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] Sent: Friday, February 06, 2015 5:58 PM To: Ulanov, Alexander Cc: Joseph Bradley; 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>>
