search for: p_1

On 07/29/2013 09:15 AM, Sven Verdoolaege wrote: > On Mon, Jul 29, 2013 at 07:37:14AM -0700, Tobias Grosser wrote: >> On 07/29/2013 03:18 AM, Sven Verdoolaege wrote: >>> On Sun, Jul 28, 2013 at 04:42:25PM -0700, Tobias Grosser wrote: >>>> Sven: In terms of making the behaviour of isl easier to understand, >>>> it may make sense to fail/assert in case

...++) { for (ctr = 0; ctr < 8; ctr++) { x1 = input[i*64 + ctr*8 + 1] ; x0 = input[i*64 + ctr*8 + 0] ; input[i*64 + ctr*8 + 0] = x0 - x1; input[i*64 + ctr*8 + 1] = x0 + x1; input[i*64 + ctr*8 + 2] = x0 * x1; } Without this patch file, Polly would produce the Context as follows: Context: [p_0, p_1, p_2] -> { : p_0 >= -9223372036854775808 and p_0 <= 9223372036854775807 and p_1 >= -9223372036854775808 and p_1 <= 9223372036854775807 and p_2 >= -9223372036854775808 and p_2 <= 9223372036854775807 } p0: {0,+,128}<%for.cond2.preheader> p1: {2,+,128}<%for.cond2.preheade...

Hi Sebastian, Recently, I found the "Polly - Calculate dependences" pass would lead to significant compile-time overhead when compiling some loop-intensive source code. Tobias told me you found similar problem as follows: http://llvm.org/bugs/show_bug.cgi?id=14240 My evaluation shows that "Polly - Calculate dependences" pass consumes 96.4% of total compile-time overhead

Full_Name: Manuel Luethi Version: 2.9.1 OS: Windows XP Submission from: (NULL) (129.132.128.136) Hi I created the following vectors: p_1=c(0.2,0.2,0.2,0.2,0.1,0.25,0.4,0.1,0.25,0.4,0.1,0.25,0.4,0.1,0.25,0.4,0.2,0.5,0.8,0.2,0.5,0.8,0.2,0.5,0.8,0.2,0.5,0.8) p_2=c(0,0,0,0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.4,0.25,0.1,0.4,0.25,0.1,0.4,0.25,0.1,0.4,0.25,0.1) As these are probabilities, I calculated the remainder as p_3=1...

...; input[i*64 + ctr*8 + 0] = x0 - x1; input[i*64 + ctr*8 + 1] = x0 + x1; input[i*64 + ctr*8 + 2] = x0 * x1; } // SCoP end bar(); // Unknown function call stops further expansion of SCoP } } Which is translated to the following scop: Context: [p_0, p_1, p_2] -> { : p_0 >= -2147483648 and p_0 <= 2147483647 and p_1 >= -2147483648 and p_1 <= 2147483647 and p_2 >= -2147483648 and p_2 <= 2147483647 } p_0: {0,+,128}<%for.cond2.preheader> p_1: {2,+,128}<%for.cond2.preheader> p_2: {4,+,128}<%for.cond2...

...input[i*64 + ctr*8 + 1] = x0 + x1; > input[i*64 + ctr*8 + 2] = x0 * x1; > } > // SCoP end > > bar(); // Unknown function call stops further expansion of SCoP > } >} > >Which is translated to the following scop: > > Context: > [p_0, p_1, p_2] -> { : p_0 >= -2147483648 and p_0 <= 2147483647 >and p_1 >= -2147483648 and p_1 <= 2147483647 and p_2 >= -2147483648 and >p_2 <= 2147483647 } > p_0: {0,+,128}<%for.cond2.preheader> > p_1: {2,+,128}<%for.cond2.preheader> > p_2: {4,...

...polybench$ polly-clang -O3 -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++) {...

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

...opInfo, but I need your > help on handling some problems about ISL and SCEV. I copied the list as the discussion may be helpful for others. @Sven, no need to read all. Just search for your name. [..] >>The interesting observation is, that Polly introduces three parameters >>(p_0, p_1, p_2) for this SCoP, even though in the C source code only the >>variable 'i' is SCoP invariant. However, due to the way >>SCEVExpr(essions) in LLVM are nested, Polly sees three scop-invariant >>SCEVExpr(essions) which are all translated into independent parameters. >&g...

