<div dir="ltr">Hi Farad, > <span style="color:rgb(0,0,0);font-family:Arial;font-size:14.6667px;white-space:pre-wrap">Thanks for such a quick response! And thanks for answering my questions!

> </span><span style="font-family:Arial;font-size:14.6667px;white-space:pre-wrap">Thanks for explaining this part with the example!

> </span><span style="font-family:Arial;font-size:14.6667px;white-space:pre-wrap">Thanks for suggesting this! I will try to do this and get back to you.

No problem, always here to help. Let me reiterate: Don't hesitate to ask questions as you progress! My description

I hope was useful to get started but it's certainly not enough (talking from experience).

> </span><span style="font-family:Arial;font-size:14.6667px;white-space:pre-wrap">I know about some standard fix-point lattice-based data flow analysis like...

That's good, it's useful.

> </span><span style="font-family:Arial;font-size:14.6667px;white-space:pre-wrap">I see. It would be great if we can come up with some specific examples where using these deduced attributes can improve existing inter and intra procedural optimization passes. I am very interested to work towards exploring this potential of Attributor.

I think everyone involved in the Attributor, in one way or another, thinks about how other analyses can benefit from it. So, yeah, you're welcome. :)

Also, probably this will be a very interesting panel discussion for you: </span><a href="https://www.youtube.com/watch?v=cC2cspQgSxM">https://www.youtube.com/watch?v=cC2cspQgSxM</a><span style="font-family:Arial;font-size:14.6667px;white-space:pre-wrap">

> </span><span style="font-family:Arial;font-size:14.6667px;white-space:pre-wrap">It would be great if you can share some of the ongoing issues or discussion regarding improving Liveness information deduction using Attributor.

I'll refrain from doing that right now, mainly because I'm afraid that I may lead you to a wrong direction (or too soon at least). So, let's wait for Johannes

to get in touch and we will certainly discuss it. I'd suggest that we try to familiarize yourself with the Attributor in the meantime, which is a certainly

important immediate goal.

