llvm dev - Apr 2014 - [LLVMdev] multithreaded performance disaster with -fprofile-instr-generate (contention on profile counters)

On Apr 23, 2014, at 7:31 AM, Kostya Serebryany <kcc at google.com> wrote:
> I've run one proprietary benchmark that reflects a large portion of the
google's server side code.
> -fprofile-instr-generate leads to 14x slowdown due to counter contention.
That's serious.
> Admittedly, there is a single hot function that accounts for half of that
slowdown,
> but even if I rebuild that function w/o -fprofile-instr-generate, the
slowdown remains above 5x.
> This is not a toy code that I've written to prove my point -- this is
real code one may want to profile with -fprofile-instr-generate.
> We need another approach for threaded code. 
> 
> There is another ungood feature of the current instrumentation. Consider
this function:
> std::vector<int> v(1000);
> void foo() { v[0] = 42; }
> 
> Here we have a single basic block and a call, but since the coverage is
emitted by the
> FE before inlining (and is also emitted for std::vector methods) we get
this assembler at -O2:
> 0000000000400b90 <_Z3foov>:
>   400b90:       48 ff 05 11 25 20 00    incq   0x202511(%rip)        #
6030a8 <__llvm_profile_counters__Z3foov>
>   400b97:       48 ff 05 42 25 20 00    incq   0x202542(%rip)        #
6030e0 <__llvm_profile_counters__ZNSt6vectorIiSaIiEEixEm>
>   400b9e:       48 8b 05 4b 26 20 00    mov    0x20264b(%rip),%rax        #
6031f0 <v>
>   400ba5:       c7 00 2a 00 00 00       movl   $0x2a,(%rax)
>   400bab:       c3                      retq   
> 
> Suddenly, an innocent function that uses std::vector becomes a terrible
point of contention.
> Full test case below, -fprofile-instr-generate leads to 10x slowdown. 
> 
> ========================> 
> Now, here is a more detailed proposal of logarithmic self-cooling counter
mentioned before. Please comment.
> The counter is a number of the form (2^k-1). 
> It starts with 0.
> After the first update it is 1.
> After *approximately* 1 more update it becomes 3
> After *approximately* 2 more updates it becomes 7
> After *approximately* 4 more updates it becomes 15
> ...
> After *approximately* 2^k more updates it becomes 2^(k+2)-1
> 
> The code would look like this:
>   if ((fast_thread_local_rand() & counter) == 0)
>     counter = 2 * counter + 1;
> 
> Possible implementation for fast_thread_local_rand: 
> long fast_thread_local_rand() {
>   static __thread long r;
>   return r++;
> }
> Although I would try to find something cheaper that this. (Ideas?)
> 
> 
> The counter is not precise (well, the current racy counters are not precise
either).
> But statistically it should be no more than 2x away from the real counter. 
> Will this accuracy be enough for the major use cases? 
> 
> Moreover, this approach allows to implement the counter increment using a
callback:
>   if ((fast_thread_local_rand() & counter) == 0)  
__cov_increment(&counter);
> which in turn will let us use the same hack as in AsanCoverage: use the PC
to map the counter to the source code.
> (= no need to create separate section in the objects).
> 
> Thoughts? 
> 
> —kcc
I can see that the behavior of our current instrumentation is going to be a
problem for the kinds of applications that you’re looking at. If you can find a
way to get the overhead down without losing accuracy and without hurting the
performance for applications without significant contention, then we can just
adopt that. If you can’t do it without tradeoffs, then we should have a separate
option to let those who care switch between different kinds of instrumentation.

Using the PC to map to the source code is simply not going to work with
-fprofile-instr-generate. The mapping from counter values to the user-visible
execution counts is complex and relies on detailed knowledge of the clang ASTs.
