[llvm-dev] RFC: Sanitizer-based Heap Profiler

[Teresa Johnson via llvm-dev]

Hi Andrey,

I was actually just typing up a reply welcoming contributions and to
suggest you give the existing profile support a try - I realized I need to
add documentation for the usage to llvm/clang's docs which I will do soon
but it sounds like you figured it out ok.

Some answers below.

On Thu, Jul 8, 2021 at 8:03 AM Andrey Bokhanko <andreybokhanko at gmail.com>
wrote:

> Hi Teresa,
>
> One more thing, if you don't mind.
>
> On Tue, Jul 6, 2021 at 12:54 AM Teresa Johnson <tejohnson at google.com>
> wrote:
>
>> We initially plan to use the profile information to provide guidance to
>> the dynamic allocation runtime on data allocation and placement. We'll send
>> more details on that when it is fleshed out too.
>>
>
> I played with the current implementation, and became a bit concerned if
> the current data profile is sufficient for an efficient data allocation
> optimization.
>
> First, there is no information on temporal locality -- only total_lifetime
> of an allocation block is recorded, not start / end times -- let alone
> timestamps of actual memory accesses. I wonder what criteria would be used
> by data profile-based allocation runtime to allocate two blocks from the
> same memory chunk?
>

It would be difficult to add all of this information for every allocation
and particularly every access without being prohibitively expensive. Right
now we have the ave/min/max lifetime, and just a single boolean per context
indicating whether there was a lifetime overlap with the prior allocation
for that context. We can probably expand this a bit to have somewhat richer
aggregate information, but like I said, recording and emitting all
start/end times and timestamps will be an overwhelming amount of
information. As I mentioned in my other response, initially the goal is to
provide hints about hotness and lifetime length (short vs long) to the
memory allocator so that it can make smarter decisions about how and where
to allocate data.


>
> Second, according to the data from [Savage'20], memory accesses affinity
> (= space distance between temporarily close memory accesses from two
> different allocated blocks) is crucial: figure #12 demonstrates that this
> is vital for omnetpp benchmark from SPEC CPU 2017.
>

Right now we don't track this information. Part of the issue is that memory
accesses themselves don't interact with the profile runtime library, but
rather the code is instrumented to update shadow counters inline - this
keeps the overhead reasonable. My understanding from reading the HALO paper
and asking the authors at CGO is that the overheads are currently quite
large (both the PIN-based runtime, and also the offline grouping
algorithm), and it didn't support multithreaded applications yet.

Definitely interested in contributions or ideas on how we could collect
richer information with the approach we're taking (allocations tracked by
the runtime per context and fast shadow memory based updates for accesses).


>
> Said this, my concerns are based essentially on a single paper that
> employs specific algorithms to guide memory allocation and measures their
> impact on a specific set of benchmarks. I wonder if you have preliminary
> data that validates sufficiency of the implemented data profile for
> efficient optimization of heap memory allocations?
>

I don't have anything I can share yet but we will do so in the future. For
an idea of how lifetime based allocation would work, here's a related paper
which used ML to identify context-sensitive lifetimes and used the info in
a custom allocator:

https://research.google/pubs/pub49008/
Maas, Martin & Andersen, David & Isard, Michael & Javanmard, Mohammad Mahdi
& McKinley, Kathryn & Raffel, Colin. (2020). Learning-based Memory
Allocation for C++ Server Workloads. Proceedings of the 25th ACM
International Conference on Architectural Support for Programming Languages
and Operating Systems (ASPLOS). 541-556. 10.1145/3373376.3378525.

Teresa


> References:
> [Savage'20] Savage, J., & Jones, T. M. (2020). HALO: Post-Link Heap-Layout
> Optimisation. CGO 2020: Proceedings of the 18th ACM/IEEE International
> Symposium on Code Generation and Optimization,
> https://doi.org/10.1145/3368826.3377914
>
> Yours,
> Andrey
>
>

-- 
Teresa Johnson |  Software Engineer |  tejohnson at google.com |
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