Best,

Stefanos Baziotis</div> <div class="gmail_quote"><div dir="ltr" class="gmail_attr">Στις Σάβ, 14 Μαρ 2020 στις 12:05 π.μ., ο/η Fahad Nayyar <<a href="mailto:fahad17049@iiitd.ac.in">fahad17049@iiitd.ac.in</a>> έγραψε: </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><div dir="ltr"><span id="gmail-m_5520322452696191483gmail-docs-internal-guid-b79031a7-7fff-b067-914a-3cdb08399317"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Dear Stefanos, <p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Thanks for such a quick response! And thanks for answering my questions! <p dir="ltr" style="line-height:1.38;margin-left:36pt;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">> Starting off, understanding the theory of data-flow analysis can help.<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">I know about some standard fix-point lattice-based data flow analysis like reaching definitions, live variable analysis, etc. I have done a course on <span style="font-size:11pt;font-family:Arial;font-weight:700;font-style:italic;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">“Program analysis” <span style="font-size:11pt;font-family:Arial;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">at my college.  <p dir="ltr" style="line-height:1.38;margin-left:36pt;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">> The deduction of these attributes is inter-connected, which is the whole point of the Attributor<span style="font-size:11pt;font-family:Arial;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. <p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Thanks for explaining this part with the example!  <p dir="ltr" style="line-height:1.38;margin-left:36pt;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">> I'd suggest that you try to run the Attributor and follow a specific attribute's updates and see what it tries to deduce. That is, see its updateImpl().<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Thanks for suggesting this! I will try to do this and get back to you. <p dir="ltr" style="line-height:1.38;margin-left:36pt;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">> At this point, LLVM is focused on heavy inlining, which while very useful, you'll lose a lot of the interprocedural information.<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">I see. It would be great if we can come up with some specific examples where using these deduced attributes can improve existing inter and intra procedural optimization passes. I am very interested to work towards exploring this potential of Attributor. So I would try to include such examples in my GSOC proposal. <p dir="ltr" style="line-height:1.38;margin-left:36pt;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">> Liveness is certainly something that we're currently trying to improve and I don't think we'll ever stop.<span style="font-size:11pt;font-family:Arial;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> <p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">It would be great if you can share some of the ongoing issues or discussion regarding improving Liveness information deduction using Attributor. <p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Thanks and regards<p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Fahad Nayyar </div> <div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Sat, Mar 14, 2020 at 2:44 AM Stefanos Baziotis <<a href="mailto:stefanos.baziotis@gmail.com" target="_blank">stefanos.baziotis@gmail.com</a>> wrote: </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr">Hi Fahad, We're all happy to see you being interested in LLVM! More so in the Attributor! I'm a relatively new contributor so I think I can help. Please note that the Attributor, apart from Johannes (who CC'd), has at least another 2 great contributors, Hideto and Stefan (who I also CC'd). They were among the initial creators. In the rest of this post I'll try to help you familiarize yourself with the Attributor and maybe answer your questions. Johannes can then give you specific things to do to get started. Starting off, understanding the theory of data-flow analysis can help. I'd say don't get too hang up on it, you just have to understand the idea of fix-point analyses. I don't how much you know about the Attributor, so I'll defer a too long (or too beginner) description because you might already know a lot of things. You can of course any specific questions you want: A summary is: The Attributor tries to deduce attributes in different points of an LLVM IR program (you can see that in the video). The deduction of these attributes is inter-connected, which is the whole point of the Attributor. The attributes "ask" one another for information. For example, one attribute tries to see if a load loads from null pointer. But the pointer operand might be non-constant (like %v in LLVM IR). Well, another attribute, whose job is to do value simplification (i.e. constant folding / propagation etc.) might have folded that (%v) into the constant null. So, the former can ask him. These connections give the power and the complexity. The attributes have a state, that changes. When the state stops changing, it has reached a fixpoint, at which point the deduction of it stops. From the initialization of the attribute until a fixpoint is reached, the state changes in updates (called updateImpl() in the source code). This is where attributes try to deduce new things, ask one another and eventually try to reach a fixpoint. Finally, a fixpoint can be enforced. Because if we for some reason never stop changing, it would run forever. Note however that attributes should be programmed in a way that fixpoint should be able to be reached (This is where theory might help a little). I'd suggest that you try to run the Attributor and follow a specific attribute's updates and see what it tries to deduce. That is, see its updateImpl(). With a couple of prints you can get a good idea of what it does and what info it gets from other attributes (and when it stops). You can of course ask us if you're interested in a specific one, if there's something you don't understand etc. Now, to (try to) answer your questions and hopefully other people can help. > <span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;vertical-align:baseline">How Attributor can help for standard inter-procedural and intra-procedural analysis passes of LLVm. I’ve seen the tutorial<span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;font-weight:700;vertical-align:baseline"> [4]<span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;vertical-align:baseline">. I would like to discuss ways of improving other optimization passes similarly (or some examples which have already been implemented). The Attributor AFAIK is self-contained. It's not in "production" yet and so it's not connected with other passes. At this point, LLVM is focused on heavy inlining, which while very useful, you'll lose a lot of the interprocedural information. Note that there are other transforms that do Inter-Procedural Optimization (<a href="https://github.com/llvm/llvm-project/tree/master/llvm/lib/Transforms/IPO" target="_blank">https://github.com/llvm/llvm-project/tree/master/llvm/lib/Transforms/IPO</a>) but they don't follow the idea of the Attributor. But they might follow a fix-point analysis. <span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;vertical-align:baseline">> Improve dynamic memory related capabilities of Attributor. For example Improve <span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;font-weight:700;vertical-align:baseline">HeapToStackConversions<span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;vertical-align:baseline">. Maybe such deductions can help safety (dis)provers. For example, can we improve the <span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;font-weight:700;vertical-align:baseline">use-after-free bug detection<span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;vertical-align:baseline"> using some attributes?

Stefan should know more about H2S. Regarding the use-after-free, I don't think there's currently any plans for it directly, but they can be I assume. > <span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;vertical-align:baseline">Improve Liveness related capabilities of Attributor. Again I want to consider whether some attribute deduction can help liveness (dis)provers. For example <span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;font-weight:700;vertical-align:baseline">NoReturn, WillReturn<span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;vertical-align:baseline"> can be improved. I am sure these 2 attributes do not cover all the cases as it is an undecidable problem. But I was wondering whether there is room for improvement in their deduction mechanism.

Liveness is certainly something that we're currently trying to improve and I don't think we'll ever stop. Most of the attributes interact with the deadness attribute (AAIsDead) both for asking it info and

providing it info (i.e. the undefined-behavior attribute hopefully will at some point be able to tell AAIsDead that a block is dead because it contains UB).

> </span><span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;vertical-align:baseline">Is there any attribute that tells whether a function has </span><span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;font-weight:700;vertical-align:baseline">side-effects</span><span style="color:rgb(0,0,0);font-family:Arial;font-size:11pt;font-variant-numeric:normal;font-variant-east-asian:normal;white-space:pre-wrap;background-color:transparent;vertical-align:baseline"> (does it always gives the same output for the same input? Or does it affect some global variable directly or indirectly?)?

