Not yet since it's running in the cluster. Will run locally with profiler. Thanks for help.
Sincerely, DB Tsai ------------------------------------------------------- My Blog: https://www.dbtsai.com LinkedIn: https://www.linkedin.com/in/dbtsai On Wed, Apr 23, 2014 at 10:22 PM, David Hall <d...@cs.berkeley.edu> wrote: > On Wed, Apr 23, 2014 at 10:18 PM, DB Tsai <dbt...@dbtsai.com> wrote: >> >> ps, it doesn't make sense to have weight and gradient sparse unless >> with strong L1 penalty. > > > Sure, I was just checking the obvious things. Have you run it through it a > profiler to see where the problem is? > > >> >> >> Sincerely, >> >> DB Tsai >> ------------------------------------------------------- >> My Blog: https://www.dbtsai.com >> LinkedIn: https://www.linkedin.com/in/dbtsai >> >> >> On Wed, Apr 23, 2014 at 10:17 PM, DB Tsai <dbt...@dbtsai.com> wrote: >> > In mllib, the weight, and gradient are dense. Only feature is sparse. >> > >> > Sincerely, >> > >> > DB Tsai >> > ------------------------------------------------------- >> > My Blog: https://www.dbtsai.com >> > LinkedIn: https://www.linkedin.com/in/dbtsai >> > >> > >> > On Wed, Apr 23, 2014 at 10:16 PM, David Hall <d...@cs.berkeley.edu> >> > wrote: >> >> Was the weight vector sparse? The gradients? Or just the feature >> >> vectors? >> >> >> >> >> >> On Wed, Apr 23, 2014 at 10:08 PM, DB Tsai <dbt...@dbtsai.com> wrote: >> >>> >> >>> The figure showing the Log-Likelihood vs Time can be found here. >> >>> >> >>> >> >>> >> >>> https://github.com/dbtsai/spark-lbfgs-benchmark/raw/fd703303fb1c16ef5714901739154728550becf4/result/a9a11M.pdf >> >>> >> >>> Let me know if you can not open it. >> >>> >> >>> Sincerely, >> >>> >> >>> DB Tsai >> >>> ------------------------------------------------------- >> >>> My Blog: https://www.dbtsai.com >> >>> LinkedIn: https://www.linkedin.com/in/dbtsai >> >>> >> >>> >> >>> On Wed, Apr 23, 2014 at 9:34 PM, Shivaram Venkataraman < >> >>> shiva...@eecs.berkeley.edu> wrote: >> >>> >> >>> > I don't think the attachment came through in the list. Could you >> >>> > upload >> >>> > the results somewhere and link to them ? >> >>> > >> >>> > >> >>> > On Wed, Apr 23, 2014 at 9:32 PM, DB Tsai <dbt...@dbtsai.com> wrote: >> >>> > >> >>> >> 123 features per rows, and in average, 89% are zeros. >> >>> >> On Apr 23, 2014 9:31 PM, "Evan Sparks" <evan.spa...@gmail.com> >> >>> >> wrote: >> >>> >> >> >>> >> > What is the number of non zeroes per row (and number of features) >> >>> >> > in >> >>> >> > the >> >>> >> > sparse case? We've hit some issues with breeze sparse support in >> >>> >> > the >> >>> >> past >> >>> >> > but for sufficiently sparse data it's still pretty good. >> >>> >> > >> >>> >> > > On Apr 23, 2014, at 9:21 PM, DB Tsai <dbt...@stanford.edu> >> >>> >> > > wrote: >> >>> >> > > >> >>> >> > > Hi all, >> >>> >> > > >> >>> >> > > I'm benchmarking Logistic Regression in MLlib using the newly >> >>> >> > > added >> >>> >> > optimizer LBFGS and GD. I'm using the same dataset and the same >> >>> >> methodology >> >>> >> > in this paper, http://www.csie.ntu.edu.tw/~cjlin/papers/l1.pdf >> >>> >> > > >> >>> >> > > I want to know how Spark scale while adding workers, and how >> >>> >> optimizers >> >>> >> > and input format (sparse or dense) impact performance. >> >>> >> > > >> >>> >> > > The benchmark code can be found here, >> >>> >> > https://github.com/dbtsai/spark-lbfgs-benchmark >> >>> >> > > >> >>> >> > > The first dataset I benchmarked is a9a which only has 2.2MB. I >> >>> >> > duplicated the dataset, and made it 762MB to have 11M rows. This >> >>> >> > dataset >> >>> >> > has 123 features and 11% of the data are non-zero elements. >> >>> >> > > >> >>> >> > > In this benchmark, all the dataset is cached in memory. >> >>> >> > > >> >>> >> > > As we expect, LBFGS converges faster than GD, and at some >> >>> >> > > point, no >> >>> >> > matter how we push GD, it will converge slower and slower. >> >>> >> > > >> >>> >> > > However, it's surprising that sparse format runs slower than >> >>> >> > > dense >> >>> >> > format. I did see that sparse format takes significantly smaller >> >>> >> > amount >> >>> >> of >> >>> >> > memory in caching RDD, but sparse is 40% slower than dense. I >> >>> >> > think >> >>> >> sparse >> >>> >> > should be fast since when we compute x wT, since x is sparse, we >> >>> >> > can >> >>> >> > do >> >>> >> it >> >>> >> > faster. I wonder if there is anything I'm doing wrong. >> >>> >> > > >> >>> >> > > The attachment is the benchmark result. >> >>> >> > > >> >>> >> > > Thanks. >> >>> >> > > >> >>> >> > > Sincerely, >> >>> >> > > >> >>> >> > > DB Tsai >> >>> >> > > ------------------------------------------------------- >> >>> >> > > My Blog: https://www.dbtsai.com >> >>> >> > > LinkedIn: https://www.linkedin.com/in/dbtsai >> >>> >> > >> >>> >> >> >>> > >> >>> > >> >> >> >> > >
