Consider removing --check-bounds=no?

[Keno]

I think we should consider removing the --check-bounds=no option.
I can't really think of any situation in which it would be safe or sensible to turn it on.
If you really must have something like it, a Cassette pass that disables bounds checking
in a particular region of code, could achieve any residual benefits without being as massive a footgun.
Worse, turning on --check-bounds=no on current master can actually result in significantly
worse performance because it removes inference's ability to do concrete evaluation (constant folding).
Note that that we can make the option a noop in a minor release since throwing a BoundsError
is allowable undefined behavior.

EDIT: note for future readers that the recommended replacement is to mark @inbounds code that is discovered to benefit from this flag from @profile analysis.

[matthias314]

Here is a case where I regularly use --check-bounds=no: I write a program that does some computation, say based on some integer parameter n. The larger n is, the longer the program runs. I want to get the result for as large a value of n as possible. I run the program for small n with bounds checking turned on to make sure everything works. Then I turn bounds checking off to push n as high as possible. That's quick and easy and does the trick for me.
(This is of course for code that I use myself, not for packages released to the public.)

[vtjnash]

Just to be clear, the reason we want to remove it is it requires us to compile the code more conservatively, leading to significant losses in inference accuracy, leading to significant losses of performance when running with check-bounds=no

[JeffBezanson]

@matthias314 Out of curiosity, what kind of speedup do you get?

[gbaraldi]

Triage discussed this in length and the conclusion was that issues that are present in --check-bounds=no are similarly present with @inbounds meaning that this is probably a bandaid.
One idea @oscardssmith proposed but wasn't sure of the feasibility is to refine the inbounds effect to not taint if we can prove that the acesses are always inbounds.

[vtjnash]

That sounds easy to prove: just remove --check-bounds=no and the required property is proved exactly when expected, and more often than it could be proven under --check-bounds=no in the current system (e.g. we can eliminate taint on bounds more easily with check-bounds=yes/auto than with check-bounds=no. That is the statement of purpose for this issue.)

[matthias314]

@JeffBezanson The speedup varies; often it is indeed not impressive.

My point was not so much about the effectiveness of the current implementation of --check-bounds=no, but about the general idea: Instead of sprinkling my code with @inbounds, @boundscheck and @propagate_inbounds, I find it easier not to worry about it when I write my code and then turn all checks off once I think my code is correct.

[LilithHafner]

leading to significant losses of performance when running with check-bounds=no

Could we get a specific example of @inbounds and/or check-bounds=no decreasing performance? I feel like there should be an open issue for this but I'm having trouble finding either a gh issue or an example of this behavior.

[oscardssmith]

julia> Base.@assume_effects :terminates_locally function f(s)
           t = 0.
           for i in 1:2^20
               for m in 1:length(s)
                   t += 1/(i + s[m])
               end
           end
           t
       end
f (generic function with 1 method)

julia> Base.@assume_effects :terminates_locally function f_inbounds(s)
           t = 0.
           for i in 1:2^20
               for m in 1:length(s)
                   @inbounds t += 1/(i + s[m])
               end
           end
           t
       end
f_inbounds (generic function with 1 method)

julia> g() = f((2,3,4))
g (generic function with 1 method)

julia> g_inbounds() = f_inbounds((2,3,4))
g_inbounds (generic function with 1 method)

julia> @btime g()
  0.861 ns (0 allocations: 0 bytes)
37.903821175206865

julia> @btime g_inbounds()
  2.683 ms (0 allocations: 0 bytes)
37.903821175206865

So a nice example where @inbounds a 3 million times regression.

[LilithHafner]

Thanks! I figured out the reason I couldn't reproduce this is that I wasn't using the latest master.

[maleadt]

Apparently there's GPU users that care about this, because GPUs are very sensitive to the branch-heavy code introduced by bounds checks (typically because it increases register pressure which hurts occupancy) and they are using unoptimized kernels that don't have the necessary @inbounds annotations. At the same time, --check-bounds=no is now (on 1.9) unusable to the because the regression in const-prop breaks static compilation, so that's not great.

@Keno Can you elaborate on the Cassette-like solution? What's a reasonable way to implement this for GPUCompiler's abstract interpreter without the const-prop regressions?

[omlins]

The feature check-bounds=no is one of the top beautiful things of Julia in my opinion: it allows to develop code with the benefit of bounds checking, and once everything works as it should, with a single switch we can remove the bounds checking not necessary anymore to have it run faster. I think there is a large community, in particular domain scientists (who in the end are the probably largest part of the target users) that will strongly appreciate if this feature can be conserved. :)

[vtjnash]

The biggest security flaw in C come from the fact it does not have bounds checking enabled in releases.