> 
> 
> % clang++ -O2 -lpthread coverage_mt_vec.cc && time ./a.out 
> TIME: real: 0.219; user: 0.430; system: 0.000
> % clang++ -O2 -lpthread -fprofile-instr-generate coverage_mt_vec.cc
&& time ./a.out
> TIME: real: 3.743; user: 7.280; system: 0.000
> 
> % cat coverage_mt_vec.cc
> #include <pthread.h>
> #include <vector>
> 
> std::vector<int> v(1000);
> 
> __attribute__((noinline)) void foo() { v[0] = 42; }
>   
> void *Thread1(void *) {
>   for (int i = 0; i < 100000000; i++)
>     foo(); 
>   return 0;
> }   
> 
> __attribute__((noinline)) void bar() { v[999] = 66; }
> 
> void *Thread2(void *) {
>   for (int i = 0; i < 100000000; i++)
>     bar();
>   return 0;
> } 
>   
> int main() {
>   static const int kNumThreads = 16;
>   pthread_t t[kNumThreads];
>   pthread_create(&t[0], 0, Thread1, 0);
>   pthread_create(&t[1], 0, Thread2, 0);
>   pthread_join(t[0], 0);
>   pthread_join(t[1], 0);
>   return 0;
> }
> 
> 
> 
> 
> On Fri, Apr 18, 2014 at 11:45 PM, Xinliang David Li <xinliangli at
gmail.com> wrote:
> 
> 
> 
> On Fri, Apr 18, 2014 at 12:13 AM, Dmitry Vyukov <dvyukov at
google.com> wrote:
> Hi,
> 
> This is long thread, so I will combine several comments into single email.
> 
> 
> >> - 8-bit per-thread counters, dumping into central counters on
overflow.
> >The overflow will happen very quickly with 8bit counter.
> 
> Yes, but it reduces contention by 256x (a thread must execute at least 256
loop iterations between increments). In practice, if you reduce contention below
some threshold, it does not represent a problem anymore.
> 
> 
> 
> >> - per-thread counters. Solves the problem at huge cost in RAM
per-thread
> >It is not practical. Especially for TLS counters -- it creates huge
pressure on stack memory.
> 
> Do we have any numbers about number of counters? If there are 100K 1-byte
counters, I would consider it as practical.
> 
> 
> A medium sized app I looked at has about 10M counters (arcs only).  It is
also not uncommon to see such apps running with hundreds of threads.
> 
>  
> 
> 
> 
> 
> > In Google GCC, we implemented another technique which proves to be
very effective -- it is called FDO sampling.
> > Basically counters will be updated every N samples.
> 
> How does it work?
> 
> Similar to how occurrences based PMU sampling work. Setting sampling period
to 100 can reduce the instrumentation overhead by close to 100x without
introducing much precision loss.
>  
> 
> 
> 
> >> It seems to me like we’re going to have a hard time getting good
multithreaded performance without significant impact on the single-threaded
behavior.
> > I don't really agree.
> 
> 
> >We are talking about developers here. Nobody would know the exact
thread counts, but developers know the ballpark number
> 
> I strongly believe that we must relief developers from this choice during
build time, and do our best to auto-tune (if the final scheme requires tuning).
> 
> 
> That really depends.   If the tuning space is small, it won't be a
problem for the developer/builder.
> 
>  
> First, such questions puts unnecessary burden on developers. We don't
ask what register allocation algorithm to use for each function, right?
> 
> Crazy developers can actually do that via internal options, but this is
totally different case.  People just needs one flag to turn on/off sharding.
When sharding is on, compiler can pick the best 'N' according to some
heuristics at compile time.
> 
>  
> Second, there are significant chances we will get a wrong answer, because
e.g. developer's view of how threaded is the app can differ from reality or
from our classification.
> Third, the app can be build by a build engineer; or the procedure can be
applied to a base with 10'000 apps; or threaded-ness can change; or the app
can work in different modes; or the app can have different phases.
> 
> 
> We have forgotten to mention the benefit of implementation simplicity.  If
the static/simple solution solves the problem for most of the use cases,
designing fancy dynamic solution sounds like over-engineering to me.  It
(overhead reduction technique) may also get in the way of easier functional
enhancement in the future.
> 
>  David
> 
> 
> _______________________________________________
> LLVM Developers mailing list
> LLVMdev at cs.uiuc.edu         http://llvm.cs.uiuc.edu
> http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev
> 
> 
> _______________________________________________
> LLVM Developers mailing list
> LLVMdev at cs.uiuc.edu         http://llvm.cs.uiuc.edu
> http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev
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>
>
> I can see that the behavior of our current instrumentation is going to be
> a problem for the kinds of applications that you’re looking at. If you can
> find a way to get the overhead down without losing accuracy and without
> hurting the performance for applications without significant contention,
> then we can just adopt that. If you can’t do it without tradeoffs, then we
> should have a separate option to let those who care switch between
> different kinds of instrumentation.
>
So far none of the ideas we discussed can guarantee the same
single-threaded performance, memory consumption and functionality compared
to the current -fprofile-instr-generate.
