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From John Canny <ca...@berkeley.edu>
Subject Re: Using CUDA within Spark / boosting linear algebra
Date Thu, 26 Mar 2015 16:20:16 GMT
I mentioned this earlier in the thread, but I'll put it out again. Dense 
BLAS are not very important for most machine learning workloads: at 
least for non-image workloads in industry (and for image processing you 
would probably want a deep learning/SGD solution with convolution 
kernels). e.g. it was only relevant for 1/7 of our recent benchmarks, 
which should be a reasonable sample. What really matters is sparse BLAS 
performance. BIDMat is still an order of magnitude faster there. Those 
kernels are only in BIDMat, since NVIDIAs sparse BLAS dont perform well 
on power-law data.

Its also the case that the overall performance of an algorithm is 
determined by the slowest kernel, not the fastest. If the goal is to get 
closer to BIDMach's performance on typical problems, you need to make 
sure that every kernel goes at comparable speed. So the real question is 
how much faster MLLib routines do on a complete problem with/without GPU 
acceleration. For BIDMach, its close to a factor of 10. But that 
required running entirely on the GPU, and making sure every kernel is 
close to its limit.

-John

If you think nvblas would be helpful, you should try it in some 
end-to-end benchmarks.
On 3/25/15, 6:23 PM, Evan R. Sparks wrote:
> Yeah, much more reasonable - nice to know that we can get full GPU 
> performance from breeze/netlib-java - meaning there's no compelling 
> performance reason to switch out our current linear algebra library 
> (at least as far as this benchmark is concerned).
>
> Instead, it looks like a user guide for configuring Spark/MLlib to use 
> the right BLAS library will get us most of the way there. Or, would it 
> make sense to finally ship openblas compiled for some common platforms 
> (64-bit linux, windows, mac) directly with Spark - hopefully 
> eliminating the jblas warnings once and for all for most users? 
> (Licensing is BSD) Or am I missing something?
>
> On Wed, Mar 25, 2015 at 6:03 PM, Ulanov, Alexander 
> <alexander.ulanov@hp.com <mailto:alexander.ulanov@hp.com>> wrote:
>
>     As everyone suggested, the results were too good to be true, so I
>     double-checked them. It turns that nvblas did not do
>     multiplication due to parameter NVBLAS_TILE_DIM from "nvblas.conf"
>     and returned zero matrix. My previously posted results with nvblas
>     are matrices copying only. The default NVBLAS_TILE_DIM==2048 is
>     too big for my graphic card/matrix size. I handpicked other values
>     that worked. As a result, netlib+nvblas is on par with
>     BIDMat-cuda. As promised, I am going to post a how-to for nvblas
>     configuration.
>
>     https://docs.google.com/spreadsheets/d/1lWdVSuSragOobb0A_oeouQgHUMx378T9J5r7kwKSPkY/edit?usp=sharing
>
>
>
>     -----Original Message-----
>     From: Ulanov, Alexander
>     Sent: Wednesday, March 25, 2015 2:31 PM
>     To: Sam Halliday
>     Cc: dev@spark.apache.org <mailto:dev@spark.apache.org>; Xiangrui
>     Meng; Joseph Bradley; Evan R. Sparks; jfcanny
>     Subject: RE: Using CUDA within Spark / boosting linear algebra
>
>     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
>     <http://eecs.berkeley.edu/%7Ehzhao/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
>     >>>>
>     >>>>
>     -------------------------------------------------------------------
>     >>>> -- To unsubscribe, e-mail: dev-unsubscribe@spark.apache.org
>     <mailto:dev-unsubscribe@spark.apache.org><mailto:dev-unsubscribe@spark.apache.org
>     <mailto:dev-unsubscribe@spark.apache.org>><mailto:
>     >>>> dev-unsubscribe@spark.apache.org
>     <mailto:dev-unsubscribe@spark.apache.org><mailto:dev-unsubscribe@spark.apach
>     <mailto:dev-unsubscribe@spark.apach>
>     >>>> e.org <http://e.org>>><mailto:dev-unsubscribe@spark.apac
>     <mailto:dev-unsubscribe@spark.apac><mailto:dev-unsubscribe@sp
>     <mailto:dev-unsubscribe@sp>
>     >>>> ark.apac> he.org <http://he.org><http://he.org>
>     >>>> <mailto:dev-unsubscribe@spark.apache.org
>     <mailto:dev-unsubscribe@spark.apache.org><mailto:dev-unsubscribe@spa
>     <mailto:dev-unsubscribe@spa>
>     >>>> rk.apache.org <http://rk.apache.org>>>> For additional
>     commands, e-mail:
>     >>>> dev-help@spark.apache.org
>     <mailto:dev-help@spark.apache.org><mailto:dev-help@spark.apache.org <mailto:dev-help@spark.apache.org>><mailto:
>     >>>> dev-help@spark.apache.org
>     <mailto:dev-help@spark.apache.org><mailto:dev-help@spark.apache.org <mailto:dev-help@spark.apache.org>>><mailto:dev-help@spark.apache.org
>     <mailto:dev-help@spark.apache.org><mailto:dev-help@spark.apache.org <mailto:dev-help@spark.apache.org>><mailto:
>     >>>> dev-help@spark.apache.org
>     <mailto:dev-help@spark.apache.org><mailto:dev-help@spark.apache.org <mailto:dev-help@spark.apache.org>>>>
>     >>>>
>     >>>>
>     >>>>
>     >>>>
>     >>>
>
>     --
>     Best regards,
>     Sam
>
>


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