[omlins]

Bounds checking can naturally drastically impact performance as we can see running the following example The performance achieved without bounds checking is here over three times higher than with bounds checking activated (executed on a P100 GPU):

omlins@nid02356:~/tmpwdir/ParallelStencil/examples> julia -O3 --check-bounds=no --math-mode=fast diffusion2D_shmem_novis.jl
time_s=1.1297500133514404 t_it=0.012552777926127115 T_eff=513.2291021090084
Benchmarktools (min): t_it=0.012316079 T_eff=523.0926940302998

omlins@nid02356:~/tmpwdir/ParallelStencil/examples> julia -O3 --math-mode=fast diffusion2D_shmem_novis.jl
time_s=3.5698001384735107 t_it=0.03966444598303901 T_eff=162.42382275438484
Benchmarktools (min): t_it=0.039542728 T_eff=162.9237857337511

[KristofferC]

I think the argument is to use @inbounds where it matters which prevents making your whole application vulnerable to index mistakes with --check-bounds=false.

[omlins]

Bounds checking can naturally drastically impact performance as we can see running the following example The performance achieved without bounds checking is here over three times higher than with bounds checking activated (executed on a P100 GPU):

omlins@nid02356:~/tmpwdir/ParallelStencil/examples> julia -O3 --check-bounds=no --math-mode=fast diffusion2D_shmem_novis.jl
time_s=1.1297500133514404 t_it=0.012552777926127115 T_eff=513.2291021090084
Benchmarktools (min): t_it=0.012316079 T_eff=523.0926940302998

omlins@nid02356:~/tmpwdir/ParallelStencil/examples> julia -O3 --math-mode=fast diffusion2D_shmem_novis.jl
time_s=3.5698001384735107 t_it=0.03966444598303901 T_eff=162.42382275438484
Benchmarktools (min): t_it=0.039542728 T_eff=162.9237857337511

This example shows that bounds checking drastically impacts performance for high performance (GPU) applications. As a result, when running scientific high performance code, we do not want a single bounds check to happen . In the same time, while developing a scientific high performance code, we would like bounds check everywhere.
Thus, we do not want to add @inbounds statements into our code in order to be able to develop conveniently with bounds checking; at run time however, we need to deactivate the bounds checking everywhere: the global switch --check-bounds=no which has been available until now` has been a perfect solution.

[maleadt]

when running scientific high performance code, we do not want to have a single bounds check to happen

The point is that your entire session shouldn't be running under --check-bounds=no, or a single mistake can kill your it. You apparently want GPU code to run without bounds checks (even though it would still be better to properly optimize your kernels using appropriate @inbounds annotations), so the option should be narrowed to GPU code generation, which as I mentioned already is something that could be added to e.g. @cuda calls, or as an env var to GPUCompiler.jl.

[omlins]

The point is that your entire session shouldn't be running under --check-bounds=no, or a single mistake can kill your it.

At the moment we run the code without bounds checks (meaning for a production run), there will be no errors almost 100% of the time.

so the option should be narrowed to GPU code generation

That could be a good solution indeed, if bounds checking would not impact performance of high performance cpu code. However, running the same code on cpu we can already see that this is not true (problem size: 1024^2):

omlins@nid02061:~/tmpwdir/ParallelStencil/examples> julia --threads=12 -O3 --check-bounds=no --math-mode=fast diffusion2D_shmem_novis.jl
time_s=0.032401084899902344 t_it=0.0003600120544433594 T_eff=69.90272600430198
Benchmarktools (min): t_it=0.000321402 T_eff=78.30014747885825

omlins@nid02061:~/tmpwdir/ParallelStencil/examples> julia --threads=12 -O3 --math-mode=fast diffusion2D_shmem_novis.jl
time_s=0.044291019439697266 t_it=0.0004921224382188586 T_eff=51.137322839988364
Benchmarktools (min): t_it=0.000453692 T_eff=55.46896132177777

[Seelengrab]

Any indiscriminate use of --check-bounds=no is just as dangerous as just throwing @inbounds on a loop "to make things go fast".

Any use of @inbounds should always be accompanied by a matching @boundscheck before the use, as well as a @propagate_inbounds to allow the removal of that @boundscheck. If any of those pieces are missing, you're going to run into either correctness issues or suboptimal performance. The argument that @inbounds and @boundscheck should not be necessary because the compiler should be able to figure it out is in principle good, but falls apart due to the fact that those annotations are precisely used when the compiler can't figure them out on its own (if we had such a perfect compiler, we'd do all computation at compile time!). Thus, what @inbounds communicates from the developer to the compiler is that you have a mathematical proof that all indexing operations in the block are valid (with the caveat that the code is too concise to communicate that proof to the compiler).