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

...----------------------------------------------------------- p<-data.frame(a=rnorm(10),b=rnorm(10),c=rnorm(10),d=rnorm(10)) test<-data.frame(a=rnorm(1),b=rnorm(1),c=rnorm(1),d=rnorm(1)) result<-list() for(i in 1:nrow(p)){ result[[i]]<-sum((p[i,]-test)^2) } result_1<-unlist(result) p_1<-cbind(p,result_1) ------------------------------------------------------------------------------------------------------- is there any efficient way to shorten the time and make same output? -- View this message in context: http://r.789695.n4.nabble.com/how-to-speed-up-the-inefficient-code-...

...ng -O3 -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...

Hi, Does R have any functions implementing such multinomial regression: (S_t^A,S_t^B)~MN(N_t-Y_{t-1},P_t^A,P_t^B) where MN(n,p_1,p_2) is multinomial distribution with parameters n, p_1, p_2. Here P_t^A and P_t^B are nonlinear functions from predictor variables and parameters which need to be estimated. Here A and B are used for notation, they are not parameters. My second question is about nls capabilities. Can I use lagge...

Dear R List, I have a dataset with the following structure: """personal_id, p_0, p_1, p_2, .... , p_36, p_37 1, NA, 1, 4, .... , 1, NA 2, NA, NA, NA, .... , 4, NA . . . 6020, NA, 3, 3, ...., NA, NA 6021, NA, 2, 2, ...., 4, NA """ I used some made-up data. It is just meant to show the structure of the dataset. The variables of interest are p_0, ... p_37. They re...

I am trying to make the following Kalman filter equations work and therefore produce their graphs. v_t=y_t - a_t a_t+1=a_t+K_t*v_t F_t=P_t+sigma.squared.epsilon P_t+1=P_t*(1-K_t)+sigma.squared.eta K_t=P_t/F_t Given: a_1=0,P_1=10^7,sigma.squared.epsilon=15099, sigma.squared.eta=1469.1 I have attached my code,which of course doesnt work.It produces NAs for the Fs,Ks and the a. Can somebody tell me please what am I doing wrong in this loop? Why doesnt this loop work as it should be;to produce plots of a declining filtere...

Hi all, Does anyone have an R implementation of the queplot (K.?M. Goldberg and B.?Iglewicz. Bivariate extensions of the boxplot. Technometrics, 34(3):pp. 307?320, 1992)? I'm struggling with the estimation of the asymmetry parameters. Hadley -- Assistant Professor / Dobelman Family Junior Chair Department of Statistics / Rice University http://had.co.nz/

Saludo cordial para cada uno. Les pido ayuda para generar números aleatorios de una mixtura k-puntos. Sabemos que la función de distribución F es una mixtura k-puntos si es de la forma F(x) = p_1 F_1(x) + p_2 F_2(x) + … + p_k F_k(x), donde F_j es una función de distribución de probabilidad, p_j > 0 y suma(p_j) = 1, para j = 1, 2, …, k. En mi caso particular F es la suavización de la función de probabilidad discreta {p_j} mediante kernel gamma. En el siguiente código F es la función Ke...

...how to deal with this one. Encapsulating this part of the code in extern "C" { } has no effect. runSimulation is just a regular function, with a lot of int* and double* parameters, and it is not part of a class. It does inside create a class, though. Outline: void runSimulation(int *p_1 ... etc) { try { CModel Model(); Model.Run(); } catch (CException& e) { cout << "exception" << endl; } } Encapsulating this function in extern "C" also has no effect. Anyone any ideas what I should do? The system I am using is an Intel-...

...ampling were 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

I have a simple X, Y data frame that I am trying to run regression analysis on. The linear regression looks great, but when I use lm(formula = y ~ poly(x, degree = 5)) I get the same coeffecients. So for example if I use degree =3 my formula would look like y = 4.2 x^3 + 3.2x^2 + 2.1x + 1.0 and my degree 5 would look like y = 6.5x^5+ 5.4x^4 + 4.2 x^3 + 3.2x^2 + 2.1x + 1.0, which doesn't make