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From "Evan R. Sparks" <evan.spa...@gmail.com>
Subject Re: Using CUDA within Spark / boosting linear algebra
Date Wed, 25 Feb 2015 22:53:58 GMT
Thanks for compiling all the data and running these benchmarks, Alex. The
big takeaways here can be seen with this chart:
https://docs.google.com/spreadsheets/d/1aRm2IADRfXQV7G2vrcVh4StF50uZHl6kmAJeaZZggr0/pubchart?oid=1899767119&format=interactive

1) A properly configured GPU matrix multiply implementation (e.g.
BIDMat+GPU) can provide substantial (but less than an order of magnitude)
benefit over a well-tuned CPU implementation (e.g. BIDMat+MKL or
netlib-java+openblas-compiled).
2) A poorly tuned CPU implementation can be 1-2 orders of magnitude worse
than a well-tuned CPU implementation, particularly for larger matrices.
(netlib-f2jblas or netlib-ref) This is not to pick on netlib - this
basically agrees with the authors own benchmarks (
https://github.com/fommil/netlib-java)

I think that most of our users are in a situation where using GPUs may not
be practical - although we could consider having a good GPU backend
available as an option. However, *ALL* users of MLlib could benefit
(potentially tremendously) from using a well-tuned CPU-based BLAS
implementation. Perhaps we should consider updating the mllib guide with a
more complete section for enabling high performance binaries on OSX and
Linux? Or better, figure out a way for the system to fetch these
automatically.

- Evan



On Thu, Feb 12, 2015 at 4:18 PM, Ulanov, Alexander <alexander.ulanov@hp.com>
wrote:

> Just to summarize this thread, I was finally able to make all performance
> comparisons that we discussed. It turns out that:
> BIDMat-cublas>>BIDMat
> MKL==netlib-mkl==netlib-openblas-compiled>netlib-openblas-yum-repo==netlib-cublas>netlib-blas>f2jblas
>
> Below is the link to the spreadsheet with full results.
>
> https://docs.google.com/spreadsheets/d/1lWdVSuSragOobb0A_oeouQgHUMx378T9J5r7kwKSPkY/edit?usp=sharing
>
> One thing still needs exploration: does BIDMat-cublas perform copying
> to/from machine’s RAM?
>
> -----Original Message-----
> From: Ulanov, Alexander
> Sent: Tuesday, February 10, 2015 2:12 PM
> To: Evan R. Sparks
> Cc: Joseph Bradley; dev@spark.apache.org
> Subject: RE: Using CUDA within Spark / boosting linear algebra
>
> Thanks, Evan! It seems that ticket was marked as duplicate though the
> original one discusses slightly different topic. I was able to link netlib
> with MKL from BIDMat binaries. Indeed, MKL is statically linked inside a
> 60MB library.
>
> |A*B  size | BIDMat MKL | Breeze+Netlib-MKL  from BIDMat|
> Breeze+Netlib-OpenBlas(native system)| Breeze+Netlib-f2jblas |
> +-----------------------------------------------------------------------+
> |100x100*100x100 | 0,00205596 | 0,000381 | 0,03810324 | 0,002556 |
> |1000x1000*1000x1000 | 0,018320947 | 0,038316857 | 0,51803557 |1,638475459
> |
> |10000x10000*10000x10000 | 23,78046632 | 32,94546697 |445,0935211 |
> 1569,233228 |
>
> It turn out that pre-compiled MKL is faster than precompiled OpenBlas on
> my machine. Probably, I’ll add two more columns with locally compiled
> openblas and cuda.
>
> Alexander
>
> From: Evan R. Sparks [mailto:evan.sparks@gmail.com]
> Sent: Monday, February 09, 2015 6:06 PM
> To: Ulanov, Alexander
> Cc: Joseph Bradley; dev@spark.apache.org
> Subject: Re: Using CUDA within Spark / boosting linear algebra
>
> Great - perhaps we can move this discussion off-list and onto a JIRA
> ticket? (Here's one: https://issues.apache.org/jira/browse/SPARK-5705)
>
> It seems like this is going to be somewhat exploratory for a while (and
> there's probably only a handful of us who really care about fast linear
> algebra!)