We'll keep looking for the solution (and continue using AsanCoverage in the
meantime).
I am really surprised that the applications you care about do not have this
issue, but you probably know better :)

--kcc
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On Wed, Apr 23, 2014 at 10:48 PM, Bob Wilson <bob.wilson at apple.com>
wrote:
> On Apr 23, 2014, at 7:31 AM, Kostya Serebryany <kcc at google.com>
wrote:
>
> I've run one proprietary benchmark that reflects a large portion of the
> google's server side code.
> -fprofile-instr-generate leads to 14x slowdown due to counter contention.
> That's serious.
> Admittedly, there is a single hot function that accounts for half of that
> slowdown,
> but even if I rebuild that function w/o -fprofile-instr-generate, the
> slowdown remains above 5x.
> This is not a toy code that I've written to prove my point -- this is
real
> code one may want to profile with -fprofile-instr-generate.
> We need another approach for threaded code.
>
> There is another ungood feature of the current instrumentation. Consider
> this function:
> std::vector<int> v(1000);
> void foo() { v[0] = 42; }
>
> Here we have a single basic block and a call, but since the coverage is
> emitted by the
> FE before inlining (and is also emitted for std::vector methods) we get
this
> assembler at -O2:
> 0000000000400b90 <_Z3foov>:
>   400b90:       48 ff 05 11 25 20 00    incq   0x202511(%rip)        #
> 6030a8 <__llvm_profile_counters__Z3foov>
>   400b97:       48 ff 05 42 25 20 00    incq   0x202542(%rip)        #
> 6030e0 <__llvm_profile_counters__ZNSt6vectorIiSaIiEEixEm>
>   400b9e:       48 8b 05 4b 26 20 00    mov    0x20264b(%rip),%rax        #
> 6031f0 <v>
>   400ba5:       c7 00 2a 00 00 00       movl   $0x2a,(%rax)
>   400bab:       c3                      retq
>
> Suddenly, an innocent function that uses std::vector becomes a terrible
> point of contention.
> Full test case below, -fprofile-instr-generate leads to 10x slowdown.
>
> ========================>
> Now, here is a more detailed proposal of logarithmic self-cooling counter
> mentioned before. Please comment.
> The counter is a number of the form (2^k-1).
> It starts with 0.
> After the first update it is 1.
> After *approximately* 1 more update it becomes 3
> After *approximately* 2 more updates it becomes 7
> After *approximately* 4 more updates it becomes 15
> ...
> After *approximately* 2^k more updates it becomes 2^(k+2)-1
>
> The code would look like this:
>   if ((fast_thread_local_rand() & counter) == 0)
>     counter = 2 * counter + 1;
>
> Possible implementation for fast_thread_local_rand:
> long fast_thread_local_rand() {
>   static __thread long r;
>   return r++;
> }
> Although I would try to find something cheaper that this. (Ideas?)
>
>
> The counter is not precise (well, the current racy counters are not precise
> either).
> But statistically it should be no more than 2x away from the real counter.
> Will this accuracy be enough for the major use cases?
>
> Moreover, this approach allows to implement the counter increment using a
> callback:
>   if ((fast_thread_local_rand() & counter) == 0)
> __cov_increment(&counter);
> which in turn will let us use the same hack as in AsanCoverage: use the PC
> to map the counter to the source code.
> (= no need to create separate section in the objects).
>
> Thoughts?
>
> —kcc
>
>
> I can see that the behavior of our current instrumentation is going to be a
> problem for the kinds of applications that you’re looking at. If you can
> find a way to get the overhead down without losing accuracy
What are your requirements for accuracy?
Current implementation does not provide 100% accuracy, so it's
something less than 100%. What is it?
What use cases for numeric counters (as opposed to bool flag) do we
need to support? Is it only feedback-driven optimizations?

> and without
> hurting the performance for applications without significant contention,
What is the acceptable threshold? 0.01% would be fine, right? What is
the maximum value that you are ready to agree with?

> then we can just adopt that. If you can’t do it without tradeoffs, then we
> should have a separate option to let those who care switch between
different
> kinds of instrumentation.
>
> Using the PC to map to the source code is simply not going to work with
> -fprofile-instr-generate. The mapping from counter values to the
> user-visible execution counts is complex and relies on detailed knowledge
of
> the clang ASTs.
