As some of you may know, I committed my basic-block autovectorization pass a few days ago. I encourage anyone interested to try it out (pass -vectorize to opt or -mllvm -vectorize to clang) and provide feedback. Especially in combination with -unroll-allow-partial, I have observed some significant benchmark speedups, but, I have also observed some significant slowdowns. I would like to share my thoughts, and hopefully get feedback, on next steps. 1. "Target Data" for vectorization - I think that in order to improve the vectorization quality, the vectorizer will need more information about the target. This information could be provided in the form of a kind of extended target data. This extended target data might contain: - What basic types can be vectorized, and how many of them will fit into (the largest) vector registers - What classes of operations can be vectorized (division, conversions / sign extension, etc. are not always supported) - What alignment is necessary for loads and stores - Is scalar-to-vector free? 2. Feedback between passes - We may to implement a closer coupling between optimization passes than currently exists. Specifically, I have in mind two things: - The vectorizer should communicate more closely with the loop unroller. First, the loop unroller should try to unroll to preserve maximal load/store alignments. Second, I think it would make a lot of sense to be able to unroll and, only if this helps vectorization should the unrolled version be kept in preference to the original. With basic block vectorization, it is often necessary to (partially) unroll in order to vectorize. Even when we also have real loop vectorization, however, I still think that it will be important for the loop unroller to communicate with the vectorizer. - After vectorization, it would make sense for the vectorization pass to request further simplification, but only on those parts of the code that it modified. 3. Loop vectorization - It would be nice to have, in addition to basic-block vectorization, a more-traditional loop vectorization pass. I think that we'll need a better loop analysis pass in order for this to happen. Some of this was started in LoopDependenceAnalysis, but that pass is not yet finished. We'll need something like this to recognize affine memory references, etc. I look forward to hearing everyone's thoughts. -Hal -- Hal Finkel Postdoctoral Appointee Leadership Computing Facility Argonne National Laboratory
Hi Hal,> As some of you may know, I committed my basic-block autovectorization > pass a few days ago. I encourage anyone interested to try it out (pass > -vectorize to opt or -mllvm -vectorize to clang) and provide feedback. > Especially in combination with -unroll-allow-partial, I have observed > some significant benchmark speedups, but, I have also observed some > significant slowdowns.codegen for vector constructs is not always that great in my experience. It could be that your vectorizer is doing the right thing, and it's codegen that needs to be improved. For example when I use the GCC autovectorizer I often see LLVM codegen unnecessarily scalarizing the vector code. Did you try to analyse these slowdowns? Ciao, Duncan. I would like to share my thoughts, and hopefully> get feedback, on next steps. > > 1. "Target Data" for vectorization - I think that in order to improve > the vectorization quality, the vectorizer will need more information > about the target. This information could be provided in the form of a > kind of extended target data. This extended target data might contain: > - What basic types can be vectorized, and how many of them will fit > into (the largest) vector registers > - What classes of operations can be vectorized (division, conversions / > sign extension, etc. are not always supported) > - What alignment is necessary for loads and stores > - Is scalar-to-vector free?
Duncan, I also noticed cases where vector IR is scalariezd by the codegen. From what I have seen (which is based on a different vectorizer with a different code model, etc) there are two main areas for improvements: 1. Complex instructions - Instructions such as shuffles are very sensitive to the ability of the codegen to lower them. If a vectorizer generates shuffle instructions which are not handled properly by the manual lowering code, then the instruction is scalarized. 2. Instructions with mixed types -Instructions which operate on mixed types, such as 2xfloat->2xdouble, are usually scalarized by the type legalizer. Nadav -----Original Message----- From: llvmdev-bounces at cs.uiuc.edu [mailto:llvmdev-bounces at cs.uiuc.edu] On Behalf Of Duncan Sands Sent: Friday, February 03, 2012 10:50 To: llvmdev at cs.uiuc.edu Subject: Re: [LLVMdev] Vectorization: Next Steps Hi Hal,> As some of you may know, I committed my basic-block autovectorization > pass a few days ago. I encourage anyone interested to try it out (pass > -vectorize to opt or -mllvm -vectorize to clang) and provide feedback. > Especially in combination with -unroll-allow-partial, I have observed > some significant benchmark speedups, but, I have also observed some > significant slowdowns.codegen for vector constructs is not always that great in my experience. It could be that your vectorizer is doing the right thing, and it's codegen that needs to be improved. For example when I use the GCC autovectorizer I often see LLVM codegen unnecessarily scalarizing the vector code. Did you try to analyse these slowdowns? Ciao, Duncan. I would like to share my thoughts, and hopefully> get feedback, on next steps. > > 1. "Target Data" for vectorization - I think that in order to improve > the vectorization quality, the vectorizer will need more information > about the target. This information could be provided in the form of a > kind of extended target data. This extended target data might contain: > - What basic types can be vectorized, and how many of them will fit > into (the largest) vector registers > - What classes of operations can be vectorized (division, > conversions / sign extension, etc. are not always supported) > - What alignment is necessary for loads and stores > - Is scalar-to-vector free?_______________________________________________ LLVM Developers mailing list LLVMdev at cs.uiuc.edu http://llvm.cs.uiuc.edu http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev --------------------------------------------------------------------- Intel Israel (74) Limited This e-mail and any attachments may contain confidential material for the sole use of the intended recipient(s). Any review or distribution by others is strictly prohibited. If you are not the intended recipient, please contact the sender and delete all copies.
