parallel-processing

问题 I have a lot of packages to install in my pip requirement and I'd like to process them in parallell. I know that, for example, that if I want n parallel jobs from make I have to write make -j n ; is there an equivalent command for pip requirements? Thanks! 回答1: Sometimes pip uses make to build dependencies. If before it starts you set MAKEFLAGS like: export MAKEFLAGS="-j$(nproc)" pip install -r requirements.txt This may help building native dependencies. Note: nproc resovles as the number of

问题 I have a lot of packages to install in my pip requirement and I'd like to process them in parallell. I know that, for example, that if I want n parallel jobs from make I have to write make -j n ; is there an equivalent command for pip requirements? Thanks! 回答1: Sometimes pip uses make to build dependencies. If before it starts you set MAKEFLAGS like: export MAKEFLAGS="-j$(nproc)" pip install -r requirements.txt This may help building native dependencies. Note: nproc resovles as the number of

问题 In Julia v1.1, assume that I have a very large text file (30GB) and I want parallelism (multi-threads) to read eachline, how can I do ? This code is an attempt to do this after checking Julia's documentation on multi-threading, but it's not working at all open("pathtofile", "r") do file # Count number of lines in file seekend(file) fileSize = position(file) seekstart(file) # skip nseekchars first characters of file seek(file, nseekchars) # progress bar, because it's a HUGE file p = Progress

问题 In Julia v1.1, assume that I have a very large text file (30GB) and I want parallelism (multi-threads) to read eachline, how can I do ? This code is an attempt to do this after checking Julia's documentation on multi-threading, but it's not working at all open("pathtofile", "r") do file # Count number of lines in file seekend(file) fileSize = position(file) seekstart(file) # skip nseekchars first characters of file seek(file, nseekchars) # progress bar, because it's a HUGE file p = Progress

问题 In Julia v1.1, assume that I have a very large text file (30GB) and I want parallelism (multi-threads) to read eachline, how can I do ? This code is an attempt to do this after checking Julia's documentation on multi-threading, but it's not working at all open("pathtofile", "r") do file # Count number of lines in file seekend(file) fileSize = position(file) seekstart(file) # skip nseekchars first characters of file seek(file, nseekchars) # progress bar, because it's a HUGE file p = Progress

问题 Inspired by the experimental fuzzy_join function from the statar package I wrote a function myself which combines exact and fuzzy (by string distances) matching. The merging job I have to do is quite big (resulting into multiple string distance matrices with a little bit less than one billion cells) and I had the impression that the fuzzy_join function is not written very efficiently (with regard to memory usage) and the parallelization is implemented in a weird manner (the computation of the

问题 Inspired by the experimental fuzzy_join function from the statar package I wrote a function myself which combines exact and fuzzy (by string distances) matching. The merging job I have to do is quite big (resulting into multiple string distance matrices with a little bit less than one billion cells) and I had the impression that the fuzzy_join function is not written very efficiently (with regard to memory usage) and the parallelization is implemented in a weird manner (the computation of the

问题 Inspired by the experimental fuzzy_join function from the statar package I wrote a function myself which combines exact and fuzzy (by string distances) matching. The merging job I have to do is quite big (resulting into multiple string distance matrices with a little bit less than one billion cells) and I had the impression that the fuzzy_join function is not written very efficiently (with regard to memory usage) and the parallelization is implemented in a weird manner (the computation of the

问题 I am using a weighted Levenshtein distance with the following costs: insertion: 1 deletion: 1 replacement: 2 As pointed out by wildwasser in a comment, this means, that a substitution is treated as an insertion and a deletion. So substitutions could be avoided by the algorithm. For the normal implementation with a cost of 1 for each operation there are multiple bitparallel implementations like e.g. Myers/Hyyrö: static const uint64_t masks[64] = { 0x0000000000000001, 0x0000000000000003,

问题 I wrote a function to run R parallel, but it doesn't seem to work. The code is ''' rm(list=ls()) square<-function(x){ library(Iso) y=ufit(x,lmode<-2,x<-c(1:length(x)),type="b")[[2]] return(y) } num<-c(1,2,1,4) cl <- makeCluster(getOption("cl.cores",2)) clusterExport(cl,"square") results<-parLapply(cl,num,square) stopCluster(cl) ''' and the error is: Error in checkForRemoteErrors(val) : 2 nodes produced errors; first error: cannot open the connection I think a possible reason is that I used