Personally, I'd prefer enforcing the use of @boundscheck when using @inbounds, but since that makes currently working code error, it could be a deprecation at best.

In regards to GPU code (or any code, really) - it seems much more safe to me to use @inbounds and @boundscheck and set --check-bounds=yes during development, than use --check-bounds=no in production. The former will still catch accidental out-of-bounds accesses in dependencies in production instead of introducing correctness (or even security) issues, while the latter will not.

---

In case it's not clear - I'm in favour of removing --check-bounds=no, while also making the compiler better at interacting with explicit @inbounds and (possibly in a future release, if at all) requiring the use of @boundscheck paired with @inbounds. To bring another point of contention over from triage - the observed behavior of constant folded vs. "interpreted" julia code should not differ at all.

[sloede]

Being able to do development with memory safety and then easily switch to faster performance using a simple command line option is literally one of the best and most used features for me as an HPC user. When talking to colleagues from the scientific computing community, maybe the single strongest argument favor of Julia is that it takes away nothing of your performance while making it it much easier to work and prototype with.

Another major points I bring up when advocating Julia is that it makes great strides in solving the two-language problem. By default, everything is memory safe and still reasonably fast. But when necessary, it's as simple as flipping a single switch and you get performance on par with Fortran/C++, and that's for real production code, not just academic setups.
IMHO, this argument would take a serious blow since in the future it means you will have the two languages inside Julia:
The regular, inexperienced-user-friendly and non-optimized code, and the super high performance code with @inbounds and @boundscheck etc. at all the right places. Maybe there will be an array type someone comes up with that will automatically allow switching to @inbounds? And hello numjl, your friendly in-Julia DSL modelled after numpy...

In addition, you now have to annotate (or better yet: first test, then annotate, since no premature optimization) each loop that might be remotely performance critical. I did a quick check; in our code base for Trixi.jl, we have around 2000 for loops. Given that some of them are not performance relevant and some of them are nested, we still need to check ~1000 places and verify if and how they could benefit from manual optimizations. Compare that with the ease of just writing --check-bounds=no and that's it. Yes, it is not a big issue to manually optimize everything for small to medium sized packages, but compare that to production codes with tens to hundreds of thousands of lines.

Finally, here are some numbers. For a production run with Trixi.jl, I see a strong influence of --check-bounds=no on the performance (5 hottest kernels; all numbers per iteration):

	--check-bounds=no	Regular	Impact
calc_volume_integral!	121ms	122ms	0%
calc_interface_flux!	66.9ms	120ms	+79%
calc_surface_integral!	23.7ms	46.1ms	+95%
prolong2interfaces!	16.4ms	33.9ms	+100%
apply_jacobian!	6.66ms	7.78ms	+17%
...	...	...
Overall	240ms	336ms	+40%

Thus while the effect of disabling bounds checking varies, there is a significant impact on many core algorithms.

---

TL;DR At the current state, I think removing --check-bounds=no is not the best idea. It would cause many HPC codes written in Julia over the past years to immediately cease to be competitive with C++/Fortran codes, unless hand-optimizing all loops again, taking away one of the major benefits (and selling points) Julia currently has. Finally, at least in our case --check-bounds=no has a significant impact on performance (30% reduction in overall runtime) and is thus always used where performance is critical, e.g., production runs on a large number of processors.

[KristofferC]

Any indiscriminate use of --check-bounds=no is just as dangerous as just throwing @inbounds on a loop "to make things go fast".

Clearly not, since an @inbounds is restricted to the loop you apply it to while the --check-bounds=no is applied to the whole process.

[vchuravy]

I am sympathetic to the performance argument, but the actual issue here is that --check-bounds=no turns a correct program into an incorrect program.

Local annotations have the benefit that they only require local knowledge to reason about them, whereas global flags require global knowledge (in my opinion impossible to obtain).
As an example we could add a mode to Julia that turns all exceptions off (a generalized check-bounds=no). One sometimes comes across some code that uses exception for control-flow (a style that is frowned upon, but nonetheless legal), we even had code like that in Base... Which would mean that turning exceptions of "globally" would have lead to dead-locking Julia programs.

Now out-of-bounds exceptions are hopefully not used like that, but --check-bounds=no can turn correct programs into incorrect programs.

In Julia we have exposed local options to control unsafe behaviours (like fast-math or opting out of bounds-checking) and as others have pointed out @inbounds applies to blocks, for-loops and even functions and is not to onerous to use.

