search for: vectorizability

On Sun, May 22, 2011 at 1:07 PM, Serg Anohovsky <serg.anohovsky at gmail.com>wrote: > Hello. > I have compiled the simple program: > > #include <stdio.h> > #include <stdlib.h> > > int v1[10000]; > > int main() > { > int i; > > for (i = 0; i < 10000; i++) { > v1[i] = i; > } > > This loop

----- Original Message ----- > Hi Preston! > > > > > I'd start by making a plan (a design!) with goals and stuff. > > Publish it so we can see what you mean by "vectorization". > > I will send a separate email later, but here is a quick overview. I > see the vectorizer having four main components: > > 1. Preparation passes: If-conversion,

Nadav Rotem <nrotem at apple.com> wrote: > I sent a patch to llvm-commit with a new loop vectorizer. > This is a very simple loop vectorizer, but we have to start somewhere. > With this new loop vectorizer we can already vectorize a good number of loops. > I know that we can improve the new loop vectorizer in a number of ways. > We can implement a precise dependence test, >

Hi Preston! > > I'd start by making a plan (a design!) with goals and stuff. > Publish it so we can see what you mean by "vectorization". I will send a separate email later, but here is a quick overview. I see the vectorizer having four main components: 1. Preparation passes: If-conversion, loop transformations, etc. 2. Cost model - This unit decides on the best

Hi Gerolf, I think we have several (perhaps separable) issues here: 1. Do we have a canonical form for loops, preserved through the optimizer, that allows naturally-constructed loop nests to remain separable? 2. Do we forbid non-lowering transformations that turn vectorizable loops into non-vectorizable loops? 3. How do we detect cases where transformations cause a negative answer to either

Hello. I have compiled the simple program: #include <stdio.h> #include <stdlib.h> int v1[10000]; int main() { int i; for (i = 0; i < 10000; i++) { v1[i] = i; } for (i = 0; i < 10000; i++) { printf("%d ", v1[i]); } return 0; } Next, I disasseble the executable file and have not found

...he code for the loop). The scalar remainder loop, when reached from the vectorized loop, is already known to be vectorizable to a VF larger than EpilogVF. No need to introduce again any potential aliasing, wrapping or whatnot checks, even if this redundancy can later be eliminated, if instead this vectorizability property could be recorded somehow. Similar to having annotated the remainder loop with “#pragma clang loop vectorize(assume_safety)”, except that this vectorizability property does not hold when reaching the remainder loop along the other path – that which fails these checks for the main loop......

Hi Nadav, Do you have any small write-up of current design of loop vectorizer?. If so, can you please send it across?. I want to see if there are dependencies such as unrolling for the vectorization. In the design we may also have to consider BB vectorizer and loop vectorizer working well together with no ambiguous requirements/dependencies. Regards, Shivaram -----Original Message-----

Hi everybody, On 10/17/12 12:32 AM, Hal Finkel wrote: >>> Do you have a plan for xforms to increase the amount of >>> vectorization? >> >> Yes. We will need to implement a predication phase and to design the >> interaction with other loop transformations. Also, this will have to >> work well with the cost model. We also need to think of a good way to

If we are on the subject of byte compilation, let me bring a couple of examples which have been puzzling me for some time. I'd like to know a) if the compilation will likely to improve the performance for this type of computations, and b) at least roughly understand the reasons for the observed numbers, specifically why x[i]<- assignment is so much slower than x[i] extraction. The loops

On Wed, Jun 5, 2013 at 5:51 PM, Nadav Rotem <nrotem at apple.com> wrote: > Hi, > > Thanks for the feedback. I think that we agree that vectorization on -Os > can benefit many programs. Regarding -O2 vs -O3, maybe we should set a > higher cost threshold for O2 to increase the likelihood of improving the > performance ? We have very few regressions on -O3 as is and with

Hi, Thanks for the feedback. I think that we agree that vectorization on -Os can benefit many programs. Regarding -O2 vs -O3, maybe we should set a higher cost threshold for O2 to increase the likelihood of improving the performance ? We have very few regressions on -O3 as is and with better cost models I believe that we can bring them close to zero, so I am not sure if it can help that much.

There is a work taking place by multiple people in this area and more is expected to happen and I’d like to make sure we’re working toward a common end goal. I tried to collect the use-cases for run-time memory checks and the specific memchecks required for each: 1. Loop Vectorizer: each memory access is checked against all other memory accesses in the loop (except read vs read) 2. Loop

I've recently mentioned in a few places that I'm interested in enhancing the loop vectorizer to handle multiple exit loops, and have been asked to share plans.  This email is intended to a) share my current thinking and b) help spark discussion among interested parties.  I do need to warn that my near term plans for this have been delayed; I got pulled into an internal project

---------- Forwarded message ---------- From: Serg Anohovsky <serg.anohovsky at gmail.com> Date: 2011/5/22 Subject: Re: [LLVMdev] No SSE instructions To: Chris Lattner <clattner at apple.com> 2011/5/22 Chris Lattner <clattner at apple.com> > > On May 22, 2011, at 10:47 AM, Justin Holewinski wrote: > > On Sun, May 22, 2011 at 1:07 PM, Serg Anohovsky

Hi, We'd like to propose new Loop Distribution pass. The main motivation is to allow partial vectorization of loops. One such example is the main loop of 456.hmmer in SpecINT_2006. The current version of the patch improves hmmer by 24% on ARM64 and 18% on X86. The goal of the pass is to distribute a loop that can't be vectorized because of memory dependence cycles. The pass splits

Hi, I would like to start a discussion about enabling the loop vectorizer by default for -Os. The loop vectorizer can accelerate many workloads and enabling it for -Os and -O2 has obvious performance benefits. At the same time the loop vectorizer can increase the code size because of two reasons. First, to vectorize some loops we have to keep the original loop around in order to handle the last

Extracting from data frame one element at a time the way you did is expensive. I.e., test[i, 6] is slower than test$whatever[i]. As an example: > dat <- data.frame(a = sample(LETTERS, 1e6, replace=TRUE), b=1:1e6, + c=rep("A", 1e6)) > dat$a <- as.character(dat$a) > dat$c <- as.character(dat$c) > > system.time( + for(i in 1:10) { + dat[i, 3]

----- Original Message ----- > From: "Hal Finkel" <hfinkel at anl.gov> > To: "Chandler Carruth" <chandlerc at google.com> > Cc: llvmdev at cs.uiuc.edu > Sent: Thursday, July 16, 2015 1:58:02 AM > Subject: Re: [LLVMdev] Improving loop vectorizer support for loops > with a volatile iteration variable > ----- Original Message ----- > >

Luke, Thank you for sharing the benchmark results. The improvement is very substantial, I am looking forward to the release of the byte compiler! The arithmetic shows that x[i]<- is still the bottleneck. I suspect that this is due to a very involved dispatching/search for the appropriate function on the C level. There might be significant gain if loops somehow cached the result of the initial