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11674740
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2012-07-26T17:28:29.270
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2012-07-26T17:28:29.270
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I've figured it out! The problem was in the <code>sapply()</code> call. <code>sapply</code> does a fair amount of renaming and property setting which slows things down massively for arrays of this size. Replacing <code>DecodeSpectrum</code> with the following code brought the sample time from <code>14.66</code> seconds down to <code>3.36</code> seconds, a 4-fold increase! Here's the new body of <code>DecodeSpectrum</code>: <pre><code>DecodeSpectrum <- function(array_length, mz_array, intensity_array) { ## needed to tell `vapply` how long the result should be. No, there isn't an ## easier way to do this. resultLength <- rep(1.0, array_length) vapply(list(mz_array=mz_array, intensity_array=intensity_array), readBin, resultLength, what="double", endian="little", n=array_length, USE.NAMES=FALSE) } </code></pre> The <code>Rprof</code> output now looks like: <pre><code>$by.self self.time self.pct total.time total.pct "<Anonymous>" 0.64 19.75 2.14 66.05 "DecodeSpectrum" 0.46 14.20 1.12 34.57 ".Call" 0.42 12.96 0.42 12.96 "FUN" 0.38 11.73 0.38 11.73 "&" 0.16 4.94 0.16 4.94 ">" 0.14 4.32 0.14 4.32 "c" 0.14 4.32 0.14 4.32 "list" 0.14 4.32 0.14 4.32 "vapply" 0.12 3.70 0.66 20.37 "mapply" 0.10 3.09 2.54 78.40 "simplify2array" 0.10 3.09 0.30 9.26 "<" 0.08 2.47 0.08 2.47 "t" 0.04 1.23 2.72 83.95 "as.vector" 0.04 1.23 0.08 2.47 "unlist" 0.04 1.23 0.08 2.47 "lapply" 0.04 1.23 0.04 1.23 "unique.default" 0.04 1.23 0.04 1.23 "NextSegment" 0.02 0.62 0.50 15.43 "odbcFetchRows" 0.02 0.62 0.46 14.20 "unique" 0.02 0.62 0.10 3.09 "array" 0.02 0.62 0.04 1.23 "attr" 0.02 0.62 0.02 0.62 "match.fun" 0.02 0.62 0.02 0.62 "odbcValidChannel" 0.02 0.62 0.02 0.62 "parent.frame" 0.02 0.62 0.02 0.62 $by.total total.time total.pct self.time self.pct "ProcessAllSegments" 3.24 100.00 0.00 0.00 "t" 2.72 83.95 0.04 1.23 "do.call" 2.68 82.72 0.00 0.00 "mapply" 2.54 78.40 0.10 3.09 "<Anonymous>" 2.14 66.05 0.64 19.75 "DecodeSpectrum" 1.12 34.57 0.46 14.20 "vapply" 0.66 20.37 0.12 3.70 "NextSegment" 0.50 15.43 0.02 0.62 "odbcFetchRows" 0.46 14.20 0.02 0.62 ".Call" 0.42 12.96 0.42 12.96 "FUN" 0.38 11.73 0.38 11.73 "simplify2array" 0.30 9.26 0.10 3.09 "&" 0.16 4.94 0.16 4.94 ">" 0.14 4.32 0.14 4.32 "c" 0.14 4.32 0.14 4.32 "list" 0.14 4.32 0.14 4.32 "unique" 0.10 3.09 0.02 0.62 "<" 0.08 2.47 0.08 2.47 "as.vector" 0.08 2.47 0.04 1.23 "unlist" 0.08 2.47 0.04 1.23 "lapply" 0.04 1.23 0.04 1.23 "unique.default" 0.04 1.23 0.04 1.23 "array" 0.04 1.23 0.02 0.62 "attr" 0.02 0.62 0.02 0.62 "match.fun" 0.02 0.62 0.02 0.62 "odbcValidChannel" 0.02 0.62 0.02 0.62 "parent.frame" 0.02 0.62 0.02 0.62 $sample.interval [1] 0.02 $sampling.time [1] 3.24 </code></pre> It's possible that some additional performance could be squeezed out of messing with the <code>do.call('mapply', ...)</code> call, but I'm satisfied enough with the performance as is that I'm not willing to waste time on that.
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