[sloede]

@vchuravy I see your point about fine-grained control and its benefits. On the other hand, you lose a big part of what makes Julia currently very flexible. Now the same code can be memory safe or fast, without the user having to make a conscious decision in each (potentially) performance critical section. Why would you give up this awesome feature and, one of the strongest selling points of Julia in a world where you compete with established, fast code languages like C++/Fortran or slow, rapid prototyping langues like Python? I feel like this would instead lead Julia more towards Python, where you can be just as fast as Julia if you restrict yourself to loops that are amenable to @njit/Numba (with @inbounds everywhere, it will even look similar 😬).

And again, adding @inbounds whereever it makes sense from a performance perspective is not a true solution to our misgivings (as I believe was pointed out by someone else above). It is exactly that we do need this ability to have bounds checking enabled by default (for rapid prototyping), with the option to quickly switch to "production mode".

If the main argument in favor is that users abuse the --check-bounds=no option and wreaking havoc on the Julia support channels, I would classify this as an educational (or people-related) issue and not a technical issue. Therefore, imho the correct solution should be found in the instructional realm and not by applying a code patch. Otherwise, I strongly believe that the alternative will be a new package that exports a macro such as @dynamic_inbounds, with the ability to switch between the actual @inbounds and a no-op based on, e.g., Preferences.jl, just to get the old behavior back. This seems bound to cause a lot of new issues, i.e., just kicking the can of user confusion down the road 🤷

[vchuravy]

Why would you give up this awesome feature and, one of the strongest selling points of Julia in a world where you compete with established, fast code languages like C++/Fortran or slow, rapid prototyping langues like Python?

For me --check-bounds=no has never been a selling point of Julia and I have never taught it in my performance engineering workshops. In contrast the local control a programmer has is for me a major advantage of Julia in contrast to C/C++ especially w.r.t fast-math. Over the years we have improved the compiler and we are now at a point where @inbounds is needed less and less, but I would expect a HPC application to use it, instead of relying on a global compiler/runtime flag.

The issue is that --check-bounds=no turns correct code into incorrect code. A second issue for me is the social one where the reliance of --check-bounds=no for performance, leads to a worse experience by default for users of your packages and of Julia. Julia is now "slow by default" and you have to tell your users to run Trixi.jl based applications with --check-bounds=no for best performance, instead of using the local mechanism and have a good user experience by default.

I could see a use for development where I would like to answer the question "would @inbounds help", but the current usage scenario you describe leads to a worse experience for everyone. User don't get fast code by default, and if they use --check-bounds=no they can't be sure that an "untested code-path" doesn't lead to a numerical corruption down the road and their climate simulation is wrong.

[williamfgc]

My two cents is to document the recommended way for developers to refactor existing code and to write code onwards no matter what the outcome is for the recommended change. Mostly to be sure I'm not giving advice on deprecated functionality as we provide tutorials for the HPC folks new to Julia. Thanks!

[Seelengrab]

Clearly not, since an @inbounds is restricted to the loop you apply it to while the --check-bounds=no is applied to the whole process.

A single erroneous @inbounds can have just as disastrous consequences as whole program --check-bounds=no, the latter just creates vastly more opportunities for mistakes to become (in-)visible. Since all that is needed for an incorrect result is a single misuse, both are equally dangerous (just think of the OffsetArrays kerfuffle, spawned by incorrect use of @inbounds..).

Another major points I bring up when advocating Julia is that it makes great strides in solving the two-language problem. By default, everything is memory safe and still reasonably fast. But when necessary, it's as simple as flipping a single switch and you get performance on par with Fortran/C++, and that's for real production code, not just academic setups.

Now the same code can be memory safe or fast, without the user having to make a conscious decision in each (potentially) performance critical section.

In my experience, well written/idiomatic julia code is as fast (or sometimes beats) equivalent C/C++/Fortran code while retaining the safety features a high level language provides. Turning those safety features off and saying "look, without those safety features we are just as fast!" is, in my opinion, misrepresenting the strength of julia, of being able to be strictly better than the "old guard", further perpetuating the myth that the only way to go fast is to not have safe programs. This is not an either/or thing - we can, should (and ultimately MUST) have both at the same time.

Therefore, imho the correct solution should be found in the instructional realm and not by applying a code patch.

The correct way to deal with a tool that you can do nothing but cut yourself with if not held in exactly the right way (which doesn't exist here, as this is a global flag and I'm pretty sure you're not auditing all your dependencies for correct behavior) is not to tell people to only hold it in the exactly right way, it is to fix the tool so you can't cut yourself in the first place.

