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 <men...@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 <jos...@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.spa...@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/1aRm2IADRfXQV7G2vrcVh4StF50uZHl6kmAJeaZZggr0/pubchart?oid=1899767119&format=interactive >>> >>> 1) A properly configured GPU matrix multiply implementation (e.g. >>> BIDMat+GPU) can provide substantial (but less than an order of magnitude) >>> benefit over a well-tuned CPU implementation (e.g. BIDMat+MKL or >>> netlib-java+openblas-compiled). >>> 2) A poorly tuned CPU implementation can be 1-2 orders of magnitude worse >>> than a well-tuned CPU implementation, particularly for larger matrices. >>> (netlib-f2jblas or netlib-ref) This is not to pick on netlib - this >>> basically agrees with the authors own benchmarks ( >>> https://github.com/fommil/netlib-java) >>> >>> I think that most of our users are in a situation where using GPUs may not >>> be practical - although we could consider having a good GPU backend >>> available as an option. However, *ALL* users of MLlib could benefit >>> (potentially tremendously) from using a well-tuned CPU-based BLAS >>> implementation. Perhaps we should consider updating the mllib guide with a >>> more complete section for enabling high performance binaries on OSX and >>> Linux? Or better, figure out a way for the system to fetch these >>> automatically. >>> >>> - Evan >>> >>> >>> >>> On Thu, Feb 12, 2015 at 4:18 PM, Ulanov, Alexander < >>> alexander.ula...@hp.com> wrote: >>> >>>> Just to summarize this thread, I was finally able to make all performance >>>> comparisons that we discussed. It turns out that: >>>> BIDMat-cublas>>BIDMat >>>> MKL==netlib-mkl==netlib-openblas-compiled>netlib-openblas-yum-repo==netlib-cublas>netlib-blas>f2jblas >>>> >>>> Below is the link to the spreadsheet with full results. >>>> >>>> https://docs.google.com/spreadsheets/d/1lWdVSuSragOobb0A_oeouQgHUMx378T9J5r7kwKSPkY/edit?usp=sharing >>>> >>>> One thing still needs exploration: does BIDMat-cublas perform copying >>>> to/from machine’s RAM? >>>> >>>> -----Original Message----- >>>> From: Ulanov, Alexander >>>> Sent: Tuesday, February 10, 2015 2:12 PM >>>> To: Evan R. Sparks >>>> Cc: Joseph Bradley; dev@spark.apache.org >>>> Subject: RE: Using CUDA within Spark / boosting linear algebra >>>> >>>> Thanks, Evan! It seems that ticket was marked as duplicate though the >>>> original one discusses slightly different topic. I was able to link netlib >>>> with MKL from BIDMat binaries. Indeed, MKL is statically linked inside a >>>> 60MB library. >>>> >>>> |A*B size | BIDMat MKL | Breeze+Netlib-MKL from BIDMat| >>>> Breeze+Netlib-OpenBlas(native system)| Breeze+Netlib-f2jblas | >>>> +-----------------------------------------------------------------------+ >>>> |100x100*100x100 | 0,00205596 | 0,000381 | 0,03810324 | 0,002556 | >>>> |1000x1000*1000x1000 | 0,018320947 | 0,038316857 | 0,51803557 >>>> |1,638475459 | >>>> |10000x10000*10000x10000 | 23,78046632 | 32,94546697 |445,0935211 | >>>> 1569,233228 | >>>> >>>> It turn out that pre-compiled MKL is faster than precompiled OpenBlas on >>>> my machine. Probably, I’ll add two more columns with locally compiled >>>> openblas and cuda. >>>> >>>> Alexander >>>> >>>> From: Evan R. Sparks [mailto:evan.spa...@gmail.com] >>>> Sent: Monday, February 09, 2015 6:06 PM >>>> To: Ulanov, Alexander >>>> Cc: Joseph Bradley; dev@spark.apache.org >>>> Subject: Re: Using CUDA within Spark / boosting linear algebra >>>> >>>> Great - perhaps we can move this discussion off-list and onto a JIRA >>>> ticket? (Here's one: https://issues.apache.org/jira/browse/SPARK-5705) >>>> >>>> It seems like this is going to be somewhat exploratory for a while (and >>>> there's probably only a handful of us who really care about fast linear >>>> algebra!) >>>> >>>> - Evan >>>> >>>> On Mon, Feb 9, 2015 at 4:48 PM, Ulanov, Alexander < >>>> alexander.ula...