问对余弦距离进行并行化的最佳方法是哪种？

EN

x <- as.matrix(mtcars)
library(lsa)

cosine(as.matrix(mtcars))

cosR <- function(x) {
      co <- array(0, c(ncol(x), ncol(x)))
      ## f <- colnames(x)
      ## dimnames(co) <- list(f, f)
      for (i in 2:ncol(x)) {
        for (j in 1:(i - 1)) {
            co[i,j] <- crossprod(x[,i], x[,j])/
                sqrt(crossprod(x[,i]) * crossprod(x[,j]))
        }
    }
    co <- co + t(co)
    diag(co) <- 1
    return(as.matrix(co))
}

library(Rcpp)
library(RcppArmadillo)
cppFunction(depends='RcppArmadillo',
            code="NumericMatrix cosCpp(NumericMatrix Xr) {
            int n = Xr.nrow(), k = Xr.ncol();
            arma::mat X(Xr.begin(), n, k, false); // reuses memory and avoids extra copy
            arma::mat Y = arma::trans(X) * X; // matrix product
            arma::mat res = Y / (arma::sqrt(arma::diagvec(Y)) * arma::trans(arma::sqrt(arma::diagvec(Y))));
            return Rcpp::wrap(res);
           }")

identical(cosR(x),unname(cosine(x)))
all.equal(cosCpp(x),cosR(x))

library(microbenchmark)
microbenchmark(cosine(x),cosR(x),cosCpp(x))
## Unit: nanoseconds
##       expr    min      lq       mean  median      uq      max neval cld
##  cosine(x) 460046 1181837 2069604.51 1530719 2528021  8757989   100   b
##    cosR(x) 542414 1096448 1915011.12 1331277 2321596 11740233   100   b
##  cosCpp(x)      7   12472   35827.76   17999   30556   644551   100  a