[Rcpp-devel] RcppArmadillo -- crude benchmark of various mcmc vs rcppbugs

[Dirk Eddelbuettel]

On 26 April 2012 at 15:58, Whit Armstrong wrote:
| Slightly off topic, but Dirk kindly allowed me to make a quick
| benchmark post on rcppbugs.

"Allowed" ?  Come on, benchmarks are always ok, as are posts on Rcpp. This is
after all the Rcpp list...
| 
| Just a short post to the list showing some performance numbers (for
| running a simple linear regression) of rcppbugs (with an RcppArmadillo
| core) vs MCMCpack (Scythe core) vs JAGS (?? core).
| 
| I was fairly optimistic about the speed of rcppbugs, yet this exceeds
| my expectations by so much that I fear there may be a bug in my
| benchmark code.
| 
| I'm gearing up for my presentation at R/Finance, so I would be
| grateful to anyone who wants to kick the tires a bit.  The core has
| been tested (we've been using the pure c++ implementation for over a
| year), but the R binding (via rcppbugs) is completely new.
| 
| Thanks in advance.  Code below (you'll need to install MCMCpack,
| rjags, and rcppbugs (now on CRAN) to run it).
| 
| The quick summary is a 5x speedup over MCMCpack and a 77x speedup (I'm
| not sure I believe this yet) over JAGS.

A bit more modest here (two-year old i7 running Ubuntu amd64)

R> print(round(all.times.ratio,2))
          time ratio
lm        0.09  0.08
rcppbugs  1.18  1.00
mcmcpack  4.05  3.45
jags     57.06 48.56

But still pretty good compared to MCMCpack. That is possibly legit -- modern
C++ versus a C++ design from a decade ago.  Jags is a little harder to
believe.  Maybe time to write an email to Martyn?

Thanks for sharing this. Looking forward to your talk next month.

Dirk
 
| Comments welcome.
| 
| -Whit
| 
| 
| library(rcppbugs,quietly=TRUE,verbose=FALSE)
| library(rjags,quietly=TRUE,verbose=FALSE)
| library(MCMCpack,quietly=TRUE,verbose=FALSE)
| 
| NR <- 1e2L
| NC <- 2L
| 
| y <- rnorm(NR,1) + 10
| X <- matrix(nr=NR,nc=NC)
| X[,1] <- 1
| X[,2] <- y + rnorm(NR)/2 - 10
| 
| 
| lm.time <- system.time(lm.res <- lm.fit(X,y))
| ##print(coef(lm.res))
| 
| b <- mcmc.normal(rnorm(NC),mu=0,tau=0.0001)
| tau.y <- mcmc.uniform(sd(as.vector(y)),lower=0,upper=100)
| ##y.hat <- deterministic(function(X,b) { X %*% b }, X, b)
| y.hat <- linear(X,b)
| y.lik <- mcmc.normal(y,mu=y.hat,tau=tau.y,observed=TRUE)
| m <- create.model(b, tau.y, y.hat, y.lik)
| 
| 
| iterations <- 1e5L
| burn <- iterations
| adapt <- 1e3L
| thin <- 10L
| 
| cat("running rcppbugs model...\n")
| rcppbugs.time <- system.time(ans <- run.model(m,
| iterations=iterations, burn=burn, adapt=adapt, thin=thin))
| rcppbugs.coefs <- apply(ans[["b"]],2,mean)
| ##print(rcppbugs.coefs)
| print(rcppbugs.time)
| 
| cat("running MCMCpack model...\n")
| mcmcpack.time <- system.time(mcmcpack.out <- MCMCregress(y ~
| X.2,data=data.frame(y=y,X=X),burnin = burn, mcmc = iterations,
| thin=thin))
| print(mcmcpack.time)
| 
| cat("running jags model...\n")
| 
| bug.model <- '
| model {
| 	for (i in 1:NR){
| 		y[i] ~ dnorm(y.hat[i], tau.y)
| 		y.hat[i] <- inprod(b, X[i,])
| 	}
|         for (j in 1:NC){
|         	b[j] ~ dnorm(0, .0001)
|         }
| 	tau.y ~ dunif(0, 100)
| }
| '
| bug.file <- tempfile(fileext=".bug")
| cat(bug.model,file=bug.file)
| 
| jags.time1 <- system.time(
|              jags <- jags.model(bug.file,
|                                 data = list('X' = X,
|                                 'y' = y,
|                                 'NR' = NR,
|                                 'NC' = NC),
|                                 n.chains = 1,
|                                 n.adapt = adapt,
|                                 quiet=TRUE
|                                 ))
| jags.time2 <- system.time(update(jags, n.iter=burn, progress.bar="none"))
| jags.time3 <- system.time(jags.trace <-
| jags.samples(jags,c('b','tau.y'),n.iter=iterations,thin=thin,progress.bar="none"))
| 
| jags.time <- jags.time1 + jags.time2 + jags.time3
| print(jags.time)
| jags.coefs <- apply(jags.trace$b,1,mean)
| 
| ##print(jags.trace)
| 
| cat("rcppbugs speedup:\n")
| all.times <- rbind(lm.time["elapsed"],rcppbugs.time["elapsed"],mcmcpack.time["elapsed"],jags.time["elapsed"])
| all.times.ratio <- cbind(all.times,all.times/rcppbugs.time["elapsed"])
| colnames(all.times.ratio) <- c("time","ratio")
| rownames(all.times.ratio) <- c("lm","rcppbugs","mcmcpack","jags")
| print(round(all.times.ratio,2))
| 
| cat("coef comparison:\n")
| coef.compare <-
| rbind(coef(lm.res),rcppbugs.coefs,summary(mcmcpack.out)$statistics[,"Mean"][1:2],jags.coefs)
| rownames(coef.compare) <- c("lm","rcppbugs","mcmcpack","jags")
| print(coef.compare)
| _______________________________________________
| Rcpp-devel mailing list
| Rcpp-devel at lists.r-forge.r-project.org
| https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel
-- 
R/Finance 2012 Conference on May 11 and 12, 2012 at UIC in Chicago, IL
See agenda, registration details and more at http://www.RinFinance.com