@hp.com<mailto:alexander.ula...@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.spa...@gmail.com<mailto: >>>> evan.spa...@gmail.com>] >>>> Sent: Friday, February 06, 2015 5:58 PM >>>> >>>> To: Ulanov, Alexander >>>> Cc: Joseph Bradley; dev@spark.apache.org<mailto:dev@spark.apache.org> >>>> Subject: Re: Using CUDA within Spark / boosting linear algebra >>>> >>>> I would build OpenBLAS yourself, since good BLAS performance comes from >>>> getting cache sizes, etc. set up correctly for your particular hardware - >>>> this is often a very tricky process (see, e.g. ATLAS), but we found that on >>>> relatively modern Xeon chips, OpenBLAS builds quickly and yields >>>> performance competitive with MKL. >>>> >>>> To make sure the right library is getting used, you have to make sure >>>> it's first on the search path - export >>>> LD_LIBRARY_PATH=/path/to/blas/library.so will do the trick here. >>>> >>>> For some examples of getting netlib-java setup on an ec2 node and some >>>> example benchmarking code we ran a while back, see: >>>> https://github.com/shivaram/matrix-bench >>>> >>>> In particular - build-openblas-ec2.sh shows you how to build the library >>>> and set up symlinks correctly, and scala/run-netlib.sh shows you how to get >>>> the path setup and get that library picked up by netlib-java. >>>> >>>> In this way - you could probably get cuBLAS set up to be used by >>>> netlib-java as well. >>>> >>>> - Evan >>>> >>>> On Fri, Feb 6, 2015 at 5:43 PM, Ulanov, Alexander < >>>> alexander.ula...@hp.com<mailto:alexander.ula...@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.spa...@gmail.com<mailto: >>>> evan.spa...@gmail.com>] >>>> Sent: Friday, February 06, 2015 5:19 PM >>>> To: Ulanov, Alexander >>>> Cc: Joseph Bradley; dev@spark.apache.org<mailto:dev@spark.apache.org> >>>> >>>> Subject: Re: Using CUDA within Spark / boosting linear algebra >>>> >>>> Getting breeze to pick up the right blas library is critical for >>>> performance. I recommend using OpenBLAS (or MKL, if you already have it). >>>> It might make sense to force BIDMat to use the same underlying BLAS library >>>> as well. >>>> >>>> On Fri, Feb 6, 2015 at 4:42 PM, Ulanov, Alexander < >>>> alexander.ula...@hp.com<mailto:alexander.ula...@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:jos...@databricks.com<mailto: >>>> jos...@databricks.com>] >>>> Sent: Thursday, February 05, 2015 5:29 PM >>>> To: Ulanov, Alexander >>>> Cc: Evan R. Sparks; dev@spark.apache.org<mailto:dev@spark.apache.org> >>>> Subject: Re: Using CUDA within Spark / boosting linear algebra >>>> >>>> Hi Alexander, >>>> >>>> Using GPUs with Spark would be very exciting. Small comment: Concerning >>>> your question earlier about keeping data stored on the GPU rather than >>>> having to move it between main memory and GPU memory on each iteration, I >>>> would guess this would be critical to getting good performance. If you >>>> could do multiple local iterations before aggregating results, then the >>>> cost of data movement to the GPU could be amortized (and I believe that is >>>> done in practice). Having Spark be aware of the GPU and using it as >>>> another part of memory sounds like a much bigger undertaking. >>>> >>>> Joseph >>>> >>>> On Thu, Feb 5, 2015 at 4:59 PM, Ulanov, Alexander < >>>> alexander.ula...@hp.com<mailto:alexander.ula...@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.spa...@gmail.com<mailto: >>>> evan.spa...