On Fri, Feb 03, 2012 at 09:49:30AM +0100, Duncan Sands wrote:> Hi Hal, > > > As some of you may know, I committed my basic-block autovectorization > > pass a few days ago. I encourage anyone interested to try it out (pass > > -vectorize to opt or -mllvm -vectorize to clang) and provide feedback. > > Especially in combination with -unroll-allow-partial, I have observed > > some significant benchmark speedups, but, I have also observed some > > significant slowdowns. > > codegen for vector constructs is not always that great in my experience. > It could be that your vectorizer is doing the right thing, and it's > codegen that needs to be improved. For example when I use the GCC > autovectorizer I often see LLVM codegen unnecessarily scalarizing the > vector code. Did you try to analyse these slowdowns? > > Ciao, Duncan.Duncan, Is there a recommended approach for testing the new -vectorize support within dragonegg? Jack> > I would like to share my thoughts, and hopefully > > get feedback, on next steps. > > > > 1. "Target Data" for vectorization - I think that in order to improve > > the vectorization quality, the vectorizer will need more information > > about the target. This information could be provided in the form of a > > kind of extended target data. This extended target data might contain: > > - What basic types can be vectorized, and how many of them will fit > > into (the largest) vector registers > > - What classes of operations can be vectorized (division, conversions / > > sign extension, etc. are not always supported) > > - What alignment is necessary for loads and stores > > - Is scalar-to-vector free? > > _______________________________________________ > LLVM Developers mailing list > LLVMdev at cs.uiuc.edu http://llvm.cs.uiuc.edu > http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev
On Fri, 2012-02-03 at 09:49 +0100, Duncan Sands wrote:> Hi Hal, > > > As some of you may know, I committed my basic-block autovectorization > > pass a few days ago. I encourage anyone interested to try it out (pass > > -vectorize to opt or -mllvm -vectorize to clang) and provide feedback. > > Especially in combination with -unroll-allow-partial, I have observed > > some significant benchmark speedups, but, I have also observed some > > significant slowdowns. > > codegen for vector constructs is not always that great in my experience. > It could be that your vectorizer is doing the right thing, and it's > codegen that needs to be improved. For example when I use the GCC > autovectorizer I often see LLVM codegen unnecessarily scalarizing the > vector code. Did you try to analyse these slowdowns?There are a lot of them and I've only looked at a small fraction as of yet. I have seen things that look like codegen deficiencies, but I've not confirmed this in detail. One important case that I have noticed is where the pass will vectorize sign-extended conversions, or int/float conversions, etc. which end up being expensive to scalarize. There are also cases where it vectorizes small integer operations which just get scalarized by the codegen. I need to spend some more time looking at this. -Hal> > Ciao, Duncan. > > I would like to share my thoughts, and hopefully > > get feedback, on next steps. > > > > 1. "Target Data" for vectorization - I think that in order to improve > > the vectorization quality, the vectorizer will need more information > > about the target. This information could be provided in the form of a > > kind of extended target data. This extended target data might contain: > > - What basic types can be vectorized, and how many of them will fit > > into (the largest) vector registers > > - What classes of operations can be vectorized (division, conversions / > > sign extension, etc. are not always supported) > > - What alignment is necessary for loads and stores > > - Is scalar-to-vector free? > > _______________________________________________ > LLVM Developers mailing list > LLVMdev at cs.uiuc.edu http://llvm.cs.uiuc.edu > http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev-- Hal Finkel Postdoctoral Appointee Leadership Computing Facility Argonne National Laboratory
On Feb 2, 2012, at 7:56 PM, Hal Finkel wrote:> As some of you may know, I committed my basic-block autovectorization > pass a few days ago. I encourage anyone interested to try it out (pass > -vectorize to opt or -mllvm -vectorize to clang) and provide feedback. > Especially in combination with -unroll-allow-partial, I have observed > some significant benchmark speedups, but, I have also observed some > significant slowdowns. I would like to share my thoughts, and hopefully > get feedback, on next steps.Hi Hal, I haven't had a chance to look at your pass in detail, but here are some opinions: :)> 1. "Target Data" for vectorization - I think that in order to improve > the vectorization quality, the vectorizer will need more information > about the target. This information could be provided in the form of a > kind of extended target data. This extended