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On 16/01/2014, 23:47 , Andrew Trick wrote: > > On Jan 15, 2014, at 4:13 PM, Diego Novillo <dnovillo at google.com > <mailto:dnovillo at google.com>> wrote: > >> Chandler also pointed me at the vectorizer, which has its own >> unroller. However, the vectorizer only unrolls enough to serve the >> target, it's not as general as the runtime-triggered

...lled. In this particular case, there are three loops that need to be unrolled to get the performance I'm looking for. Of the three, only one gets far enough in the analysis to decide whether we unroll it or not. >> > > I assume the other two loops are quantum_cnot's <http://sourcecodebrowser.com/libquantum/0.2.4/gates_8c_source.html#l00054> and quantum_toffoli's <http://sourcecodebrowser.com/libquantum/0.2.4/gates_8c_source.html#l00082>. > > The problem for the unroller in the loop vectorizer is that it wants to if-convert those loops. The conditional store prevents...

...arting to use the sample profiler to analyze new performance opportunities. The loop unroller has popped up in several of the benchmarks I'm running. In particular, libquantum. There is a ~12% opportunity when the runtime unroller is triggered. This helps functions like quantum_sigma_x (http://sourcecodebrowser.com/libquantum/0.2.4/gates_8c_source.html#l00149). The function accounts for ~20% of total runtime. By allowing the runtime unroller, we can speedup the program by about 12%. I have been poking at the unroller a little bit. Currently, the runtime unroller is only triggered by a special flag or if...

Dear all, For a project I need to calculate the conditional AIC of a mixed effects model. Luckily, I found a reference in the R help forum for a function to be used: CAIC <- function(model) { sigma <- attr(VarCorr(model), 'sc') observed <- attr(model, 'y') predicted <- fitted(model) cond.loglik <- sum(dnorm(observed,