[llvm-dev] [RFC] Propeller: A frame work for Post Link Optimizations

[Sriraman Tallam via llvm-dev]

We have been working on addressing most of the concerns raised and we
have updated the patches with the following changes.

1) Exception Handling

TLDR;  We now handle exceptions and can optimize exception heavy
benchmarks and clang with exceptions enabled.

We have updated Propeller patches to handle exceptions. In contrast to
the previous approach which grouped all exception-handling related
basic blocks into one section, we now group just the landing pads
within one section and create a separate section for every other basic
block (even those which potentially throw exceptions by calling other
functions). This allows us to reorder code for exception-heavy
programs with greater flexibility. Grouping all landing pad basic
blocks in the same section lets us satisfy the ABI requirement of “one
landing pad fragment per procedure fragment.” This could be further
relaxed to separately grouping together all the landing pads which are
used by every basic block section. We will leave this for future
updates.

With this change, we are able to speedup the SPEC benchmark xalancbmk
by 0.5% which we were unable to do so previously. (Without exception
handling support this benchmark regressed by 1%).

We also built clang with exceptions enabled (DLLVM_ENABLE_EH=ON) and
were able to get performance improvements similar to what we got
without exceptions.

2)  Minimizing the number of basic block sections

TLDR;  We have significantly cut down the number of basic blocks for
which sections are created.

Previously,  basic block sections were created for all the basic
blocks in a function that had samples.  We have modified the patches
to allow creation of sections for only those basic blocks that have
samples.  This greatly reduces the number of sections created. This
does not affect benchmark performance in any manner.  For clang,
making this change reduces the object size bloat from 100% (2x) to 50%
(1.5x). Clang is an extreme benchmark for object size bloats since the
perf samples touch 10% of all functions.  For larger datacenter
workload benchmarks we evaluated, the object size bloats are less than
5% with this change.

We are working on further reducing this so that the object file size
bloats can be kept as minimal as possible.  Currently, we create a
section for a basic block even if it received just one perf sample.
This is overkill and can be restricted to only basic blocks that were
really hot.

3) Interprocedural Basic Block Reordering

TLDR;  Interprocedural basic block reordering further improves
performance by 1% (clang benchmark) but is slower and we are working
on a better implementation.

Previously,  the function reordering done was intra-procedural and one
of the comments was that if this could be done inter-procedurally. We
have now added an option to do this inter-procedurally, it is not the
default.  While the inter-procedural reordering algorithm is slower by
10x (increasing from 3 seconds to 30 seconds for clang) because the
code layout search space is much larger, the performance of the final
benchmark improves by an additional 1%. We are working on improving
the implementation to make this much faster.

4) Propeller functionality in lld is separated out

After discussing with Rui, we have moved Propeller code into a
separate directory.  Propeller interfaces with lld through
well-defined API. This provides the advantage that Propeller code
itself and future changes can be reviewed independently.

5) Reusing optimized LLVM IR files to re-link

TLDR;  We are working on re-using native object files from previous
link and patches will be available soon

The final re-link with Propeller can directly start from the high
level optimized IR files.  We are working on a patch to support
options “--save-precodegen” which will automatically save optimized IR
files which will later be reused by Propeller to reduce link time,
Branch : https://github.com/google/llvm-propeller/tree/plo-codegen-backend.

We are also working on directly using native object files from the
previous link for those objects which did not accrue any profiling
samples.  Even for the clang benchmark, only 7% of the objects needed
to be re-generated with basic block sections and the cached native
object files can be used for the rest.   This approach works
particularly well with ThinLTO as the lto.cache can directly be used
to obtain the native object files for modules that are not affected.
We will send out patches for this soon.

6) Time Overheads of CFI and Debug Info

TLDR;  Experiments show varying  overheads with Debug Info depending
on the symbolizers.  We did not see any overheads with CFI.
Collapsing .debug_ranges in the linker solves the overheads associated
with this.

We tried to measure two things.  Overheads from accessing .eh_frame
and .debug_ranges.  With Basic block sections, a new CFI FDE is
created for every basic block that gets a new section and a new range
pair is added to  debug_ranges. We wrote a benchmark , using
libunwind, to dump the stack trace (no symbolization) and then do then
dump the stack trace running the symbolizer (symbolization) using a
Google Internal API.

The benchmark had ~12000 functions and basic block sections for all
basic blocks resulted in ~130000 more basic block sections  and
equivalent debug range entries, an order of magnitude more.

For a vanilla binary, on average over several iterations, it took
~70ns to dump the stack trace without symbolization and ~235000 ns
with symbolization.   We then enabled basic block sections across the
board and re-measured and found no measurable impact. Symbolization
seems to dominate the time.

We then measured overheads with addr2line and llvm-symbolizer.  We
just timed symbolizing a couple of addresses. With addr2line, the
binary with sections slows down by 5% to 10%.  With llvm-symbolizer,
the slowdown is much more, by upto 30% sometimes. Looking at the
symbolizer code, we found places where the symbols are scanned
linearly and also places where the .debug_ranges are not collapsed.
Looking at the different symbolizer implementations, we found that
some use binary search, some collapse debug ranges as and when they
are added and some just do a linear search.

The primary reason for this overhead is that the .debug_ranges are
much larger. We are looking at improving this by doing one of these:

a) Have the linker collapse the debug_ranges which are pretty much
contiguous as most of the basic blocks end up in the same function.
This is the easier solution and we are working on a patch. This should
completely solve the problem.
b) Alternately, modify the symbolizers to collapse the ranges as and
when they are added.

All our experiments were with full basic block sections.  When basic
block sections are turned on selectively, these problems should not
manifest even now.