[PallHaraldsson]

Could a compromise be made that you could disable --check-bounds=no at a module level (or alternatively modules disallow that by default, and you could opt into an exception, something you do until you know you've added enough @inbounds)? There is plenty of code that doesn't need to be fast (and/or isn't well tested), and such a module could still opt into @inbounds locally.

[williamfgc]

A possibility to disable the removal of bounds checking for modules where it is not needed would certainly be well appreciated.

Any larger-than-local-array scope would certainly be appreciated. Hope that's what comes out of this discussion. There are scientific schemas with thousands of array variables (any atmospheric or mildly large experimental dataset, or even a multiphysics particle or mesh based simulation) adding @inbounds in every index access operation becomes a tough sell for Julia at these scales.

NASA used rigorous engineering practices to write code that is systemically safe, instead of burning compute power to rely on software testing for correctness of their Apollo Lander.

Tests at small scales actually saves a tremendous amount of compute power before launching at scale, CI is good preventive/predictive maintenance not available at that time. But I'd rather not get out of scope.

[omlins]

Any larger-than-local-array scope would certainly be appreciated. Hope that's what comes out of this discussion.

To conclude the input from my side: people that currently rely on the feature --check-bounds=no are certainly open to (and see an benefit in) using somewhat less coarse grained mechanisms for the deactivation of bounds checking as long as they don't impede to write high performance code that is close to math notation. However, while such new coarse grain mechanism are not available, I would propose to keep the optional flag --check-bounds=no.

...and at that point I would like to say: thanks to all the people working on the julia compiler and the core language for their awesome work!

[Seelengrab]

This means then that there is, a priori, no problem with the implementation of --check-bounds=no and that the @inbounds implementation would need to be improved ( if possible?) to fix this constant folding and evaluation issue.

No - this means that both are bad and if you have to use it, choose @inbounds over --check-bounds=no. Fixing the issue is vastly more likely to only happen in the context of @inbounds than globally (if it were possible to solve it globally, I'm quite certain Rust would not need unsafe).

A possibility to disable the removal of bounds checking for modules where it is not needed would certainly be well appreciated.

As pointed out above, this is not trivial, at all, due to bounds checking removal requiring inlining in the first place, which is very likely to cross module boundaries. This also comes with the problem of just removing bounds checking not necessarily meaning that the compiler can vectorize effectively, which is what's often needed/what people want to achieve with that in the first place (see e.g. eschnett/SIMD.jl#102).

adding @inbounds in every index access operation becomes a tough sell for Julia at these scales.

That is not what people in this thread are asking and would indeed be too cumbersome to use - instead, use @boundscheck at the start of your function for your bounds checking needs in your kernel/code, then have a big @inbounds begin block or @inbounds for or similar. It's also usually not necessary to use @inbounds in every indexing expression, since the compiler is in fact able to figure the inboundsness out in quite a lot of common patterns & cases (e.g. broadcasting). You can see an example of this pattern e.g. here in the Code dropdown.

[williamfgc]

That is not what people in this thread are asking and would indeed be too cumbersome to use - instead, use...

Updating the documentation here with this info (and deprecation) would be appreciated, in particular for those new to Julia coming from Fortran, Matlab, Numpy, etc. and helps us in trying to convince people to adopt Julia. Thanks everyone for the great work and discussion.

Edit: my motivation is that we are preparing tutorials for HPC crowds so we want to point them in the right direction moving forward @vchuravy

[timholy]

@williamfgc not sure if you're talking about the documentation for existing Julia versions or you're just asking about making sure the documentation gets updated if there are changes, but assuming the former: it's easy to improve the docs yourself (for simple changes you can just edit in your browser), and often it's better if the person who sees the gap is the one who tries to close it. (To me the docs look just fine, I wouldn't know what to change, but since I already know how it works I'm sure I'm not seeing them from the right perspective.)

[williamfgc]

@timholy more like bringing awareness that docs would become soon outdated if this flag is removed (it's already broken) and block-style @inbounds begin...end (edit: and other helpful situations) are not mentioned. I am more thinking about those new to Julia reading the doc for the first time and want to code moving past v1.8 as this is a breaking change. Happy to contribute and thanks for pointing this out.

[chriselrod]

Thus, we do not want to add @inbounds statements into our code in order to be able to develop conveniently with bounds checking; at run time however, we need to deactivate the bounds checking everywhere: the global switch --check-bounds=no which has been available until now` has been a perfect solution.

What about the opposite: develop using --check-bounds=yes, and then run with auto?

Discussion above mostly focused on this being less pretty.

[vtjnash]

That is recommended

[Keno]

See RFC for one possible approach in #50239