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 > >> > > >> > > > > >