>
>
>
> % clang++ -O2 -lpthread coverage_mt_vec.cc && time ./a.out
> TIME: real: 0.219; user: 0.430; system: 0.000
> % clang++ -O2 -lpthread -fprofile-instr-generate coverage_mt_vec.cc
&& time
> ./a.out
> TIME: real: 3.743; user: 7.280; system: 0.000
>
> % cat coverage_mt_vec.cc
> #include <pthread.h>
> #include <vector>
>
> std::vector<int> v(1000);
>
> __attribute__((noinline)) void foo() { v[0] = 42; }
>
> void *Thread1(void *) {
>   for (int i = 0; i < 100000000; i++)
>     foo();
>   return 0;
> }
>
> __attribute__((noinline)) void bar() { v[999] = 66; }
>
> void *Thread2(void *) {
>   for (int i = 0; i < 100000000; i++)
>     bar();
>   return 0;
> }
>
> int main() {
>   static const int kNumThreads = 16;
>   pthread_t t[kNumThreads];
>   pthread_create(&t[0], 0, Thread1, 0);
>   pthread_create(&t[1], 0, Thread2, 0);
>   pthread_join(t[0], 0);
>   pthread_join(t[1], 0);
>   return 0;
> }
>
>
>
>
> On Fri, Apr 18, 2014 at 11:45 PM, Xinliang David Li <xinliangli at
gmail.com>
> wrote:
>>
>>
>>
>>
>> On Fri, Apr 18, 2014 at 12:13 AM, Dmitry Vyukov <dvyukov at
google.com>
>> wrote:
>>>
>>> Hi,
>>>
>>> This is long thread, so I will combine several comments into single
>>> email.
>>>
>>>
>>> >> - 8-bit per-thread counters, dumping into central counters
on
>>> >> overflow.
>>> >The overflow will happen very quickly with 8bit counter.
>>>
>>> Yes, but it reduces contention by 256x (a thread must execute at
least
>>> 256 loop iterations between increments). In practice, if you reduce
>>> contention below some threshold, it does not represent a problem
anymore.
>>>
>>>
>>>
>>> >> - per-thread counters. Solves the problem at huge cost in
RAM
>>> >> per-thread
>>> >It is not practical. Especially for TLS counters -- it creates
huge
>>> > pressure on stack memory.
>>>
>>> Do we have any numbers about number of counters? If there are 100K
1-byte
>>> counters, I would consider it as practical.
>>>
>>
>> A medium sized app I looked at has about 10M counters (arcs only).  It
is
>> also not uncommon to see such apps running with hundreds of threads.
>>
>>
>>>
>>>
>>>
>>>
>>>
>>> > In Google GCC, we implemented another technique which proves
to be very
>>> > effective -- it is called FDO sampling.
>>> > Basically counters will be updated every N samples.
>>>
>>> How does it work?
>>
>>
>> Similar to how occurrences based PMU sampling work. Setting sampling
>> period to 100 can reduce the instrumentation overhead by close to 100x
>> without introducing much precision loss.
>>
>>>
>>>
>>>
>>>
>>> >> It seems to me like we’re going to have a hard time
getting good
>>> >> multithreaded performance without significant impact on
the single-threaded
>>> >> behavior.
>>> > I don't really agree.
>>>
>>>
>>> >We are talking about developers here. Nobody would know the
exact thread
>>> > counts, but developers know the ballpark number
>>>
>>> I strongly believe that we must relief developers from this choice
during
>>> build time, and do our best to auto-tune (if the final scheme
requires
>>> tuning).
>>
>>
>>
>> That really depends.   If the tuning space is small, it won't be a
problem
>> for the developer/builder.
>>
>>
>>>
>>> First, such questions puts unnecessary burden on developers. We
don't ask
>>> what register allocation algorithm to use for each function, right?
>>
>>
>> Crazy developers can actually do that via internal options, but this is
>> totally different case.  People just needs one flag to turn on/off
sharding.
>> When sharding is on, compiler can pick the best 'N' according
to some
>> heuristics at compile time.
>>
>>
>>>
>>> Second, there are significant chances we will get a wrong answer,
because
>>> e.g. developer's view of how threaded is the app can differ
from reality or
>>> from our classification.
>>> Third, the app can be build by a build engineer; or the procedure
can be
>>> applied to a base with 10'000 apps; or threaded-ness can
change; or the app
>>> can work in different modes; or the app can have different phases.
>>>
>>
>> We have forgotten to mention the benefit of implementation simplicity. 
If
>> the static/simple solution solves the problem for most of the use
cases,
>> designing fancy dynamic solution sounds like over-engineering to me. 
