🚀 go-pugleaf

> Hi,
> 	S on our 11/750 - 4.2BSD seems very slow. I know that this situation
> can be improved by binding commonly used functions into S's executive. How
> much of an improvement can reasonably be expected ? Any hints as to which
> functions are good candidates ?

We have EVERYTHING (except some device drivers) in the executive.  This
creates a big executive, but I "feel" a substantial speedup.  I don't
have any hard figures on this.  With virtual memory under 4.2 I see no
reason to have any stand alone functions, except for testing purposes.
Does anyone disagree with this?

-- 

Mike Meyer --  Phone (608) 262-1157

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I tried the first example S command on our 750 running 4.2.  It took
6 minutes of elapsed time which was about 3 or 4 minutes
of actual cpu time.  Our S is configured to be as much in core
as possible, i.e, I used the biglist when compiling it.  The
load average was only about 2 when I tried this.

Steve Hubert
 Dept. of Stat., U. of Wash, Seattle
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On example one try:  s_sin((1:20)%o(1:20))  This should help.
S for loops are notoriously slow and every effort should
be made to find ways toexploit S's natural looping constructs.
The operator %o is a special case ofthe general function outer.
See also apply and sapply for useful functions.

				Roger Koenker
				Department of Economics
				University of Illinois

On a lightly loaded VAX 11/780 running 4.2 BSD, with nearly everything
in the S executive,

	write(1:10000, "/tmp/junk")

took about 25 seconds of user+system time (2 minutes real); and a
shortly subsequent

	m <- matrix( read("/tmp/junk"),100,100)

took about 20 seconds of user+system time.  On the other hand,

	for (i in 1:20) for (j in 1:20) m[i,j] <- sin(j/10)

was so slow I didn't wait for it to finish.  I'm not an expert on the
bowels of S, but a cursory glance at $M/lang3.yr shows that each
occurence of `<-' invokes $F/assign, which in turn calls $L/getds,
$P/pcopy, and $L/putds.  Thus one problem with the nested for loop is
that assignment is very expensive in S; apparently each assignment
copies a dataset from one file to another!  Doing O(n^2) complete file
rewrites to initialize a matrix is *bound* to be slow.

Fortunately, S has extensive resources for avoiding loops. Two examples
that spring to mind, taking perhaps .3 and .1 seconds respectively:

    m <- matrix(0,20,20);    m <- sin( col(m)/10)

    m <- matrix( sin(1:20/10), 20,20, byrow=T)

			Jim Leinweber

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