search for: 7500000

...ipulation of large data objects. For example, I have a function which receives a (large) numeric matrix, matches against more data (maybe imported from MySql) and returns a large list structure for further analysis. A typical call may look like this . > myInputData <- matrix(sample(1:100, 7500000, T), nrow=5000) > myPortfolio <- createPortfolio(myInputData) It seems I can only repeat this code process 2/3 times before I have to restart R (to get the memory back). I use the same object names (myInputData and myPortfolio) each time, so I am not create more large objects .. I think...

...eing dealt with. Metadata node counts stabilize much earlier in the process. The rest of the numbers are based on counting `MDNodes` and their respective `MDNodeOperands`, and multiplying by the cost of their operands. Here's a dump from around the peak metadata node count: LineTables = 7500000[30000000], InlinedLineTables = 6756182, Directives = 7611669[42389128], Arrays = 570609[577447], Others = 1176556[5133065] Tag = 256, Count = 554992, Ops = 2531428, Name = DW_TAG_auto_variable Tag = 16647, Count = 988, Ops = 4940, Name = DW_TAG_GNU_template_parameter_pack...

In r219010, I merged integer and string fields into a single header field. By reducing the number of metadata operands used in debug info, this saved 2.2GB on an `llvm-lto` bootstrap. I've done some profiling of DW_TAGs to see what parts of PR17891 and PR17892 to tackle next, and I've concluded that they will be insufficient. Instead, I'd like to implement a more aggressive plan,