It
>> (overhead reduction technique) may also get in the way of easier
functional
>> enhancement in the future.
>>
>>  David
>>
>>
>> _______________________________________________
>> LLVM Developers mailing list
>> LLVMdev at cs.uiuc.edu         http://llvm.cs.uiuc.edu
>> http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev
>>
>
> _______________________________________________
> LLVM Developers mailing list
> LLVMdev at cs.uiuc.edu         http://llvm.cs.uiuc.edu
> http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev
>
>

(Sorry to jump in before reading the whole thread...)

On 2014-Apr-24, at 1:33, Dmitry Vyukov <dvyukov at google.com> wrote:
> On Wed, Apr 23, 2014 at 10:48 PM, Bob Wilson <bob.wilson at
apple.com> wrote:
>> I can see that the behavior of our current instrumentation is going to
be a
>> problem for the kinds of applications that you’re looking at. If you
can
>> find a way to get the overhead down without losing accuracy
> 
> What are your requirements for accuracy?
> Current implementation does not provide 100% accuracy, so it's
> something less than 100%. 
Modulo any bugs, my understanding is that the current implementation
*does* provide 100% accuracy for single-threaded applications, as long
as the counters don't overflow.  Am I missing something?

On Apr 24, 2014, at 1:33 AM, Dmitry Vyukov <dvyukov at google.com>
wrote:
>> 
>> I can see that the behavior of our current instrumentation is going to
be a
>> problem for the kinds of applications that you’re looking at. If you
can
>> find a way to get the overhead down without losing accuracy
> 
> What are your requirements for accuracy?
> Current implementation does not provide 100% accuracy, so it's
> something less than 100%. What is it?
> What use cases for numeric counters (as opposed to bool flag) do we
> need to support? Is it only feedback-driven optimizations?
That’s a fair point. The current implementation is potentially inaccurate
because the counter increments are not thread-safe. In a low-contention
situation, that won’t matter much, but the counts could become quite inaccurate
if there are multiple threads running the same code at the same time.

I don’t have a specific goal in mind right now for accuracy. We plan to use this
instrumentation for both PGO and code coverage. Some coverage users only care
about a boolean check but others want to see the actual execution counts. If we
display the count values and they are significantly different from the real
execution counts, that will lead to much confusion.
> 
> 
>> and without
>> hurting the performance for applications without significant
contention,
> 
> What is the acceptable threshold? 0.01% would be fine, right? What is
> the maximum value that you are ready to agree with?
Like I said, I don’t have a specific value in mind. My sense is that most of the
applications we care about are quite different from the massively parallel code
that Google cares about. We may have many threads but they’re all doing
different things and contention is much less likely to be a problem.

I really think we need to see specifics before we can decide anything here.
> 
> 
>> then we can just adopt that. If you can’t do it without tradeoffs, then
we
>> should have a separate option to let those who care switch between
different
>> kinds of instrumentation.
>> 
>> Using the PC to map to the source code is simply not going to work with
>> -fprofile-instr-generate. The mapping from counter values to the
>> user-visible execution counts is complex and relies on detailed
knowledge of
>> the clang ASTs.
>> 
>> 
>> 
>> % clang++ -O2 -lpthread coverage_mt_vec.cc && time ./a.out
>> TIME: real: 0.219; user: 0.430; system: 0.000
>> % clang++ -O2 -lpthread -fprofile-instr-generate coverage_mt_vec.cc
&& time
>> ./a.out
>> TIME: real: 3.743; user: 7.280; system: 0.000
>> 
>> % cat coverage_mt_vec.cc
>> #include <pthread.h>
>> #include <vector>
>> 
>> std::vector<int> v(1000);
>> 
>> __attribute__((noinline)) void foo() { v[0] = 42; }
>> 
>> void *Thread1(void *) {
>>  for (int i = 0; i < 100000000; i++)
>>    foo();
>>  return 0;
>> }
>> 
>> __attribute__((noinline)) void bar() { v[999] = 66; }
>> 
>> void *Thread2(void *) {
>>  for (int i = 0; i < 100000000; i++)
>>    bar();
>>  return 0;
>> }
>> 
>> int main() {
>>  static const int kNumThreads = 16;
>>  pthread_t t[kNumThreads];
>>  pthread_create(&t[0], 0, Thread1, 0);
>>  pthread_create(&t[1], 0, Thread2, 0);
>>  pthread_join(t[0], 0);
>>  pthread_join(t[1], 0);
>>  return 0;
>> }
>> 
>> 
>> 
>> 
>> On Fri, Apr 18, 2014 at 11:45 PM, Xinliang David Li <xinliangli at
gmail.com>
>> wrote:
>>> 
>>> 
>>> 
>>> 
>>> On Fri, Apr 18, 2014 at 12:13 AM, Dmitry Vyukov <dvyukov at
google.com>
>>> wrote:
>>>> 
>>>> Hi,
>>>> 
>>>> This is long thread, so I will combine several comments into
single
>>>> email.
