Displaying 5 results from an estimated 5 matches for "p_4".
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2013 May 03
2
[LLVMdev] [Polly] GSoC Proposal: Reducing LLVM-Polly Compiling overhead
...3 -mllvm -polly
-mllvm -debug-only=polly-cloog linear-algebra/kernels/gemm/gemm.c -I
utilities/ utilities/polybench.c -mllvm -polly-ignore-aliasing
-DPOLYBENCH_USE_SCALAR_LB
:: init_array : entry.split => for.end56
if ((nj >= 1) && (nk >= 1) && (p_1 >= 1) && (p_4 >= 1)) {
for (c2=0;c2<=p_4-1;c2+=32) {
for
(c3=max(-32*floord(p_1-12*p_4+10,32)-32*p_4,-32*c2-32*floord(-12*c2+p_1+10,32)-640);c3<=-20*c2;c3+=32)
{
for
(c4=max(ceild(-c3-p_1-30,20),c2);c4<=min(min(floord(-c3,20),c2+31),p_4-1);c4++)
{
for (c5=max(c3,-20*c4-p_1...
2013 May 03
0
[LLVMdev] [Polly] GSoC Proposal: Reducing LLVM-Polly Compiling overhead
Dear Tobias,
Thank you very much for your very helpful advice.
Yes, -debug-pass and -time-passes are two very useful and powerful options when evaluating the compile-time of each compiler pass. They are exactly what I need! With these options, I can step into details of the compile-time overhead of each pass. I have finished some preliminary testing based on two randomly selected files from
2013 May 02
2
[LLVMdev] [Polly] GSoC Proposal: Reducing LLVM-Polly Compiling overhead
On 04/30/2013 04:13 PM, Star Tan wrote:
> Hi all,
[...]
> How could I find out where the time is spent on between two adjacent Polly passes? Can anyone give me some advice?
Hi Star Tan,
I propose to do the performance analysis using the 'opt' tool and
optimizing LLVM-IR, instead of running it from within clang. For the
'opt' tool there are two commands that should help
2013 May 03
0
[LLVMdev] [Polly] GSoC Proposal: Reducing LLVM-Polly Compiling overhead
...;-mllvm -debug-only=polly-cloog linear-algebra/kernels/gemm/gemm.c -I
>utilities/ utilities/polybench.c -mllvm -polly-ignore-aliasing
>-DPOLYBENCH_USE_SCALAR_LB
>:: init_array : entry.split => for.end56
>if ((nj >= 1) && (nk >= 1) && (p_1 >= 1) && (p_4 >= 1)) {
> for (c2=0;c2<=p_4-1;c2+=32) {
> for
>(c3=max(-32*floord(p_1-12*p_4+10,32)-32*p_4,-32*c2-32*floord(-12*c2+p_1+10,32)-640);c3<=-20*c2;c3+=32)
>{
> for
>(c4=max(ceild(-c3-p_1-30,20),c2);c4<=min(min(floord(-c3,20),c2+31),p_4-1);c4++)
>{
>...
2002 Dec 06
0
Non-R question.
...the same as in the 2002 study, so probaby the
overdispersion were the same (at least assuming that is the best I can
do).
So assuming equal overdispersion I can get four confidence interval s
of "binomial" p's, but people will want an hypothesis test of the overall
null: p_1=p_2=p_3=p_4. Is there some way I can construct an hypothesis test
for that null, from the four confidence intervals?
Hoping this is enough information of the background, as the mail is
already to long!
Thanks,
Kjetil Halvorsen