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.halli...@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" <men...@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.halli...@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.ula...@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.halli...@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" >> >> <men...@gmail.com<mailto:men...@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 >> >> <jos...@databricks.com<mailto: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<mailto: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<mailto: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<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.spa...@gmail.com<mailto:evan.spa...@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.ula...@hp.com<mailto:alexander.ula...@hp.com><mailto: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><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><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.ula...@hp.com<mailto:alexander.ula...@hp.com><mailto: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><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><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.ula...@hp.com<mailto:alexander.ula...@hp.com><mailto: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><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><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.ula...@hp.com<mailto:alexander.ula...@hp.com><mailto: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><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><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 >> >>>>> 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>><mailto: >> >>>>> >> >>>>> 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>><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>><mailto: >> >>>>> >> >>>>> 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>><mailto: >> >>>>> >> >>>>> 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>><mailto: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>><mailto: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