target data might contain: > - What basic types can be vectorized, and how many of them will fit > into (the largest) vector registers > - What classes of operations can be vectorized (division, conversions / > sign extension, etc. are not always supported) > - What alignment is necessary for loads and stores > - Is scalar-to-vector free?I think that this will be a really important API, but I strongly advocate that you model this after TargetLoweringInfo instead of TargetData. First, TargetData isn't actually a target API (it should be fixed, I filed PR11936 to track this). Second, targets will have to implement imperative code to return precise answers to questions. For example, you'll want something like "what is the cost of a shuffle with this mask" which will be extremely target specific, will depend on what CPU subfeatures are enabled, etc. When you start working on this, I strongly encourage you to propose the API you want here. Start small and add features as you go.> 2. Feedback between passes - We may to implement a closer coupling > between optimization passes than currently exists. Specifically, I have > in mind two things: > - The vectorizer should communicate more closely with the loop > unroller. First, the loop unroller should try to unroll to preserve > maximal load/store alignments. Second, I think it would make a lot of > sense to be able to unroll and, only if this helps vectorization should > the unrolled version be kept in preference to the original. With basic > block vectorization, it is often necessary to (partially) unroll in > order to vectorize. Even when we also have real loop vectorization, > however, I still think that it will be important for the loop unroller > to communicate with the vectorizer.I really disagree with this, see below.> 3. Loop vectorization - It would be nice to have, in addition to > basic-block vectorization, a more-traditional loop vectorization pass. I > think that we'll need a better loop analysis pass in order for this to > happen. Some of this was started in LoopDependenceAnalysis, but that > pass is not yet finished. We'll need something like this to recognize > affine memory references, etc.I think that a loop vectorizor and a basic block vectorizer both make perfect sense and are important for different classes of code. However, I don't think that we should go down the path of trying to use a "basic block vectorizor + loop unrolling" serve the purpose of a loop vectorizer. Trying to make a BBVectorizer and a loop unroller play together will be really fragile, because they'll both have to duplicate the same metrics (otherwise, for example, you'd unroll a loop that isn't vectorizable). This will also be a huge hit to compile time. -Chris
On Mon, 2012-02-06 at 14:26 -0800, Chris Lattner wrote:> On Feb 2, 2012, at 7:56 PM, Hal Finkel wrote: > > As some of you may know, I committed my basic-block autovectorization > > pass a few days ago. I encourage anyone interested to try it out (pass > > -vectorize to opt or -mllvm -vectorize to clang) and provide feedback. > > Especially in combination with -unroll-allow-partial, I have observed > > some significant benchmark speedups, but, I have also observed some > > significant slowdowns. I would like to share my thoughts, and hopefully > > get feedback, on next steps. > > Hi Hal, > > I haven't had a chance to look at your pass in detail, but here are some opinions: :) > > > 1. "Target Data" for vectorization - I think that in order to improve > > the vectorization quality, the vectorizer will need more information > > about the target. This information could be provided in the form of a > > kind of extended target data. This extended target data might contain: > > - What basic types can be vectorized, and how many of them will fit > > into (the largest) vector registers > > - What classes of operations can be vectorized (division, conversions / > > sign extension, etc. are not always supported) > > - What alignment is necessary for loads and stores > > - Is scalar-to-vector free? > > I think that this will be a really important API, but I strongly advocate that you model this after TargetLoweringInfo instead of TargetData. First, TargetData isn't actually a target API (it should be fixed, I filed PR11936 to track this). Second, targets will have to implement imperative code to return precise answers to questions. For example, you'll want something like "what is the cost of a shuffle with this mask" which will be extremely target specific, will depend on what CPU subfeatures are enabled, etc.This makes sense. What do you think will be the best way of synchronizing things like CPU subfeatures between this API and the backend target libraries? They could be linked directly, although I don't know if we want to do that. tablegen could extract a bunch of this information into separate objects that get linked into opt.