>
> - Evan
>
> On Mon, Feb 9, 2015 at 4:48 PM, Ulanov, Alexander <alexander.ulanov@hp.com
> <mailto:alexander.ulanov@hp.com>> wrote:
> Hi Evan,
>
> Thank you for explanation and useful link. I am going to build OpenBLAS,
> link it with Netlib-java and perform benchmark again.
>
> Do I understand correctly that BIDMat binaries contain statically linked
> Intel MKL BLAS? It might be the reason why I am able to run BIDMat not
> having MKL BLAS installed on my server. If it is true, I wonder if it is OK
> because Intel sells this library. Nevertheless, it seems that in my case
> precompiled MKL BLAS performs better than precompiled OpenBLAS given that
> BIDMat and Netlib-java are supposed to be on par with JNI overheads.
>
> Though, it might be interesting to link Netlib-java with Intel MKL, as you
> suggested. I wonder, are John Canny (BIDMat) and Sam Halliday (Netlib-java)
> interested to compare their libraries.
>
> Best regards, Alexander
>
> From: Evan R. Sparks [mailto:evan.sparks@gmail.com<mailto:
> evan.sparks@gmail.com>]
> Sent: Friday, February 06, 2015 5:58 PM
>
> To: Ulanov, Alexander
> Cc: Joseph Bradley; dev@spark.apache.org<mailto:dev@spark.apache.org>
> Subject: Re: Using CUDA within Spark / boosting linear algebra
>
> I would build OpenBLAS yourself, since good BLAS performance comes from
> getting cache sizes, etc. set up correctly for your particular hardware -
> this is often a very tricky process (see, e.g. ATLAS), but we found that on
> relatively modern Xeon chips, OpenBLAS builds quickly and yields
> performance competitive with MKL.
>
> To make sure the right library is getting used, you have to make sure it's
> first on the search path - export LD_LIBRARY_PATH=/path/to/blas/library.so
> will do the trick here.
>
> For some examples of getting netlib-java setup on an ec2 node and some
> example benchmarking code we ran a while back, see:
> https://github.com/shivaram/matrix-bench
>
> In particular - build-openblas-ec2.sh shows you how to build the library
> and set up symlinks correctly, and scala/run-netlib.sh shows you how to get
> the path setup and get that library picked up by netlib-java.
>
> In this way - you could probably get cuBLAS set up to be used by
> netlib-java as well.
>
> - Evan
>
> On Fri, Feb 6, 2015 at 5:43 PM, Ulanov, Alexander <alexander.ulanov@hp.com
> <mailto:alexander.ulanov@hp.com>> wrote:
> Evan, could you elaborate on how to force BIDMat and netlib-java to force
> loading the right blas? For netlib, I there are few JVM flags, such as
> -Dcom.github.fommil.netlib.BLAS=com.github.fommil.netlib.F2jBLAS, so I can
> force it to use Java implementation. Not sure I understand how to force use
> a specific blas (not specific wrapper for blas).
>
> Btw. I have installed openblas (yum install openblas), so I suppose that
> netlib is using it.
>
> From: Evan R. Sparks [mailto:evan.sparks@gmail.com<mailto:
> evan.sparks@gmail.com>]
> Sent: Friday, February 06, 2015 5:19 PM
> To: Ulanov, Alexander
> Cc: Joseph Bradley; dev@spark.apache.org<mailto:dev@spark.apache.org>
>
> Subject: Re: Using CUDA within Spark / boosting linear algebra
>
> Getting breeze to pick up the right blas library is critical for
> performance. I recommend using OpenBLAS (or MKL, if you already have it).
> It might make sense to force BIDMat to use the same underlying BLAS library
> as well.