No AFAIK, although you might be interested in this: </span><a href="https://reviews.llvm.org/D74691#1887983" target="_blank">https://reviews.llvm.org/D74691#1887983</a><br><br>I hope this was helpful! Don't hesitate to ask any questions.<br><br>Kind regards,<br>Stefanos Baziotis</div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">Στις Παρ, 13 Μαρ 2020 στις 10:25 μ.μ., ο/η Fahad Nayyar via llvm-dev <<a href="mailto:llvm-dev@lists.llvm.org" target="_blank">llvm-dev@lists.llvm.org</a>> έγραψε:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div dir="ltr"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Hi all,</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">My name is Fahad Nayyar. I am an undergraduate student from India. </span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">I am interested to participate in GSOC under the project </span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">“Improve inter-procedural analyses and optimizations”. </span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">I have been using LLVM for the past 8 months. I have written various intra-procedural analysis in LLVM as FunctionPass for my course projects and research projects. But I’ve not contributed to the LLVM community yet. I am very excited to contribute to LLVM! </span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">I am not too familiar with the inter-procedural analysis infrastructure of LLVM. I have written small toy inter-procedural dataflow analysis (like taint analysis, reaching definitions, etc) for JAVA programs using SOOT tool <b>[5].</b> I am familiar with the theory of inter-procedural analysis (I’ve read some chapters of  </span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">[1]</span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">,  </span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">[2] </span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">and</span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> [3]</span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"> for this).</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">I am trying to understand the LLVM’s </span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Attributor framework</span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">. I am interested in these 3 aspects:</span></p><ol style="margin-top:0px;margin-bottom:0px"><li dir="ltr" style="margin-left:15px;list-style-type:decimal;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline">How Attributor can help for standard inter-procedural and intra-procedural analysis passes of LLVm. I’ve seen the tutorial</span><span style="font-size:11pt;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline"> [4]</span><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline">. I would like to discuss ways of improving other optimization passes similarly (or some examples which have already been implemented).</span></p></li><li dir="ltr" style="margin-left:15px;list-style-type:decimal;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline">Improve dynamic memory related capabilities of Attributor. For example Improve </span><span style="font-size:11pt;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline">HeapToStackConversions</span><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline">. Maybe such deductions can help safety (dis)provers. For example, can we improve the </span><span style="font-size:11pt;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline">use-after-free bug detection</span><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline"> using some attributes?</span></p></li><li dir="ltr" style="margin-left:15px;list-style-type:decimal;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline">Improve Liveness related capabilities of Attributor. Again I want to consider whether some attribute deduction can help liveness (dis)provers. For example </span><span style="font-size:11pt;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline">NoReturn, WillReturn</span><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline"> can be improved. I am sure these 2 attributes do not cover all the cases as it is an undecidable problem. But I was wondering whether there is room for improvement in their deduction mechanism.</span></p></li><li dir="ltr" style="margin-left:15px;list-style-type:decimal;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline">Can we optimize the attribute deduction algorithm to reduce compile time?</span></p></li><li dir="ltr" style="margin-left:15px;list-style-type:decimal;font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap"><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline">Is there any attribute that tells whether a function has </span><span style="font-size:11pt;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline">side-effects</span><span style="font-size:11pt;background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline"> (does it always gives the same output for the same input? Or does it affect some global variable directly or indirectly?)?</span></p></li></ol><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">It would be great if Johannes can provide me some TODOs before submitting my proposal. Also please tell some specific IPO improvement goals which you have in mind for this project. I would be most interested in memory-related attributes, liveness deductions from attributes and measurable better IPO using attribute deduction.</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">Thanks and Regards.</span></p><br><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">References:</span></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">[1] </span><a href="https://www.springer.com/gp/book/9783540654100" style="text-decoration-line:none" target="_blank"><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Principles of Program Analysis.</span></a></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">[2] </span><a href="https://dl.acm.org/doi/book/10.5555/1592955" style="text-decoration-line:none" target="_blank"><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Data Flow Analysis: Theory and Practice.</span></a></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">[3] </span><a href="https://cs.au.dk/~amoeller/spa/spa.pdf" style="text-decoration-line:none" target="_blank"><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Static Program Analysis.</span></a></p><p dir="ltr" style="line-height:1.38;margin-top:0pt;margin-bottom:0pt"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;vertical-align:baseline;white-space:pre-wrap">[4] </span><a href="https://www.youtube.com/watch?v=HVvvCSSLiTw" style="text-decoration-line:none" target="_blank"><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">2019 LLVM Developers’ Meeting: J. Doerfert “The Attributor: A Versatile Inter-procedural Fixpoint.."</span></a></p><span style="font-variant-numeric:normal;font-variant-east-asian:normal;background-color:transparent;font-size:11pt;font-family:Arial;color:rgb(0,0,0);font-weight:700;vertical-align:baseline;white-space:pre-wrap">[5] </span><a href="https://github.com/Sable/soot" style="text-decoration-line:none" target="_blank"><span style="font-size:11pt;font-family:Arial;background-color:transparent;font-weight:700;font-variant-numeric:normal;font-variant-east-asian:normal;text-decoration-line:underline;vertical-align:baseline;white-space:pre-wrap">Soot - A Java optimization framework</span></a><br><br><br><br><img width="0" height="0" alt="" style="display: flex;"></div>

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