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From "Ulanov, Alexander" <alexander.ula...@hp.com>
Subject RE: Using CUDA within Spark / boosting linear algebra
Date Wed, 25 Mar 2015 22:04:30 GMT
Netlib knows nothing about GPU (or CPU), it just uses cblas symbols from the provided libblas.so.3
library at the runtime. So, you can switch at the runtime by providing another library. Sam,
please suggest if there is another way.

From: Dmitriy Lyubimov [mailto:dlieu.7@gmail.com]
Sent: Wednesday, March 25, 2015 2:55 PM
To: Ulanov, Alexander
Cc: Sam Halliday; dev@spark.apache.org; Xiangrui Meng; Joseph Bradley; Evan R. Sparks; jfcanny
Subject: Re: Using CUDA within Spark / boosting linear algebra

Alexander,

does using netlib imply that one cannot switch between CPU and GPU blas alternatives at will
at the same time? the choice is always determined by linking aliternatives to libblas.so,
right?

On Wed, Mar 25, 2015 at 2:31 PM, Ulanov, Alexander <alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>>
wrote:
Hi again,

I finally managed to use nvblas within Spark+netlib-java. It has exceptional performance for
big matrices with Double, faster than BIDMat-cuda with Float. But for smaller matrices, if
you will copy them to/from GPU, OpenBlas or MKL might be a better choice. This correlates
with original nvblas presentation on GPU conf 2013 (slide 21): http://on-demand.gputechconf.com/supercomputing/2013/presentation/SC3108-New-Features-CUDA%206%20-GPU-Acceleration.pdf

My results:
https://docs.google.com/spreadsheets/d/1lWdVSuSragOobb0A_oeouQgHUMx378T9J5r7kwKSPkY/edit?usp=sharing

Just in case, these tests are not for generalization of performance of different libraries.
I just want to pick a library that does at best dense matrices multiplication for my task.

P.S. My previous issue with nvblas was the following: it has Fortran blas functions, at the
same time netlib-java uses C cblas functions. So, one needs cblas shared library to use nvblas
through netlib-java. Fedora does not have cblas (but Debian and Ubuntu have), so I needed
to compile it. I could not use cblas from Atlas or Openblas because they link to their implementation
and not to Fortran blas.

Best regards, Alexander

-----Original Message-----
From: Ulanov, Alexander
Sent: Tuesday, March 24, 2015 6:57 PM
To: Sam Halliday
Cc: dev@spark.apache.org<mailto:dev@spark.apache.org>; Xiangrui Meng; Joseph Bradley;
Evan R. Sparks
Subject: RE: Using CUDA within Spark / boosting linear algebra

Hi,

I am trying to use nvblas with netlib-java from Spark. nvblas functions should replace current
blas functions calls after executing LD_PRELOAD as suggested in http://docs.nvidia.com/cuda/nvblas/#Usage
without any changes to netlib-java. It seems to work for simple Java example, but I cannot
make it work with Spark. I run the following:
export LD_LIBRARY_PATH=/usr/local/cuda-6.5/lib64
env LD_PRELOAD=/usr/local/cuda-6.5/lib64/libnvblas.so ./spark-shell --driver-memory 4G In
nvidia-smi I observe that Java is to use GPU:
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|    0      8873    C   bash                                            39MiB |
|    0      8910    C   /usr/lib/jvm/java-1.7.0/bin/java                39MiB |
+-----------------------------------------------------------------------------+

In Spark shell I do matrix multiplication and see the following:
15/03/25 06:48:01 INFO JniLoader: successfully loaded /tmp/jniloader8192964377009965483netlib-native_system-linux-x86_64.so
So I am sure that netlib-native is loaded and cblas supposedly used. However, matrix multiplication
does executes on CPU since I see 16% of CPU used and 0% of GPU used. I also checked different
matrix sizes, from 100x100 to 12000x12000

Could you suggest might the LD_PRELOAD not affect Spark shell?

Best regards, Alexander



From: Sam Halliday [mailto:sam.halliday@gmail.com<mailto:sam.halliday@gmail.com>]
Sent: Monday, March 09, 2015 6:01 PM
To: Ulanov, Alexander
Cc: dev@spark.apache.org<mailto:dev@spark.apache.org>; Xiangrui Meng; Joseph Bradley;
Evan R. Sparks
Subject: RE: Using CUDA within Spark / boosting linear algebra


Thanks so much for following up on this!