>
> On Fri, Feb 6, 2015 at 4:42 PM, Ulanov, Alexander <alexander.ulanov@hp.com
> <mailto:alexander.ulanov@hp.com>> wrote:
> Hi Evan, Joseph
>
> I did few matrix multiplication test and BIDMat seems to be ~10x faster
> than netlib-java+breeze (sorry for weird table formatting):
>
> |A*B  size | BIDMat MKL | Breeze+Netlib-java native_system_linux_x86-64|
> Breeze+Netlib-java f2jblas |
> +-----------------------------------------------------------------------+
> |100x100*100x100 | 0,00205596 | 0,03810324 | 0,002556 |
> |1000x1000*1000x1000 | 0,018320947 | 0,51803557 |1,638475459 |
> |10000x10000*10000x10000 | 23,78046632 | 445,0935211 | 1569,233228 |
>
> Configuration: Intel(R) Xeon(R) CPU E31240 3.3 GHz, 6GB RAM, Fedora 19
> Linux, Scala 2.11.
>
> Later I will make tests with Cuda. I need to install new Cuda version for
> this purpose.
>
> Do you have any ideas why breeze-netlib with native blas is so much slower
> than BIDMat MKL?
>
> Best regards, Alexander
>
> From: Joseph Bradley [mailto:joseph@databricks.com<mailto:
> joseph@databricks.com>]
> Sent: Thursday, February 05, 2015 5:29 PM
> To: Ulanov, Alexander
> Cc: Evan R. Sparks; dev@spark.apache.org<mailto:dev@spark.apache.org>
> Subject: Re: Using CUDA within Spark / boosting linear algebra
>
> Hi Alexander,
>
> Using GPUs with Spark would be very exciting.  Small comment: Concerning
> your question earlier about keeping data stored on the GPU rather than
> having to move it between main memory and GPU memory on each iteration, I
> would guess this would be critical to getting good performance.  If you
> could do multiple local iterations before aggregating results, then the
> cost of data movement to the GPU could be amortized (and I believe that is
> done in practice).  Having Spark be aware of the GPU and using it as
> another part of memory sounds like a much bigger undertaking.
>
> Joseph
>
> On Thu, Feb 5, 2015 at 4:59 PM, Ulanov, Alexander <alexander.ulanov@hp.com
> <mailto:alexander.ulanov@hp.com>> wrote:
> Thank you for explanation! I’ve watched the BIDMach presentation by John
> Canny and I am really inspired by his talk and comparisons with Spark MLlib.
>
> I am very interested to find out what will be better within Spark: BIDMat
> or netlib-java with CPU or GPU natives. Could you suggest a fair way to
> benchmark them? Currently I do benchmarks on artificial neural networks in
> batch mode. While it is not a “pure” test of linear algebra, it involves
> some other things that are essential to machine learning.
>
> From: Evan R. Sparks [mailto:evan.sparks@gmail.com<mailto:
> evan.sparks@gmail.com>]
> Sent: Thursday, February 05, 2015 1:29 PM
> To: Ulanov, Alexander
> Cc: dev@spark.apache.org<mailto:dev@spark.apache.org>
> Subject: Re: Using CUDA within Spark / boosting linear algebra
>
> I'd be surprised of BIDMat+OpenBLAS was significantly faster than
> netlib-java+OpenBLAS, but if it is much faster it's probably due to data
> layout and fewer levels of indirection - it's definitely a worthwhile
> experiment to run. The main speedups I've seen from using it come from
> highly optimized GPU code for linear algebra. I know that in the past Canny
> has gone as far as to write custom GPU kernels for performance-critical
> regions of code.[1]
>
> BIDMach is highly optimized for single node performance or performance on
> small clusters.[2] Once data doesn't fit easily in GPU memory (or can be
> batched in that way) the performance tends to fall off. Canny argues for
> hardware/software codesign and as such prefers machine configurations that
> are quite different than what we find in most commodity cluster nodes -
> e.g. 10 disk cahnnels and 4 GPUs.
>
> In contrast, MLlib was designed for horizontal scalability on commodity
> clusters and works best on very big datasets - order of terabytes.
>
> For the most part, these projects developed concurrently to address
> slightly different use cases. That said, there may be bits of BIDMach we
> could repurpose for MLlib - keep in mind we need to be careful about
> maintaining cross-language compatibility for our Java and Python-users,
> though.
