Ping: First: For anyone getting started with parallel programming in R, you should really read: http://trg.apbionet.org/euasiagrid/docs/parallelR.notes.pdf
Note that wherever it says "snow" you should swap in the package "parallel". I'm a big proponent of the "foreach" package, which generalizes parallel processing across different parallel infrastructures, and more clearly follows "for" - type looping (*apply statement are, in general, much more difficult to understand). Second: if you are doing high I/O, low CPU processes (lots of read/write), in general parallel processing doesn't help unless you are on a RAIDed or a parallel filesystem. Third, as Julian points out: it is easier to start with what you are trying to do, rather than "fix my script". What do you mean by: "The purpose is to generate weather information(tmin,tmim,prcp) for every 4 KM (every 4KM there is a point represent that grid) for 32 years throughout Contingent U.S." ? Are you interpolating data? Downscaling it? Extracting statistics per-state? Summarizing it? All of these have very different interpretations, and different approaches (not all of which are parallel processing). --j On Mon, Feb 3, 2014 at 8:58 AM, Julian M. Burgos <[email protected]> wrote: > Yes, but why you are quering each point individually (one by one) in a > loop, and why you are writing every single data point in a different > file? Loops are very slow in R, and reading/writing from/to a > disk file is also a very slow process. Do it several hundred thousand > times and it is no surprise that this takes forever. I do not have time to > examine > your code either (next time provide a minimally reproducible example, > not a long script), but it seems that you have some netCDF files with > environmental data and a shapefile with locations, and you want to get > the environmental data for each location. If this is what you want to > do, you should: > > a) Read the shapefiles into a SpatialPoints object. > b) Use the raster package and read the netCDF rasters into a rasterbrick > object. > c) Use the over function and do an overlay between the points and the > rasterbrick. You will get a SpaitalPointsDataFrame object, with the > data associated to each point. > d) Save the data into a single file. > > This should take some time, say 20-30 minutes perhaps, maybe much less, > depending on your > machine speed. > > Julian > > > ping yang writes: > >> Hi, >> >> The purpose is to generate weather information(tmin,tmim,prcp) for every 4 >> KM (every 4KM there is a point represent that grid) for 32 years throughout >> Contingent U.S. >> I want to run several states simultaneously which I want it run them >> parallel. >> >> Thanks, >> >> Ping >> >> >> On Sun, Feb 2, 2014 at 1:45 PM, Julian Burgos <[email protected]> wrote: >> >>> A clear explanation of what you are trying to do would help. I cannot >>> figure out why you need to generate such a huge amount of files! Writing >>> data into a disk is a very slow step. >>> >>> > Hi r-sig-geo, >>> > >>> > >>> > >>> > I am using the following script to extraction weather information for >>> each >>> > point (from a rasterized shapefile for the COUS) which generate about >>> > 460,000 files: >>> > >>> > Originally I read the coordinates from a shapefile: >>> > >>> > Library(rgdal) >>> > >>> > pl <- readOGR(".","US_2_points") >>> > >>> > >>> > >>> > then I used the following code to extract the information from three >>> > NetCDF >>> > files that contains the tmin,tmax and pr