@gmail.com>] >>>> Sent: Thursday, February 05, 2015 1:29 PM >>>> To: Ulanov, Alexander >>>> Cc: dev@spark.apache.org<mailto:dev@spark.apache.org> >>>> Subject: Re: Using CUDA within Spark / boosting linear algebra >>>> >>>> I'd be surprised of BIDMat+OpenBLAS was significantly faster than >>>> netlib-java+OpenBLAS, but if it is much faster it's probably due to data >>>> layout and fewer levels of indirection - it's definitely a worthwhile >>>> experiment to run. The main speedups I've seen from using it come from >>>> highly optimized GPU code for linear algebra. I know that in the past Canny >>>> has gone as far as to write custom GPU kernels for performance-critical >>>> regions of code.[1] >>>> >>>> BIDMach is highly optimized for single node performance or performance on >>>> small clusters.[2] Once data doesn't fit easily in GPU memory (or can be >>>> batched in that way) the performance tends to fall off. Canny argues for >>>> hardware/software codesign and as such prefers machine configurations that >>>> are quite different than what we find in most commodity cluster nodes - >>>> e.g. 10 disk cahnnels and 4 GPUs. >>>> >>>> In contrast, MLlib was designed for horizontal scalability on commodity >>>> clusters and works best on very big datasets - order of terabytes. >>>> >>>> For the most part, these projects developed concurrently to address >>>> slightly different use cases. That said, there may be bits of BIDMach we >>>> could repurpose for MLlib - keep in mind we need to be careful about >>>> maintaining cross-language compatibility for our Java and Python-users, >>>> though. >>>> >>>> - Evan >>>> >>>> [1] - http://arxiv.org/abs/1409.5402 >>>> [2] - http://eecs.berkeley.edu/~hzhao/papers/BD.pdf >>>> >>>> On Thu, Feb 5, 2015 at 1:00 PM, Ulanov, Alexander < >>>> alexander.ula...@hp.com<mailto:alexander.ula...@hp.com><mailto: >>>> alexander.ula...@hp.com<mailto:alexander.ula...@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.spa...@gmail.com<mailto: >>>> evan.spa...@gmail.com><mailto:evan.spa...@gmail.com<mailto: >>>> evan.spa...@gmail.com>>] >>>> Sent: Thursday, February 05, 2015 12:09 PM >>>> To: Ulanov, Alexander >>>> Cc: dev@spark.apache.org<mailto:dev@spark.apache.org><mailto: >>>> dev@spark.apache.org<mailto:dev@spark.apache.org>> >>>> Subject: Re: Using CUDA within Spark / boosting linear algebra >>>> >>>> I'd expect that we can make GPU-accelerated BLAS faster than CPU blas in >>>> many cases. >>>> >>>> You might consider taking a look at the codepaths that BIDMat ( >>>> https://github.com/BIDData/BIDMat) takes and comparing them to >>>> netlib-java/breeze. John Canny et. al. have done a bunch of work optimizing >>>> to make this work really fast from Scala. I've run it on my laptop and >>>> compared to MKL and in certain cases it's 10x faster at matrix multiply. >>>> There are a lot of layers of indirection here and you really want to avoid >>>> data copying as much as possible. >>>> >>>> We could also consider swapping out BIDMat for Breeze, but that would be >>>> a big project and if we can figure out how to get breeze+cublas to >>>> comparable performance that would be a big win. >>>> >>>> On Thu, Feb 5, 2015 at 11:55 AM, Ulanov, Alexander < >>>> alexander.ula...@hp.com<mailto:alexander.ula...@hp.com><mailto: >>>> alexander.ula...@hp.com<mailto:alexander.ula...@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-unsubscr...@spark.apache.org<mailto: >>>> dev-unsubscr...@spark.apache.org><mailto:dev-unsubscr...@spark.apache.org >>>> <mailto:dev-unsubscr...@spark.apache.org>> >>>> For additional commands, e-mail: dev-h...@spark.apache.org<mailto: >>>> dev-h...@spark.apache.org><mailto:dev-h...@spark.apache.org<mailto: >>>> dev-h...@spark.apache.org>> >>>> >>>> >>>> >>>> >>> -- Best regards, Sam --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