>>>> 
>>>> 
>>>>>> - 8-bit per-thread counters, dumping into central
counters on
>>>>>> overflow.
>>>>> The overflow will happen very quickly with 8bit counter.
>>>> 
>>>> Yes, but it reduces contention by 256x (a thread must execute
at least
>>>> 256 loop iterations between increments). In practice, if you
reduce
>>>> contention below some threshold, it does not represent a
problem anymore.
>>>> 
>>>> 
>>>> 
>>>>>> - per-thread counters. Solves the problem at huge cost
in RAM
>>>>>> per-thread
>>>>> It is not practical. Especially for TLS counters -- it
creates huge
>>>>> pressure on stack memory.
>>>> 
>>>> Do we have any numbers about number of counters? If there are
100K 1-byte
>>>> counters, I would consider it as practical.
>>>> 
>>> 
>>> A medium sized app I looked at has about 10M counters (arcs only). 
It is
>>> also not uncommon to see such apps running with hundreds of
threads.
>>> 
>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>>> In Google GCC, we implemented another technique which
proves to be very
>>>>> effective -- it is called FDO sampling.
>>>>> Basically counters will be updated every N samples.
>>>> 
>>>> How does it work?
>>> 
>>> 
>>> Similar to how occurrences based PMU sampling work. Setting
sampling
>>> period to 100 can reduce the instrumentation overhead by close to
100x
>>> without introducing much precision loss.
>>> 
>>>> 
>>>> 
>>>> 
>>>> 
>>>>>> It seems to me like we’re going to have a hard time
getting good
>>>>>> multithreaded performance without significant impact on
the single-threaded
>>>>>> behavior.
>>>>> I don't really agree.
>>>> 
>>>> 
>>>>> We are talking about developers here. Nobody would know the
exact thread
>>>>> counts, but developers know the ballpark number
>>>> 
>>>> I strongly believe that we must relief developers from this
choice during
>>>> build time, and do our best to auto-tune (if the final scheme
requires
>>>> tuning).
>>> 
>>> 
>>> 
>>> That really depends.   If the tuning space is small, it won't
be a problem
>>> for the developer/builder.
>>> 
>>> 
>>>> 
>>>> First, such questions puts unnecessary burden on developers. We
don't ask
>>>> what register allocation algorithm to use for each function,
right?
>>> 
>>> 
>>> Crazy developers can actually do that via internal options, but
this is
>>> totally different case.  People just needs one flag to turn on/off
sharding.
>>> When sharding is on, compiler can pick the best 'N'
according to some
>>> heuristics at compile time.
>>> 
>>> 
>>>> 
>>>> Second, there are significant chances we will get a wrong
answer, because
>>>> e.g. developer's view of how threaded is the app can differ
from reality or
>>>> from our classification.
>>>> Third, the app can be build by a build engineer; or the
procedure can be
>>>> applied to a base with 10'000 apps; or threaded-ness can
change; or the app
>>>> can work in different modes; or the app can have different
phases.
>>>> 
>>> 
>>> We have forgotten to mention the benefit of implementation
simplicity.  If
>>> the static/simple solution solves the problem for most of the use
cases,
>>> designing fancy dynamic solution sounds like over-engineering to
me.  It
>>> (overhead reduction technique) may also get in the way of easier
functional
>>> enhancement in the future.
>>> 
>>> David
>>> 
>>> 
>>> _______________________________________________
>>> LLVM Developers mailing list
>>> LLVMdev at cs.uiuc.edu         http://llvm.cs.uiuc.edu
>>> http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev
>>> 
>> 
>> _______________________________________________
>> LLVM Developers mailing list
>> LLVMdev at cs.uiuc.edu         http://llvm.cs.uiuc.edu
>> http://lists.cs.uiuc.edu/mailman/listinfo/llvmdev
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