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From "Ulanov, Alexander" <alexander.ula...@hp.com>
Subject RE: Using CUDA within Spark / boosting linear algebra
Date Mon, 02 Mar 2015 22:43:49 GMT
Thanks Sam for suggestion! I should try doing this. Now I suppose that netlib-java linked with cuBlas during the execution time does fall back to cblas library in my system, which is atlas. If I remove atlas, netlib (linked with cublas) fails with the message "undefined symbol: cblas_dgemm".  

In the meantime, I have updated my spreadsheet with BIDMat-cuda results that does copy from main memory to GPU, multiplies and the copies it back to main memory (similar to what Xiangrui did). Surprisingly (for myself), the copying overhead seems quite small, especially for the bigger matrices.

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

-----Original Message-----
From: Sam Halliday [mailto:sam.halliday@gmail.com] 
Sent: Monday, March 02, 2015 1:24 PM
To: Ulanov, Alexander
Subject: Re: Using CUDA within Spark / boosting linear algebra

That's correct. It's highly unusual for a libblas.so to only provide the Fortran API. Oh well... CBLAS sources are available in the netlib-java repository so you could simply compile them and link against whatever libblas.so[fortran] you like.

On 2 March 2015 at 21:04, Ulanov, Alexander <alexander.ulanov@hp.com> wrote:
> Hi Xiangrui,
>
> Thanks for the link, I am currently trying to use nvblas. It seems that netlib wrappers are implemented with C-BLAS interface and nvblas does not have c-blas. I wonder how it is going to work. I'll keep you updated.
>
> Alexander
>
> -----Original Message-----
> From: Xiangrui Meng [mailto:mengxr@gmail.com]
> Sent: Monday, March 02, 2015 11:42 AM
> To: Sam Halliday
> Cc: Joseph Bradley; Ulanov, Alexander; dev; Evan R. Sparks
> Subject: Re: Using CUDA within Spark / boosting linear algebra
>
> On Fri, Feb 27, 2015 at 12:33 PM, Sam Halliday <sam.halliday@gmail.com> wrote:
>> Also, check the JNILoader output.
>>
>> Remember, for netlib-java to use your system libblas all you need to 
>> do is setup libblas.so.3 like any native application would expect.
>>
>> I haven't ever used the cublas "real BLAS"  implementation, so I'd be 
>> interested to hear about this. Do an 'ldd /usr/lib/libblas.so.3' to 
>> check that all the runtime links are in order.
>>
>
> There are two shared libraries in this hybrid setup. nvblas.so must be 
> loaded before libblas.so to intercept level 3 routines using GPU. More 
> details are at: http://docs.nvidia.com/cuda/nvblas/index.html#Usage
>
>> Btw, I have some DGEMM wrappers in my netlib-java performance 
>> module... and I also planned to write more in MultiBLAS (until I 
>> mothballed the project for the hardware to catch up, which is 
>> probably has and now I just need a reason to look at it)
>>
>> On 27 Feb 2015 20:26, "Xiangrui Meng" <mengxr@gmail.com> wrote:
>>>
>>> Hey Sam,
>>>
>>> The running times are not "big O" estimates:
>>>
>>> > The CPU version finished in 12 seconds.
>>> > The CPU->GPU->CPU version finished in 2.2 seconds.
>>> > The GPU version finished in 1.7 seconds.
>>>
>>> I think there is something wrong with the netlib/cublas combination.
>>> Sam already mentioned that cuBLAS doesn't implement the CPU BLAS 
>>> interfaces. I checked the CUDA doc and it seems that to use GPU BLAS 
>>> through the CPU BLAS interface we need to use NVBLAS, which 
>>> intercepts some Level 3 CPU BLAS calls (including GEMM). So we need 
>>> to load nvblas.so first and then some CPU BLAS library in JNI. I 
>>> wonder whether the setup was correct.
>>>
>>> Alexander, could you check whether GPU is used in the netlib-cublas 
>>> experiments? You can tell it by watching CPU/GPU usage.
>>>
>>> Best,
>>> Xiangrui
>>>
>>> On Thu, Feb 26, 2015 at 10:47 PM, Sam Halliday 
>>> <sam.halliday@gmail.com>
>>> wrote:
>>> > Don't use "big O" estimates, always measure. It used to work back 
>>> > in the days when double multiplication was a bottleneck. The 
>>> > computation cost is effectively free on both the CPU and GPU and 
>>> > you're seeing pure copying costs. Also, I'm dubious that cublas is 
>>> > doing what you think it is. Can you link me to the source code for 
>>> > DGEMM?
>>> >
>>> > I show all of this in my talk, with explanations, I can't stress 
>>> > enough how much I recommend that you watch it if you want to 
>>> > understand high performance hardware acceleration for linear 
>>> > algebra :-)
>>> >
>>> > On 27 Feb 2015 01:42, "Xiangrui Meng" <mengxr@gmail.com> wrote:
>>> >>
>>> >> The copying overhead should be quadratic on n, while the 
