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Hi, I received an internal test case from a game team (it wasn't about this in particular), and I was wondering if there was maybe an opportunity to canonicalize a particular code pattern: %inputi = bitcast <4 x float> %input to <4 x i32> %row0i = and <4 x i32> %inputi, <i32 -1, i32 0, i32 0, i32 0> %row0 = bitcast <4 x i32> %row0i to <4 x float> %row1i = and <4 x i32> %inputi, <i32 0, i32 -1, i32 0, i32 0> %row1 = bitcast <4 x i32> %row1i to <4 x float> %row2i = and <4 x i32&gt...

...optimizer. A DSE is good enough. > > Which posted patch about TBAA? you have yet another solution except > decompose-vectors? Ah, no, the TBAA thing is separate really. llvmpipe generally operates on 4 rows at a time, so some functions end up with patterns like: load <16 x i8> row0 ... load <16 x i8> row1 ... load <16 x i8> row2 ... load <16 x i8> row3 ... ... do stuff ... store <16 x i8> row0 ... store <16 x i8> row1 ... store <16 x i8> row2 ... store <16 x i8> row3 ... Since the row stride is variable, llvm...

Hi, Richard, I think we are solving a same problem. I am working on shader language too. I am not satisfied with current binaries because vector operations are kept in llvm opt. glsl shader language has an operation called "swizzle". It can select sub-components of a vector. If a shader only takes components "xy" for a vec4. it's certainly wasteful to generate 4

...= 22L, ncol = 22L, dimnames = list(names(dat), names(dat))) for(i in 1:22) { #i=1 x <- dat[[i]] for(j in (1:22)) { #j=2 if(i == j) { # there's nothing to test, assign correlation 1 r[i, j] <- 1 } else { tmp <-cbind(x,dat[[j]]) row0 <-rowSums(tmp) tem2 <-tmp[row0!=0,] tmp3 <- cor.test(tem2[,1],tem2[,2]) r[i, j] <- tmp3$estimate P[i, j] <- tmp3$p.value } } } r<-as.data.frame(r) P<-as.data.frame(P) From: R-help <r-help-bounces at r-project.org> On Behalf Of Yuan Chun Ding...

...dat))) > > for(i in 1:22) { > #i=1 > x <- dat[[i]] > for(j in (1:22)) { > #j=2 > if(i == j) { > # there's nothing to test, assign correlation 1 > r[i, j] <- 1 > } else { > tmp <-cbind(x,dat[[j]]) > row0 <-rowSums(tmp) > tem2 <-tmp[row0!=0,] > tmp3 <- cor.test(tem2[,1],tem2[,2]) > r[i, j] <- tmp3$estimate > P[i, j] <- tmp3$p.value > } > } > } > r<-as.data.frame(r) > P<-as.data.frame(P) > > From: R-help <r-...

...=1 > > x <- dat[[i]] > > for(j in (1:22)) { > > #j=2 > > if(i == j) { > > # there's nothing to test, assign correlation 1 > > r[i, j] <- 1 > > } else { > > tmp <-cbind(x,dat[[j]]) > > row0 <-rowSums(tmp) > > tem2 <-tmp[row0!=0,] > > tmp3 <- cor.test(tem2[,1],tem2[,2]) > > r[i, j] <- tmp3$estimate > > P[i, j] <- tmp3$p.value > > } > > } > > } > > r<-as.data.frame(r) > > P<-as...

...> > > Which posted patch about TBAA? you have yet another solution except > > decompose-vectors? > > Ah, no, the TBAA thing is separate really. llvmpipe generally operates > on 4 rows at a time, so some functions end up with patterns like: > > load <16 x i8> row0 ... > load <16 x i8> row1 ... > load <16 x i8> row2 ... > load <16 x i8> row3 ... > ... do stuff ... > store <16 x i8> row0 ... > store <16 x i8> row1 ... > store <16 x i8> row2 ... > store <16 x i8> row3 ......

...t; > for(j in (1:22)) { > > > #j=2 > > > if(i == j) { > > > # there's nothing to test, assign correlation 1 > > > r[i, j] <- 1 > > > } else { > > > tmp <-cbind(x,dat[[j]]) > > > row0 <-rowSums(tmp) > > > tem2 <-tmp[row0!=0,] > > > tmp3 <- cor.test(tem2[,1],tem2[,2]) > > > r[i, j] <- tmp3$estimate > > > P[i, j] <- tmp3$p.value > > > } > > > } > > > } > > > r...

HI Rui, Thank you for the help! You did not remove a row if zero values exist in both column pair, right? Ding From: Rui Barradas <ruipbarradas at sapo.pt> Sent: Thursday, July 25, 2024 11:15 AM To: Yuan Chun Ding <ycding at coh.org>; r-help at r-project.org Subject: Re: [R] please help generate a square correlation matrix ?s 17:?39 de 25/07/2024, Yuan Chun Ding via R-help

...> > > #j=2 > > > > if(i == j) { > > > > # there's nothing to test, assign correlation 1 > > > > r[i, j] <- 1 > > > > } else { > > > > tmp <-cbind(x,dat[[j]]) > > > > row0 <-rowSums(tmp) > > > > tem2 <-tmp[row0!=0,] > > > > tmp3 <- cor.test(tem2[,1],tem2[,2]) > > > > r[i, j] <- tmp3$estimate > > > > P[i, j] <- tmp3$p.value > > > > } > > > > } &gt...

...gt; > #j=2 > > > > if(i == j) { > > > > # there's nothing to test, assign correlation 1 > > > > r[i, j] <- 1 > > > > } else { > > > > tmp <-cbind(x,dat[[j]]) > > > > row0 <-rowSums(tmp) > > > > tem2 <-tmp[row0!=0,] > > > > tmp3 <- cor.test(tem2[,1],tem2[,2]) > > > > r[i, j] <- tmp3$estimate > > > > P[i, j] <- tmp3$p.value > > > > } > > > >...

...if(i == j) { > > > > > > # there's nothing to test, assign correlation 1 > > > > > > r[i, j] <- 1 > > > > > > } else { > > > > > > tmp <-cbind(x,dat[[j]]) > > > > > > row0 <-rowSums(tmp) > > > > > > tem2 <-tmp[row0!=0,] > > > > > > tmp3 <- cor.test(tem2[,1],tem2[,2]) > > > > > > r[i, j] <- tmp3$estimate > > > > > > P[i, j] <- tmp3$p.value > > >...

...{ > > > > > > # there's nothing to test, assign correlation 1 > > > > > > r[i, j] <- 1 > > > > > > } else { > > > > > > tmp <-cbind(x,dat[[j]]) > > > > > > row0 <-rowSums(tmp) > > > > > > tem2 <-tmp[row0!=0,] > > > > > > tmp3 <- cor.test(tem2[,1],tem2[,2]) > > > > > > r[i, j] <- tmp3$estimate > > > > > > P[i, j] <- tmp3$p.value >...