for each year : >>> > >>> > for (year in 1981:2012) >>> > >>> > { >>> > >>> > #get the combined netCDF file >>> > >>> > tminfile <- paste("tmin","_",year,".nc",sep='') >>> > >>> > b_tmin <- brick(tminfile,varname='tmin') >>> > >>> > pptfile <- paste("ppt","_",year,".nc",sep='') >>> > >>> > b_ppt <- brick(pptfile,varname='ppt') >>> > >>> > tmaxfile <- paste("tmax","_",year,".nc",sep='') >>> > >>> > b_tmax <- brick(tmaxfile,varname='tmax') >>> > >>> > #Get the first year here!!! >>> > >>> > print(paste("processing year :",year,sep='')) >>> > >>> > for(l in 1:length(pl)) >>> > >>> > { >>> > >>> > v <- NULL >>> > >>> > #generate file with the name convention with >>> > t_n(latitude)w(longitude).txt, 5 digits after point should be work >>> > >>> > filename <- >>> > >>> paste("e:/PRISM/US3/N",round(coordinates(pl[l,])[2],5),"W",abs(round(coordinates(pl[l,])[1],5)),".wth",sep='') >>> > >>> > >>> > print(paste("processing file :",filename,sep='')) >>> > >>> > tmin <- >>> > as.numeric(round(extract(b_tmin,coordinates(pl[l,])),digits=1)) >>> > >>> > tmax <- >>> > as.numeric(round(extract(b_tmax,coordinates(pl[l,])),digits=1)) >>> > >>> > ppt <- >>> > as.numeric(round(extract(b_ppt,coordinates(pl[l,])),digits=2)) >>> > >>> > v <- cbind(tmax,tmin,ppt) >>> > >>> > tablename <- c("tmin","tmax","ppt") >>> > >>> > v <- data.frame(v) >>> > >>> > colnames(v) <- tablename >>> > >>> > v["default"] <- 0 >>> > >>> > v["year"] <- year >>> > >>> > date <- >>> > >>> seq(as.Date(paste(year,"/1/1",sep='')),as.Date(paste(year,"/12/31",sep='')),"days") >>> > >>> > month <- as.numeric(substr(date,6,7)) >>> > >>> > day <- as.numeric(substr(date,9,10)) >>> > >>> > v["month"] <- month >>> > >>> > v["day"] <- day >>> > >>> > v <- v[c("year","month","day","default","tmin","tmax","ppt")] >>> > >>> > #write into a file with the fixed wide format >>> > >>> > if(file.existes(filename)) >>> > >>> > { >>> > >>> > >>> > >>> write.fwf(x=v,filename,append=TRUE,na="NA",rownames=FALSE,colnames=FALSE,width=c(6,3,3,5,5,5,6)) >>> > >>> > } >>> > >>> > else >>> > >>> > { >>> > >>> > >>> > >>> write.fwf(x=v,filename,append=FALSE,na="NA",rownames=FALSE,colnames=FALSE,width=c(6,3,3,5,5,5,6)) >>> > >>> > } >>> > >>> > >>> > >>> > I found it will takes weeks to finish, then I divide the points from the >>> > shape file into each State(to generate a shape file for each state for >>> the >>> > points), and using the following function: >>> > >>> > >>> > >>> > weather4Point <- function(state) >>> > >>> > { >>> > >>> > #get the point file for the state >>> > >>> > setwd("c:/PRISM/US1Points/") >>> > >>> > pl <- readOGR(".",paste("points4_",state,sep='')) >>> > >>> > setwd("e:/PRISM/NetCDF/") >>> > >>> > for (year in 1981:2012) >>> > >>> > { >>> > >>> > #get the combined netCDF file >>> > >>> > tminfile <- paste("tmin","_",year,".nc",sep='') >>> > >>> > b_tmin <- brick(tminfile,varname='tmin') >>> > >>> > pptfile <- paste("ppt","_",year,".nc",sep='') >>> > >>> > b_ppt <- brick(pptfile,varname='ppt') >>> > >>> > tmaxfile <- paste("tmax","_",year,".nc",sep='') >>> > >>> > b_tmax <- brick(tmaxfile,varname='tmax') >>> > >>> > #Get the first year here!!! >>> > >>> > print(paste("processing year :",year,sep='')) >>> > >>> > for(l in 1:length(pl)) >>> > >>> > { >>> > >>> > v <- NULL >>> > >>> > #generate file with the name convention with >>> > t_n(latitude)w(longitude).txt, 5 digits after point should be work >>> > >>> > filename <- >>> > >>> paste("e:/PRISM/US3/N",round(coordinates(pl[l,])[2],5),"W",abs(round(coordinates(pl[l,])[1],5)),".wth",sep='') >>> > >>> > >>> > print(paste("processing file :",filename,sep='')) >>> > >>> > tmin <- >>> > as.numeric(round(extract(b_tmin,coordinates(pl[l,])),digits=1)) >>> > >>> > tmax <- >>> > as.numeric(round(extract(b_tmax,coordinates(pl[l,])),digits=1)) >>> > >>> > ppt <- >>> > as.numeric(round(extract(b_ppt,coordinates(pl[l,])),digits=2)) >>> > >>> > v <- cbind(tmax,tmin,ppt) >>> > >>> > tablename <- c("tmin","tmax","ppt") >>> > >>> > v <- data.frame(v) >>> > >>> > colnames(v) <- tablename >>> > >>> > v["default"] <- 0 >>> > >>> > v["year"] <- year >>> > >>> > date <- >>> > >>> seq(as.Date(paste(year,"/1/1",sep='')),as.Date(paste(year,"/12/31",sep='')),"days") >>> > >>> > month <- as.numeric(substr(date,6,7)) >>> > >>> > day <- as.numeric(substr(date,9,10)) >>> > >>> > v["month"] <- month >>> > >>> > v["day"] <- day >>> > >>> > v <- v[c("year","month","day","default","tmin","tmax","ppt")] >>> > >>> > v >>> > >>> > #print(paste(v), zero.print = ".") >>> > >>> > #write into a file with the APEX format >>> > >>> > if(file.existes(filename)) >>> > >>> > { >>> > >>> > >>> > >>> write.fwf(x=v,filename,append=TRUE,na="NA",rownames=FALSE,colnames=FALSE,width=c(6,3,3,5,5,5,6)) >>> > >>> > } >>> > >>> > else >>> > >>> > { >>> > >>> > >>> > >>> write.fwf(x=v,filename,append=FALSE,na="NA",rownames=FALSE,colnames=FALSE,width=c(6,3,3,5,5,5,6)) >>> > >>> > } >>> > >>> > } >>> > >>> > } >>> > >>> > } >>> > >>> > >>> > >>> > Then using the following code by opening several R session to make this >>> > process "parallel". >>> > >>> > >>> > >>> > States <- c("AL","AZ","AR","CO","CT","DC","FL","GA","ID","IA", >>> > >>> > "IN","KS","KY","LA","MA","ME","MI","MN","MT","NC") >>> > >>> > for (state in States) >>> > >>> > { >>> > >>> > weather4Point(state) >>> > >>> > } >>> > >>> > >>> > >>> > However, I still feel slow when I plan to do for the whole US. I am >>> asking >>> > here is there a better solution to speed up this process(any possibility >>> > to >>> > employ parallel programming in R) ? >>> > >>> > >>> > >>> > Environment information: >>> > >>> > R version 3.0.1 (2013-05-16) >>> > >>> > Platform: x86_64-w64-mingw32/x64 (64-bit) >>> > >>> > Memory: 16 GB >>> > >>> > Processor: Intel(R) Core(tm) i7-3770 CPU @ 3.4Ghz 3.4Ghz >>> > >>> > >>> > >>> > Looking forward to any suggestions? >>> > >>> > >>> > >>> > Regards, >>> > >>> > >>> > >>> > Ping >>> > >>> > [[alternative HTML version deleted]] >>> > >>> > _______________________________________________ >>> > R-sig-Geo mailing list >>> > [email protected] >>> > https://stat.ethz.ch/mailman/listinfo/r-sig-geo >>> > >>> >>> >>> > > > -- > Julian Mariano Burgos, PhD > Hafrannsóknastofnun/Marine Research Institute > Skúlagata 4, 121 Reykjavík, Iceland > Sími/Telephone : +354-5752037 > Bréfsími/Telefax: +354-5752001 > Netfang/Email: [email protected] > > _______________________________________________ > R-sig-Geo mailing list > [email protected] > https://stat.ethz.ch/mailman/listinfo/r-sig-geo -- Jonathan A. Greenberg, PhD Assistant Professor Global Environmental Analysis and Remote Sensing (GEARS) Laboratory Department of Geography and Geographic Information Science University of Illinois at Urbana-Champaign 259 Computing Applications Building, MC-150 605 East Springfield Avenue Champaign, IL 61820-6371 Phone: 217-300-1924 http://www.geog.illinois.edu/~jgrn/ AIM: jgrn307, MSN: [email protected], Gchat: jgrn307, Skype: jgrn3007 _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
