://gist.github.com/luhenry/6b24ac146a110143ad31736caf7250e6#file-blasbenchmark-scala<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgist.github.com%2Fluhenry%2F6b24ac146a110143ad31736caf7250e6%23file-blasbenchmark-scala&data=04%7C01%7Cluhenry%40microsoft.com%7C0529612745ad4559cf0608d8a1172a0d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637436468156934671%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=2PRGL%2FeVB4QMGwpNyebTAKttjESnhek5LDSQuYRYawM%3D&reserved=0>
[4]
https://gist.github.com/luhenry/6b24ac146a110143ad31736caf7250e6#file-f2j-vs-vector-log<https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgist.github.com%2Fluhenry%2F6b24ac146a110143ad31736caf7250e6%23file-f2j-vs-vector-log&data=04%7C01%7Cluhenry%40microsoft.com%7C0529612745ad4559cf0608d8a1172a0d%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C637436468156934671%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=4FA7p18jd6yVnIvRGNNeDWA5%2F%2Fw249z6%2B%2BOuJhRnTBI%3D&reserved=0>
