pmap

问题 Why doesn't this do what I think it should: benjamin@benjamin-VirtualBox:~$ julia -p 3 julia> @everywhere(function foom(bar::Vector{Any}, k::Integer) println(repeat(bar[2],bar[1])); return bar; end) julia> foo={{1,"a"},{2,"b"},{3,"c"}} julia> pmap(foom, foo, 5) From worker 2: a 1-element Array{Any,1}: {1,"a"} and that is all it outputs. I was expecting pmap to iterate through each tuple in foo and call foom on it. EDIT: It works correctly when I don't pass other arguments in: julia>

问题 I am building a microsimulation model in Julia. I have built the structure of my function and it runs great for for 1 "person". I'd like to write the script to run 100000+ people through the model and save the results in one location. Eventually I'd like to execute this in parallel. Below I have included a simple working version of the code with dummy probabilities. using Distributions # Microsim function function MicroSim(start_age, stages) stage = 0 age = start_age # Set all trackers to 0

问题 I'm not a software/scripting folk myself so struggling to understand what is happening here: watch -n 0.2 'ps -p $(pgrep -d',' -x snmpd) -o rss= | awk '{ i += $1 } END { print i }'' Basically I am wanting to print the Resident Set Size value of my snmp daemon 5 times a second (for fair resolution). I then intend on building from this to redirect the output to a text file for later analysis where I can put the data into a graph for instance. The trouble I have here is that I can run the

问题 I'm trying to better understand how pmap() works within dataframes, and I get a surprising result when applying pmap() to compute means from several columns. mtcars %>% mutate(comp_var = pmap_dbl(list(vs, am, cyl), mean)) %>% select(comp_var, vs, am, cyl) In the above example, comp_var is equal to the value of vs in its row, rather than the mean of the three variables in a given row. I know that I could get accurate results for comp_var using ... mtcars %>% rowwise() %>% mutate(comp_var =

问题 JDK version is hotspot 8u_45 I researched native memory of my java process. The native memory even consumes more space than heap. However there are many native memory blocks which confuses me. The result of pmap -x for example: 00007f8128000000 65508 25204 25204 rw--- [ anon ] 00007f812bff9000 28 0 0 ----- [ anon ] 00007f812c000000 65508 24768 24768 rw--- [ anon ] 00007f812fff9000 28 0 0 ----- [ anon ] 00007f8130000000 65508 25532 25532 rw--- [ anon ] 00007f8133ff9000 28 0 0 ----- [ anon ]

问题 I have a java application running on Java 8 inside a docker container. The process starts a Jetty 9 server and a web application is being deployed. The following JVM options are passed: -Xms768m -Xmx768m . Recently I noticed that the process consumes a lot of memory: $ ps aux 1 USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND app 1 0.1 48.9 5268992 2989492 ? Ssl Sep23 4:47 java -server ... $ pmap -x 1 Address Kbytes RSS Dirty Mode Mapping ... total kB 5280504 2994384 2980776 $ jcmd 1 VM

问题 I have a java application running on Java 8 inside a docker container. The process starts a Jetty 9 server and a web application is being deployed. The following JVM options are passed: -Xms768m -Xmx768m . Recently I noticed that the process consumes a lot of memory: $ ps aux 1 USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND app 1 0.1 48.9 5268992 2989492 ? Ssl Sep23 4:47 java -server ... $ pmap -x 1 Address Kbytes RSS Dirty Mode Mapping ... total kB 5280504 2994384 2980776 $ jcmd 1 VM

问题 I am trying to speed up filling in a matrix for a dynamic programming problem in Julia (v0.6.0), and I can't seem to get much extra speed from using pmap . This is related to this question I posted almost a year ago: Filling a matrix using parallel processing in Julia. I was able to speed up serial processing with some great help then, and I'm now trying to get extra speed from parallel processing tools in Julia. For the serial processing case, I was using a 3-dimensional matrix (essentially

问题 I'm trying to understand memory usage of some php processes. I've tried using both get_memory_usage() and pmap , but the results seem to be off by about one order of magnitude. I've tried with both memory_get_usage() and memory_get_usage(true) , as well as memory_get_peak_usage(true) , but even with memory_get_peak_usage(true) (the largest of all three varieties) there is still a huge with what is reported via pmap. More specifically, calling memory_get_peak_usage(true) every minute within my