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 >