Hmm, I wonder if we should have a concerted effort to chart performance on various pieces
of hardware...
On 9 Mar 2015 21:08, "Ulanov, Alexander" <alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>>>
wrote:
Hi Everyone, I've updated the benchmark as Xiangrui suggested. Added the comment that BIDMat
0.9.7 uses Float matrices in GPU (although I see the support of Double in the current source
code), did the test with BIDMat and CPU Double matrices. BIDMat MKL is indeed on par with
netlib MKL.

https://docs.google.com/spreadsheets/d/1lWdVSuSragOobb0A_oeouQgHUMx378T9J5r7kwKSPkY/edit?usp=sharing

Best regards, Alexander

-----Original Message-----
From: Sam Halliday [mailto:sam.halliday@gmail.com<mailto:sam.halliday@gmail.com><mailto:sam.halliday@gmail.com<mailto:sam.halliday@gmail.com>>]
Sent: Tuesday, March 03, 2015 1:54 PM
To: Xiangrui Meng; Joseph Bradley
Cc: Evan R. Sparks; Ulanov, Alexander; 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

BTW, is anybody on this list going to the London Meetup in a few weeks?

https://skillsmatter.com/meetups/6987-apache-spark-living-the-post-mapreduce-world#community

Would be nice to meet other people working on the guts of Spark! :-)


Xiangrui Meng <mengxr@gmail.com<mailto:mengxr@gmail.com><mailto:mengxr@gmail.com<mailto:mengxr@gmail.com>>>
writes:

> Hey Alexander,
>
> I don't quite understand the part where netlib-cublas is about 20x
> slower than netlib-openblas. What is the overhead of using a GPU BLAS
> with netlib-java?
>
> CC'ed Sam, the author of netlib-java.
>
> Best,
> Xiangrui
>
> On Wed, Feb 25, 2015 at 3:36 PM, Joseph Bradley <joseph@databricks.com<mailto:joseph@databricks.com><mailto:joseph@databricks.com<mailto:joseph@databricks.com>>>
wrote:
>> Better documentation for linking would be very helpful!  Here's a JIRA:
>> https://issues.apache.org/jira/browse/SPARK-6019
>>
>>
>> On Wed, Feb 25, 2015 at 2:53 PM, Evan R. Sparks
>> <evan.sparks@gmail.com<mailto:evan.sparks@gmail.com><mailto:evan.sparks@gmail.com<mailto:evan.sparks@gmail.com>>>
>> wrote:
>>
>>> 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/1aRm2IADRfXQV7G2vrcVh4StF50uZ
>>> Hl6kmAJeaZZggr0/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
>>> BIDMat+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<mailto:alexander.ulanov@hp.com><mailto:alexander.ulanov@hp.com<mailto: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_oeouQgHUMx
>>>> 378T9J5r7kwKSPkY/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<mailto:dev@spark.apache.org><mailto:dev@spark.apache.org<mailto: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<mailto:evan.sparks@gmail.com><mailto:evan.sparks@gmail.com<mailto:evan.sparks@gmail.com>>]
>>>> Sent: Monday, February 09, 2015 6:06 PM
>>>> To: Ulanov, Alexander
>>>> Cc: Joseph Bradley;
>>>> 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
>>>>
>>>> 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><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>><mailto: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 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><mailto:evan.sparks@gmail.com<mailto:evan.sparks@gmail.com>><mailto:
>>>> evan.sparks@gmail.com<mailto:evan.sparks@gmail.com><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><mailto:dev@spark.apache.org<mailto:dev@spark.apache.org>><mailto:dev@spark<mailto:dev@spark>.
>>>> apache.org<http://apache.org><mailto: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><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com><mailto: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><mailto:evan.sparks@gmail.com<mailto:evan.sparks@gmail.com>><mailto:
>>>> evan.sparks@gmail.com<mailto:evan.sparks@gmail.com><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><mailto:dev@spark.apache.org<mailto:dev@spark.apache.org>><mailto:dev@spark<mailto:dev@spark>.
>>>> apache.org<http://apache.org><mailto: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><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com><mailto: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><mailto:joseph@databricks.com<mailto:joseph@databricks.com>><mailto:
>>>> joseph@databricks.com<mailto:joseph@databricks.com><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><mailto:dev@spark.apache.org<mailto:dev@spark.apache.org>><mailto:dev@spark<mailto:dev@spark>.
>>>> apache.org<http://apache.org><mailto: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><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com><mailto: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><mailto:evan.sparks@gmail.com<mailto:evan.sparks@gmail.com>><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 1:29 PM
>>>> To: Ulanov, Alexander
>>>> Cc:
>>>> dev@spark.apache.org<mailto:dev@spark.apache.org><mailto:dev@spark.apache.org<mailto:dev@spark.apache.org>><mailto:dev@spark<mailto:dev@spark>.
>>>> apache.org<http://apache.org><mailto: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
>>>> netlib-java+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>><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>>><mailto:
>>>> alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>><mailto: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>><mailto:
>>>> evan.sparks@gmail.com<mailto:evan.sparks@gmail.com><mailto:evan.sparks@gmail.com<mailto:evan.sparks@gmail.com>>><mailto:evan.sparks@gmail.com<mailto:evan.sparks@gmail.com><mailto:evan.sparks@gmail.com<mailto:evan.sparks@gmail.com>><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>><mailto:dev@spark.apache.org<mailto:dev@spark.apache.org><mailto:dev@spark.apache.org<mailto:dev@spark.apache.org>>><mailto:
>>>> dev@spark.apache.org<mailto:dev@spark.apache.org><mailto:dev@spark.apache.org<mailto:dev@spark.apache.org>><mailto:dev@spark<mailto:dev@spark>.
>>>> apache.org<http://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>><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>>><mailto:
>>>> alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com><mailto:alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>><mailto: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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>>>>
>>>>
>>>>
>>>>
>>>

--
Best regards,
Sam

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