> > When you start working on this, I strongly encourage you to propose the API you want here. Start small and add features as you go. > > > 2. Feedback between passes - We may to implement a closer coupling > > between optimization passes than currently exists. Specifically, I have > > in mind two things: > > - The vectorizer should communicate more closely with the loop > > unroller. First, the loop unroller should try to unroll to preserve > > maximal load/store alignments. Second, I think it would make a lot of > > sense to be able to unroll and, only if this helps vectorization should > > the unrolled version be kept in preference to the original. With basic > > block vectorization, it is often necessary to (partially) unroll in > > order to vectorize. Even when we also have real loop vectorization, > > however, I still think that it will be important for the loop unroller > > to communicate with the vectorizer. > > I really disagree with this, see below. > > > 3. Loop vectorization - It would be nice to have, in addition to > > basic-block vectorization, a more-traditional loop vectorization pass. I > > think that we'll need a better loop analysis pass in order for this to > > happen. Some of this was started in LoopDependenceAnalysis, but that > > pass is not yet finished. We'll need something like this to recognize > > affine memory references, etc. > > I think that a loop vectorizor and a basic block vectorizer both make perfect sense and are important for different classes of code. However, I don't think that we should go down the path of trying to use a "basic block vectorizor + loop unrolling" serve the purpose of a loop vectorizer. Trying to make a BBVectorizer and a loop unroller play together will be really fragile, because they'll both have to duplicate the same metrics (otherwise, for example, you'd unroll a loop that isn't vectorizable). This will also be a huge hit to compile time.The only problem with this comes from loops for which unrolling is necessary to expose vectorization because the memory access pattern is too complicated to model in more-traditional loop vectorization. This generally is useful only in cases with a large number of flops per memory operation (or maybe integer ops too, but I have less experience with those), so maybe we can design a useful heuristic to handle those cases. That having been said, unroll+(failed vectorize)+rollback is not really any more expensive at compile time than unroll+(failed vectorize) except that the resulting code would run faster (actually it is cheaper to compile because the optimization/compilation of the unvectorized unrolled loop code takes longer than the non-unrolled loop). There might be a clean way of doing this; I'll think about it. Thanks again, Hal> > -Chris-- Hal Finkel Postdoctoral Appointee Leadership Computing Facility Argonne National Laboratory
I have a super-simple test case 4x4 matrix * 4-vector which gets correctly unrolled, but is not vectorized by -bb-vectorize. (I used llvm 3.1svn) I attached the test case so you can see what is going wrong there. 2012/2/3 Hal Finkel <hfinkel at anl.gov>> As some of you may know, I committed my basic-block autovectorization > pass a few days ago. I encourage anyone interested to try it out (pass > -vectorize to opt or -mllvm -vectorize to clang) and provide feedback. > Especially in combination with -unroll-allow-partial, I have observed > some significant benchmark speedups, but, I have also observed some > significant slowdowns. I would like to share my thoughts, and hopefully > get feedback, on next steps. > > 1. "Target Data" for vectorization - I think that in order to improve > the vectorization quality, the vectorizer will need more information > about the target. This information could be provided in the form of a > kind of extended target data. This extended target data might contain: > - What basic types can be vectorized, and how many of them will fit > into (the largest) vector registers > - What classes of operations can be vectorized (division, conversions / > sign extension, etc. are not always supported) > - What alignment is necessary for loads and stores > - Is scalar-to-vector free? > > 2. Feedback between passes - We may to implement a closer coupling > between optimization passes than currently exists. Specifically, I have > in mind two things: > - The vectorizer should communicate more closely with the loop > unroller. First, the loop unroller should try to unroll to preserve > maximal load/store alignments. Second, I think it would make a lot of > sense to be able to unroll and, only if this helps vectorization should > the unrolled version be kept in preference to the original. With basic > block vectorization, it is often necessary to (partially) unroll in > order to vectorize. Even when we also have real loop vectorization, > however, I still think that it will be important for the loop unroller > to communicate with the vectorizer. > - After vectorization, it would make sense for the vectorization pass > to request further simplification, but only on those parts of the code > that it modified. > > 3. Loop vectorization - It would be nice to have, in addition to > basic-block vectorization, a more-traditional loop vectorization pass. I > think that we'll need a better loop analysis pass in order for this to > happen. Some of this was started in LoopDependenceAnalysis, but that > pass is not yet finished. We'll need something like this to recognize > affine memory references, etc. > > I look forward to hearing everyone's thoughts. > > -Hal > > -- > Hal Finkel > Postdoctoral Appointee > Leadership Computing Facility > Argonne National Laboratory > > _______________________________________________ > LLVM Developers mailing list > LLVMdev at cs.uiuc.edu http://llvm.cs.uiuc.edu > http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev >-------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20120209/d8a6d21a/attachment.html> -------------- next part -------------- A non-text attachment was scrubbed... Name: matrix.c Type: text/x-csrc Size: 443 bytes Desc: not available URL: <http://lists.llvm.org/pipermail/llvm-dev/attachments/20120209/d8a6d21a/attachment.c>
Carl-Philip, The reason that this does not vectorize is that it cannot vectorize the stores; this leaves only the mul-add chains (and some chains with loads), and they only have a depth of 2 (the threshold is 6). If you give clang -mllvm -bb-vectorize-req-chain-depth=2 then it will vectorize. The reason the heuristic has such a large default value is to prevent cases where it costs more to permute all of the necessary values into and out of the vector registers than is saved by vectorizing. Does the code generated with -bb-vectorize-req-chain-depth=2 run faster than the unvectorized code? The heuristic can certainly be improved, and these kinds of test cases are very important to that improvement process. -Hal On Thu, 2012-02-09 at 13:27 +0100, Carl-Philip Hänsch wrote:> I have a super-simple test case 4x4 matrix * 4-vector which gets > correctly unrolled, but is not vectorized by -bb-vectorize. (I used > llvm 3.1svn) > I attached the test case so you can see what is going wrong there. > > 2012/2/3 Hal Finkel <hfinkel at anl.gov> > As some of you may know, I committed my basic-block > autovectorization > pass a few days ago. I encourage anyone interested to try it > out (pass > -vectorize to opt or -mllvm -vectorize to clang) and provide > feedback. > Especially in combination with -unroll-allow-partial, I have > observed > some significant benchmark speedups, but, I have also observed > some > significant slowdowns. I would like to share my thoughts, and > hopefully > get feedback, on next steps. > > 1. "Target Data" for vectorization - I think that in order to > improve > the vectorization quality, the vectorizer will need more > information > about the target. This information could be provided in the > form of a > kind of extended target data. This extended target data might > contain: > - What basic types can be vectorized, and how many of them > will fit > into (the largest) vector registers > - What classes of operations can be vectorized (division, > conversions / > sign extension, etc. are not always supported) > - What alignment is necessary for loads and stores > - Is scalar-to-vector free? > > 2. Feedback between passes - We may to implement a closer > coupling > between optimization passes than currently exists. > Specifically, I have > in mind two things: > - The vectorizer should communicate more closely with the > loop > unroller. First, the loop unroller should try to unroll to > preserve > maximal load/store alignments. Second, I think it would make a > lot of > sense to be able to unroll and, only if this helps > vectorization should > the unrolled version be kept in preference to the original. > With basic > block vectorization, it is often necessary to (partially) > unroll in > order to vectorize. Even when we also have real loop > vectorization, > however, I still think that it will be important for the loop > unroller > to communicate with the vectorizer. > - After vectorization, it would make sense for the > vectorization pass > to request further simplification, but only on those parts of > the code > that it modified. > > 3. Loop vectorization - It would be nice to have, in addition > to > basic-block vectorization, a more-traditional loop > vectorization pass. I > think that we'll need a better loop analysis pass in order for > this to > happen. Some of this was started in LoopDependenceAnalysis, > but that > pass is not yet finished. We'll need something like this to > recognize > affine memory references, etc. > > I look forward to hearing everyone's thoughts. > > -Hal > > -- > Hal Finkel > Postdoctoral Appointee > Leadership Computing Facility > Argonne National Laboratory > > _______________________________________________ > LLVM Developers mailing list > LLVMdev at cs.uiuc.edu http://llvm.cs.uiuc.edu > http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev >-- Hal Finkel Postdoctoral Appointee Leadership Computing Facility Argonne National Laboratory