[LLVMdev] dynamic data dependence extraction using llvm
mobi phil
mobi at mobiphil.com
Fri Dec 12 10:00:43 PST 2014
Thanks for sharing it... learned also something...
please share how you made the link llvm<->source using debug info
On Fri, Dec 12, 2014 at 5:55 PM, Henry Chung <zhguanwen at gmail.com> wrote:
>
> Dear Dibyendu and Mobi,
>
> Thanks for your help! :-)
>
> I finally figure it out. The solution is really simple. I just need to
> generate a new bitcode file with the following command:
> -----
> opt -mem2reg -indvars test1.bc -o test2.bc
> -----
> Then the load/store for induction variables will be removed and replaced
> by PHI instructions and all remaining load/store instructions are those I
> am interested in. I just simply insert my recording functions into it and
> everything is OK.
>
> Thanks again for your kindly help. :-)
>
> Best regards,
> Henry
>
> On Fri, Dec 12, 2014 at 5:46 PM, Das, Dibyendu <Dibyendu.Das at amd.com>
> wrote:
>>
>> This may not be very helpful but you can try one of these:
>>
>>
>>
>> a) Identify the loop-control-variable and other loop-induction
>> variables in the compiler and do not track the ld/st of these variables
>> (because you know how they behave)
>>
>> b) Create a separate section in the profile dump for the addresses
>> of the loop induction vars and during a post-pass you can do a special
>> handling for these addresses.
>>
>>
>>
>> *From:* Henry Chung [mailto:zhguanwen at gmail.com]
>> *Sent:* Friday, December 12, 2014 2:29 AM
>> *To:* Das, Dibyendu
>> *Cc:* LLVM Developers Mailing List
>> *Subject:* Re: [LLVMdev] dynamic data dependence extraction using llvm
>>
>>
>>
>> Dear Dibyendu,
>>
>>
>>
>> Thanks for your response. :-)
>>
>> > If you are looking for only dependences which are inter-iteration
>> (dependence distance != 0 ) you can do a post-pass on the ld/st addresses
>> collected
>>
>> Yes, I am more interested in inter-iteration dependence. Could you
>> provide more information or some links on post-pass approach? I have no
>> idea on your method. :-)
>>
>>
>>
>> > eliminate such intra-iteration dependences.
>>
>> For the intra-iteration dependences introduced by iteration (index)
>> variables, I just ignore. However, the "uninteresting ld/st" still can be
>> come from iteration(index) variables, such as i, j, k. For example, at the
>> end of the 4th iteration, we increase variable 'i' and introduce a store
>> instruction for 'i'. And at the beginning of the 5th iteration, we load the
>> same address of 'i', to see whether the loop condition is true or false.
>> Since I can not distinguish with the interesting and uninteresting ld/st, I
>> will get the two trace entries for the 'i' and produce a WAR dependence
>> with distance != 0.
>>
>> I just wonder how can I detect these kind of iteration (index)
>> variables, then I just need to do not insert recordload/store functions
>> into these "uninteresting" load/store instructions.
>>
>>
>>
>> Thanks,
>>
>> Henry
>>
>>
>>
>>
>>
>>
>>
>> On Thu, Dec 11, 2014 at 6:43 PM, Das, Dibyendu <Dibyendu.Das at amd.com>
>> wrote:
>>
>> I doubt there is any easy way to pick up ‘interesting ld/st’ and ignore
>> the rest. If you are looking for only dependences which are inter-iteration
>> (dependence distance != 0 ) you can do a post-pass on the ld/st addresses
>> collected and eliminate such intra-iteration dependences. Maybe there is a
>> smarter way J
>>
>>
>>
>> *From:* llvmdev-bounces at cs.uiuc.edu [mailto:llvmdev-bounces at cs.uiuc.edu] *On
>> Behalf Of *Henry Chung
>> *Sent:* Thursday, December 11, 2014 6:57 PM
>> *To:* LLVM Developers Mailing List
>> *Subject:* [LLVMdev] dynamic data dependence extraction using llvm
>>
>>
>>
>> Hi LLVM-ers,
>>
>>
>>
>> I try to develop my custom dynamic data dependence tool (focusing on
>> nested loops), currently I can successfully get the trace including
>> load/store address, loop information, etc.
>>
>>
>>
>> However, when I try to analyze dynamic data dependence based on the
>> pairwise method described in [1], the load/store for iteration variables
>> may interfere my analysis (I only care about the load/store for meaningful
>> load/store, eg, load/store for arrays).
>>
>>
>>
>> To be more precise and make the problem understandable, here is an simple
>> example:
>>
>> ------------------------------------
>>
>> My test example:
>>
>>
>>
>> for (j = 0; j < N-2; j++) {
>>
>> for (i = 1; i < N; i++) {
>>
>> x = a[i-1][j];
>>
>> a[i][j+2] = x + 1;
>>
>> }
>>
>> }
>>
>>
>>
>> The corresponding simplified llvm-IR is shown in below:
>>
>> *Beginning of simplified llvm-IR*
>>
>> entry:
>>
>> ...
>>
>> store i32 0, i32* %j, align4
>>
>> br label %for.cond
>>
>>
>>
>> for.cond:
>>
>> ...
>>
>> br ...
>>
>>
>>
>> for.body:
>>
>> store i32 1, i32* %i, align4
>>
>> br ...
>>
>>
>>
>> for.cond1:
>>
>> ...
>>
>>
>>
>> for.body3:
>>
>> ...
>>
>> %temp4 = load[10 x i32]** %a.addr, align 8
>>
>> ...
>>
>> store i32 %add, i32* %arrayidx10, align4
>>
>> br ...
>>
>>
>>
>> ... ...
>>
>> *End of simplified llvm-IR*
>>
>>
>>
>> The general idea to obtain the dynamic data dependence is that 1. get and
>> record corresponding load/store addresses; 2. analyze load/store addresses
>> in different iterations to figure out RAW, WAR or WAW dependence.
>>
>>
>>
>> However, as we can see in the llvm-IR, apart from load/store instructions
>> for array accesses we interested, there are lots of load/store instructions
>> for iteration variables, i and j for the above example. And these noise
>> load/store instructions will affect whether we have dependencies across
>> loop iterations (loop-carried dependence) and dependence distance
>> calculation.
>>
>>
>>
>> Initially, I try to only focus on analyze the address in basic blocks
>> containing "for.body", but it might be a problem if we have if-else
>> statement in source codes and sometimes it also has load/store for
>> iteration variables in basic blocks containing "for.body". Therefore, this
>> approach can not solve my problem.
>>
>>
>>
>> Any suggestion for my problem?
>>
>>
>>
>> Thanks,
>>
>> Henry
>>
>> ------------------------------------
>>
>>
>>
>>
>>
>>
>>
>> [1]. Minjang Kim, Hyesoon Kim, and Chi-Keung Luk. 2010. SD3: A Scalable
>> Approach to Dynamic Data-Dependence Profiling, MICRO2010
>>
>>
>>
>>
>>
>
>
>
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--
rgrds,
mobi phil
being mobile, but including technology
http://mobiphil.com
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