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...<- rbind(ds.tmp, ds) } ________ end example 1 __________ Stepping into details I found the following (comment shows RAM usage after this line was executed): load(myFile) # 40MB (19MB for R itself) ds.tmp <- ds # 40MB; => only a pointer seems to be copied x<-rbind(ds.tmp, ds) # 198MB x<-rbind(ds.tmp, ds) # 233MB; the same instruction a second time leads to # 35MB more RAM usage - why? Now I played around, but I couldn't find a solution. For example I bound each dataframe step by step and removed the variables and cleared memory, but I still need 1140MB(!) RAM:...

...and memory plugins, and I'm using fio with 8 threads to test this against the Linux kernel client. Without multi-conn: memory: read: IOPS=52.8k, BW=206MiB/s (216MB/s)(24.2GiB/120002msec) write: IOPS=52.8k, BW=206MiB/s (216MB/s)(24.2GiB/120002msec) file: read: IOPS=48.3k, BW=189MiB/s (198MB/s)(22.1GiB/120001msec) write: IOPS=48.3k, BW=189MiB/s (198MB/s)(22.1GiB/120001msec) With multi-conn (-C 8): memory: read: IOPS=103k, BW=401MiB/s (420MB/s)(46.0GiB/120002msec) write: IOPS=103k, BW=401MiB/s (420MB/s)(46.0GiB/120002msec) file: read: IOPS=49.2k, BW=192MiB/s (202MB/s)(22.5G...

On Fri, Jan 04, 2019 at 05:26:07PM -0600, Eric Blake wrote: > On 1/4/19 4:08 PM, Richard W.M. Jones wrote: > > First thing to say is that I need to do a *lot* more testing on this, > > so this is just an early peek. In particular, although it passed > > ‘make check && make check-valgrind’ I have *not* tested it against a > > multi-conn-aware client such as the

We upgraded from a 10/100Mbs to a 2 100/1000 bonding. We notice the speeds of NFS to be around 70-80Mb/sec. Which is slow, especially with bonding. I was wondering if we need to tune anything special with the Network and NFS. Does anyone have any experience with this? TIA