>
> - Evan
>
> [1] - http://arxiv.org/abs/1409.5402
> [2] - http://eecs.berkeley.edu/~hzhao/papers/BD.pdf
>
> On Thu, Feb 5, 2015 at 1:00 PM, Ulanov, Alexander <alexander.ulanov@hp.com
> <mailto:alexander.ulanov@hp.com><mailto:alexander.ulanov@hp.com<mailto:
> alexander.ulanov@hp.com>>> wrote:
> Hi Evan,
>
> Thank you for suggestion! BIDMat seems to have terrific speed. Do you know
> what makes them faster than netlib-java?
>
> The same group has BIDMach library that implements machine learning. For
> some examples they use Caffe convolutional neural network library owned by
> another group in Berkeley. Could you elaborate on how these all might be
> connected with Spark Mllib? If you take BIDMat for linear algebra why don’t
> you take BIDMach for optimization and learning?
>
> Best regards, Alexander
>
> From: Evan R. Sparks [mailto:evan.sparks@gmail.com<mailto:
> evan.sparks@gmail.com><mailto:evan.sparks@gmail.com<mailto:
> evan.sparks@gmail.com>>]
> Sent: Thursday, February 05, 2015 12:09 PM
> To: Ulanov, Alexander
> Cc: dev@spark.apache.org<mailto:dev@spark.apache.org><mailto:
> dev@spark.apache.org<mailto:dev@spark.apache.org>>
> Subject: Re: Using CUDA within Spark / boosting linear algebra
>
> I'd expect that we can make GPU-accelerated BLAS faster than CPU blas in
> many cases.
>
> You might consider taking a look at the codepaths that BIDMat (
> https://github.com/BIDData/BIDMat) takes and comparing them to
> netlib-java/breeze. John Canny et. al. have done a bunch of work optimizing
> to make this work really fast from Scala. I've run it on my laptop and
> compared to MKL and in certain cases it's 10x faster at matrix multiply.
> There are a lot of layers of indirection here and you really want to avoid
> data copying as much as possible.
>
> We could also consider swapping out BIDMat for Breeze, but that would be a
> big project and if we can figure out how to get breeze+cublas to comparable
> performance that would be a big win.
>
> On Thu, Feb 5, 2015 at 11:55 AM, Ulanov, Alexander <
> alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com><mailto:
> alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>>> wrote:
> Dear Spark developers,
>
> I am exploring how to make linear algebra operations faster within Spark.
> One way of doing this is to use Scala Breeze library that is bundled with
> Spark. For matrix operations, it employs Netlib-java that has a Java
> wrapper for BLAS (basic linear algebra subprograms) and LAPACK native
> binaries if they are available on the worker node. It also has its own
> optimized Java implementation of BLAS. It is worth mentioning, that native
> binaries provide better performance only for BLAS level 3, i.e.
> matrix-matrix operations or general matrix multiplication (GEMM). This is
> confirmed by GEMM test on Netlib-java page
> https://github.com/fommil/netlib-java. I also confirmed it with my
> experiments with training of artificial neural network
> https://github.com/apache/spark/pull/1290#issuecomment-70313952. However,
> I would like to boost performance more.
>
> GPU is supposed to work fast with linear algebra and there is Nvidia CUDA
> implementation of BLAS, called cublas. I have one Linux server with Nvidia
> GPU and I was able to do the following. I linked cublas (instead of
> cpu-based blas) with Netlib-java wrapper and put it into Spark, so
> Breeze/Netlib is using it. Then I did some performance measurements with
> regards to artificial neural network batch learning in Spark MLlib that
> involves matrix-matrix multiplications. It turns out that for matrices of
> size less than ~1000x780 GPU cublas has the same speed as CPU blas. Cublas
> becomes slower for bigger matrices. It worth mentioning that it is was not
> a test for ONLY multiplication since there are other operations involved.
> One of the reasons for slowdown might be the overhead of copying the
> matrices from computer memory to graphic card memory and back.
>
> So, few questions:
> 1) Do these results with CUDA make sense?
> 2) If the problem is with copy overhead, are there any libraries that
> allow to force intermediate results to stay in graphic card memory thus
> removing the overhead?
> 3) Any other options to speed-up linear algebra in Spark?
>
> Thank you, Alexander
>
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