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 >> > >> > >
