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copied!<p>This is a nice and simple example where <a href="http://dirk.eddelbuettel.com/code/rcpp.html" rel="noreferrer">Rcpp</a> can shine. </p> <p>So let us first recast functions 1 and 2 and their compiled counterparts:</p> <pre><code>library(inline) library(rbenchmark) library(compiler) fun1 <- function(z) { for(i in 2:NROW(z)) { z[i] <- ifelse(z[i-1]==1, 1, 0) } z } fun1c <- cmpfun(fun1) fun2 <- function(z) { for(i in 2:NROW(z)) { z[i] <- if(z[i-1]==1) 1 else 0 } z } fun2c <- cmpfun(fun2) </code></pre> <p>We write a <a href="http://dirk.eddelbuettel.com/code/rcpp.html" rel="noreferrer">Rcpp</a> variant very easily:</p> <pre><code>funRcpp <- cxxfunction(signature(zs="numeric"), plugin="Rcpp", body=" Rcpp::NumericVector z = Rcpp::NumericVector(zs); int n = z.size(); for (int i=1; i<n; i++) { z[i] = (z[i-1]==1.0 ? 1.0 : 0.0); } return(z); ") </code></pre> <p>This uses the <a href="http://cran.r-project.org/package=inline" rel="noreferrer">inline</a> package to compile, load and link the five-liner on the fly. </p> <p>Now we can define our test-date, which we make a little longer than the original (as just running the original too few times result in unmeasurable times):</p> <pre><code>R> z <- rep(c(1,1,0,0,0,0), 100) R> identical(fun1(z),fun2(z),fun1c(z),fun2c(z),funRcpp(z)) [1] TRUE R> </code></pre> <p>All answers are seen as identical. </p> <p>Finally, we can benchmark:</p> <pre><code>R> res <- benchmark(fun1(z), fun2(z), + fun1c(z), fun2c(z), + funRcpp(z), + columns=c("test", "replications", "elapsed", + "relative", "user.self", "sys.self"), + order="relative", + replications=1000) R> print(res) test replications elapsed relative user.self sys.self 5 funRcpp(z) 1000 0.005 1.0 0.01 0 4 fun2c(z) 1000 0.466 93.2 0.46 0 2 fun2(z) 1000 1.918 383.6 1.92 0 3 fun1c(z) 1000 10.865 2173.0 10.86 0 1 fun1(z) 1000 12.480 2496.0 12.47 0 </code></pre> <p>The compiled version wins by a factor of almost 400 against the best R version, and almost 100 against its byte-compiled variant. For function 1, the byte compilation matters much less and both variants trail the C++ by a factor of <em>well over two-thousand</em>.</p> <p>It took about one minute to write the C++ version. The speed gain suggests it was a minute well spent.</p> <p>For comparison, here is the result for the original short vector called more often:</p> <pre><code>R> z <- c(1,1,0,0,0,0) R> res2 <- benchmark(fun1(z), fun2(z), + fun1c(z), fun2c(z), + funRcpp(z), + columns=c("test", "replications", + "elapsed", "relative", "user.self", "sys.self"), + order="relative", + replications=10000) R> print(res2) test replications elapsed relative user.self sys.self 5 funRcpp(z) 10000 0.046 1.000000 0.04 0 4 fun2c(z) 10000 0.132 2.869565 0.13 0 2 fun2(z) 10000 0.271 5.891304 0.27 0 3 fun1c(z) 10000 1.045 22.717391 1.05 0 1 fun1(z) 10000 1.202 26.130435 1.20 0 </code></pre> <p>The qualitative ranking is unchanged: the <a href="http://dirk.eddelbuettel.com/code/rcpp.html" rel="noreferrer">Rcpp</a> version dominates, function2 is second-best. with the byte-compiled version being about twice as fast that the plain R variant, but still almost three times slower than the C++ version. And the relative difference are lower: relatively speaking, the function call overhead matters less and the actual looping matters more: C++ gets a bigger advantage on the actual loop operations in the longer vectors. That it is an important result as it suggests that more real-life sized data, the compiled version may reap a larger benefit.</p> <p><em>Edited to correct two small oversights in the code examples. And edited again with thanks to Josh to catch a setup error relative to fun2c.</em></p>

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