>>> >> computation cost is cubic on n. I can understand that 
>>> >> netlib-cublas is slower than netlib-openblas on small problems.
>>> >> But I'm surprised to see that it is still 20x slower on 
>>> >> 10000x10000. I did the following on a g2.2xlarge instance with BIDMat:
>>> >>
>>> >> val n = 10000
>>> >>
>>> >> val f = rand(n, n)
>>> >> flip; f*f; val rf = flop
>>> >>
>>> >> flip; val g = GMat(n, n); g.copyFrom(f); (g*g).toFMat(null); val 
>>> >> rg = flop
>>> >>
>>> >> flip; g*g; val rgg = flop
>>> >>
>>> >> The CPU version finished in 12 seconds.
>>> >> The CPU->GPU->CPU version finished in 2.2 seconds.
>>> >> The GPU version finished in 1.7 seconds.
>>> >>
>>> >> I'm not sure whether my CPU->GPU->CPU code simulates the 
>>> >> netlib-cublas path. But based on the result, the data copying 
>>> >> overhead is definitely not as big as 20x at n = 10000.
>>> >>
>>> >> Best,
>>> >> Xiangrui
>>> >>
>>> >>
>>> >> On Thu, Feb 26, 2015 at 2:21 PM, Sam Halliday 
>>> >> <sam.halliday@gmail.com>
>>> >> wrote:
>>> >> > I've had some email exchanges with the author of BIDMat: it 
>>> >> > does exactly what you need to get the GPU benefit and writes 
>>> >> > higher level algorithms entirely in the GPU kernels so that the 
>>> >> > memory stays there as long as possible. The restriction with 
>>> >> > this approach is that it is only offering high-level algorithms 
>>> >> > so is not a toolkit for applied mathematics research and 
>>> >> > development
>>> >> > --- but it works well as a toolkit for higher level analysis 
>>> >> > (e.g. for analysts and practitioners).
>>> >> >
>>> >> > I believe BIDMat's approach is the best way to get performance 
>>> >> > out of GPU hardware at the moment but I also have strong 
>>> >> > evidence to suggest that the hardware will catch up and the 
>>> >> > memory transfer costs between CPU/GPU will disappear meaning 
>>> >> > that there will be no need for custom GPU kernel 
>>> >> > implementations. i.e. please continue to use BLAS primitives 
>>> >> > when writing new algorithms and only go to the GPU for an 
>>> >> > alternative optimised implementation.
>>> >> >
>>> >> > Note that CUDA and cuBLAS are *not* BLAS. They are BLAS-like, 
>>> >> > and offer an API that looks like BLAS but takes pointers to 
>>> >> > special regions in the GPU memory region. Somebody has written 
>>> >> > a wrapper around CUDA to create a proper BLAS library but it 
>>> >> > only gives marginal performance over the CPU because of the 
>>> >> > memory transfer overhead.
>>> >> >
>>> >> > This slide from my talk
>>> >> >
>>> >> >   http://fommil.github.io/scalax14/#/11/2
>>> >> >
>>> >> > says it all. X axis is matrix size, Y axis is logarithmic time 
>>> >> > to do DGEMM. Black line is the "cheating" time for the GPU and 
>>> >> > the green line is after copying the memory to/from the GPU 
>>> >> > memory. APUs have the potential to eliminate the green line.
>>> >> >
>>> >> > Best regards,
>>> >> > Sam
>>> >> >
>>> >> >
>>> >> >
>>> >> > "Ulanov, Alexander" <alexander.ulanov@hp.com> writes:
>>> >> >
>>> >> >> Evan, thank you for the summary. I would like to add some more 
>>> >> >> observations. The GPU that I used is 2.5 times cheaper than 
>>> >> >> the CPU
>>> >> >> ($250 vs
>>> >> >> $100). They both are 3 years old. I've also did a small test 
>>> >> >> with modern hardware, and the new GPU nVidia Titan was 
>>> >> >> slightly more than 1 order of magnitude faster than Intel 
>>> >> >> E5-2650 v2 for the same tests. However, it costs as much as 
>>> >> >> CPU ($1200). My takeaway is that GPU is making a better price/value progress.
>>> >> >>
>>> >> >>
>>> >> >>
>>> >> >> Xiangrui, I was also surprised that BIDMat-cuda was faster 
>>> >> >> than netlib-cuda and the most reasonable explanation is that 
>>> >> >> it holds the result in GPU memory, as Sam suggested. At the 
>>> >> >> same time, it is OK because you can copy the result back from 
>>> >> >> GPU only when needed. However, to be sure, I am going to ask 
>>> >> >> the developer of BIDMat on his upcoming talk.
>>> >> >>
>>> >> >>
>>> >> >>
>>> >> >> Best regards, Alexander
>>> >> >>
>>> >> >>
>>> >> >> From: Sam Halliday [mailto:sam.halliday@gmail.com]
>>> >> >> Sent: Thursday, February 26, 2015 1:56 PM
>>> >> >> To: Xiangrui Meng
>>> >> >> Cc: dev@spark.apache.org; Joseph Bradley; Ulanov, Alexander; Evan R.
>>> >> >> Sparks
>>> >> >> Subject: Re: Using CUDA within Spark / boosting linear algebra
>>> >> >>
>>> >> >>
>>> >> >> Btw, I wish people would stop cheating when comparing CPU and 
>>> >> >> GPU timings for things like matrix multiply :-P
>>> >> >>
>>> >> >> Please always compare apples with apples and include the time 
>>> >> >> it takes to set up the matrices, send it to the processing 
>>> >> >> unit, doing the calculation AND copying it back to where you 
>>> >> >> need to see the results.
>>> >> >>
>>> >> >> Ignoring this method will make you believe that your GPU is 
>>> >> >> thousands of times faster than it really is. Again, jump to 
>>> >> >> the end of my talk for graphs and more discussion....  
>>> >> >> especially the bit about me being keen on funding to 
>>> >> >> investigate APU hardware further ;-) (I believe it will solve 
>>> >> >> the
>>> >> >> problem)
>>> >> >> On 26 Feb 2015 21:16, "Xiangrui Meng"
>>> >> >> <mengxr@gmail.com<mailto:mengxr@gmail.com>> wrote:
>>> >> >> 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>> 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>>
>>> >> >>> 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/1aRm2IADRfXQV7G2vrcVh
>>> >> >>>> 4 
>>> >> >>>> StF50uZHl6kmAJeaZZggr0/pubchart?oid=1899767119&format=intera
>>> >> >>>> c
>>> >> >>>> tive
>>> >> >>>>
>>> >> >>>> 1) A properly configured GPU matrix multiply implementation (e.g.
>>> >> >>>> BIDMat+GPU) can provide substantial (but less than an order 
>>> >> >>>> BIDMat+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<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-y
>>> >> >>>>> u m-repo==netlib-cublas>netlib-blas>f2jblas
>>> >> >>>>>
>>> >> >>>>> Below is the link to the spreadsheet with full results.
>>> >> >>>>>
>>> >> >>>>>
>>> >> >>>>>
>>> >> >>>>> https://docs.google.com/spreadsheets/d/1lWdVSuSragOobb0A_oe
>>> >> >>>>> o uQgHUMx378T9J5r7kwKSPkY/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>
>>> >> >>>>> 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+Breeze+Netlib-f2jblas
>>> >> >>>>> Breeze+|
>>> >> >>>>>
>>> >> >>>>>
>>> >> >>>>> +-----------------------------------------------------------------------+
>>> >> >>>>> |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>
>>> >> >>>>> ]
>>> >> >>>>> Sent: Monday, February 09, 2015 6:06 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
>>> >> >>>>>
>>> >> >>>>> 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><mai
>>> >> >>>>> lto: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>>]
>>> >> >>>>> Sent: Friday, February 06, 2015 5:58 PM
>>> >> >>>>>
>>> >> >>>>> To: Ulanov, Alexander
>>> >> >>>>> Cc: Joseph Bradley;
>>> >> >>>>>
>>> >> >>>>> dev@spark.apache.org<mailto:dev@spark.apache.org><mailto:de
>>> >> >>>>> v@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><mai
>>> >> >>>>> lto: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.F2
>>> >> >>>>> jBLAS,
>>> >> >>>>> 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>>]
>>> >> >>>>> Sent: Friday, February 06, 2015 5:19 PM
>>> >> >>>>> To: Ulanov, Alexander
>>> >> >>>>> Cc: Joseph Bradley;
>>> >> >>>>>
>>> >> >>>>> dev@spark.apache.org<mailto:dev@spark.apache.org><mailto:de
>>> >> >>>>> v@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><mai
>>> >> >>>>> lto: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>>]
>>> >> >>>>> 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:de
>>> >> >>>>> v@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><mai
>>> >> >>>>> lto: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>>]
>>> >> >>>>> Sent: Thursday, February 05, 2015 1:29 PM
>>> >> >>>>> To: Ulanov, Alexander
>>> >> >>>>> Cc:
>>> >> >>>>>
>>> >> >>>>> dev@spark.apache.org<mailto:dev@spark.apache.org><mailto:de
>>> >> >>>>> v@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 
>>> >> >>>>> netlib-java+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>><mailto:
>>> >> >>>>>
>>> >> >>>>>
>>> >> >>>>> alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com><mai
>>> >> >>>>> lto: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>>>]
>>> >> >>>>> 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:de
>>> >> >>>>> v@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><mai
>>> >> >>>>> lto: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@spa
>>> >> >>>>> rk.apache.org>><mailto:dev-unsubscribe@spark.apache.org<mai
>>> >> >>>>> lto:dev-unsubscribe@spark.apache.org>
>>> >> >>>>>
>>> >> >>>>>
>>> >> >>>>> <mailto:dev-unsubscribe@spark.apache.org<mailto:dev-unsubsc
>>> >> >>>>> ribe@spark.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>
>>> >> >>>>> >>
>>> >> >>>>>
>>> >> >>>>>
>>> >> >>>>>
>>> >> >>>>>
>>> >> >>>>
>>> >> >
>>> >> > --
>>> >> > Best regards,
>>> >> > Sam
>>> >> >
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