7) New Relocation types to simplify linker relaxation of Basic Block
Section Jumps

We are working on adding two new relocation types: R_X86_64_PC32_JUMPX
and R_X86_64_PC8_JUMPX  to the x86-64 ps-ABI to make linker handing of
relaxation simplified. Specifically, these two relocation types will
allow the linker to easily identify the jump instructions that were
created for bb sections. The PC8 relocations will only have to be
checked for growing to 32 bits during relaxation.

Proposal here: https://groups.google.com/forum/?hl=en#!topic/x86-64-abi/-2AkYw1QJuI

Thanks,
Sri





On Mon, Oct 21, 2019 at 10:38 PM Sriraman Tallam <tmsriram at google.com> wrote:
>
> We are going to be at the llvm-dev meeting the next two days.   We will get back to you after that.
>
> Sri
>
> On Mon, Oct 21, 2019 at 10:07 PM Maksim Panchenko <maks at fb.com> wrote:
>>
>> Hi Sri,
>>
>>
>>
>> Thank you for replying to our feedback. 7 out 12 high-level concerns have been
>>
>> answered; 2 of them are fully addressed. The rest are being tracked at the
>>
>> following Google doc:
>>
>>
>>
>> https://docs.google.com/document/d/1jqjUZc8sEoNl6_lrVD6ZkITyFBFqhUOZ6ZaDm_XVbb8/
>>
>>
>>
>> To keep the discussion at a high level, I have to reference the LTO vs ThinLTO
>>
>> comparison since that appears to be the central theme in your response to the
>>
>> feedback.
>>
>>
>>
>> Unlike LTO, BOLT does not have to keep the entire program in memory.
>>
>> Furthermore, as we have previously mentioned, most of the passes are run in
>>
>> parallel, and the performance scales well with the number of CPUs.
>>
>>
>>
>> To demonstrate that running BOLT on a hot subset of functions is not just a
>>
>> speculation, we have prototyped a "Thin" version that optimizes Clang-7 in under
>>
>> 15 seconds using less than 4GB of memory. No modifications to the linker or
>>
>> compiler were required. And by the way, that appears to be faster than just the
>>
>> re-linking phase of the Propeller. Larger loads show similar improvements
>>
>> providing 2x-5x savings over the original processing specs.
>>
>>
>>
>> Let me reiterate that current BOLT requires large amounts of memory not because
>>
>> it's a fundamental limitation, unlike LTO. For us, system memory was never a
>>
>> constraint. The runtime of the application, not BOLT, was the primary goal
>>
>> during the development.
>>
>>
>>
>> ThinLTO design solves a real problem and dramatically improves compilation time
>>
>> even when building on a single node. ThinLTO results provide "end-to-end build
>>
>> time" comparison to LTO. I've asked you to show a similar comparison for
>>
>> Propeller vs. BOLT. I haven't seen the results, and I suspect the total overhead
>>
>> will exceed that of even the oldest non-parallel version of BOLT.
>>
>>
>>
>> One argument I've heard is that BOLT is not taking advantage of the distributed
>>
>> build system. That's correct. It does not have to since it does not require to
>>
>> rebuild the application. In "Thin" mode, the overhead is similar to a regular
>>
>> linker running with a linker script.
>>
>>
>>
>> You are right that we do not support debug fission packages. It is unimplemented
>>
>> for a simple reason: no one asked for it previously. And as we like to say in
>>
>> the open-source community: "patches are welcome."
>>
>>
>>
>> Maksim
>>
>>
>>
>> P.S. We have updated https://github.com/facebookincubator/BOLT with instructions on running BOLT with jemalloc or tcmalloc.
>>
>>
>>
>> On 10/18/19, 11:21 AM, "Sriraman Tallam" <tmsriram at google.com> wrote:
>>
>>
>>
>> Hello Maksim,
>>
>>
>>
>> On Fri, Oct 18, 2019 at 10:57 AM Maksim Panchenko <maks at fb.com> wrote:
>>
>> Cool. The new numbers look good. If you run BOLT with jemalloc library
>>
>> preloaded, you will likely get a runtime closer to 1 minute. We’ve noticed that
>>
>> compared to the default malloc, it improves the multithreaded
>>
>> performance and brings down memory usage significantly.
>>
>>
>>
>> Great, thanks for confirming!  Would you be willing to share specific numbers, how significant is the reduction in memory with jemalloc for clang?    We double-checked our numbers with the larger benchmarks and we can confirm they were *not built with labels*.  One of our large benchmarks, search1, is about 5 times the size of clang in terms of text size as reported by size command, and we are seeing a 70G memory overhead on this. Do you have  data on the memory consumption of BOLT with larger benchmarks with jemalloc.   We are trying to build Chrome with latest BOLT so that we can share the memory overheads and the binaries with you for transparency but we are struggling with the disassembly errors. If you have data on large benchmarks we would appreciate it if you could share it.
>>
>>
>>
>> Further, if you have a recipe to use jemalloc with BOLT, please point it at us. We could try it out too and share our findings.
>>
>>
>>
>> Thanks much,
>>
>> Sri
>>
>>
>>
>>
>>
>> Thanks,
>>
>> Maksim
>>
>>
>>
>> On 10/17/19, 2:59 PM, "Sriraman Tallam" <tmsriram at google.com> wrote:
>>
>>
>>
>>
>>
>> On Wed, Oct 16, 2019 at 3:52 PM Maksim Panchenko <maks at fb.com> wrote:
>>
>> Hi Sri,
>>
>>
>>
>> I want to clarify one thing before sending a detailed reply: did you evaluate
>>
>> BOLT on Clang built with basic block sections?
>>
>> In the makefile you reference,
>>
>> there are two versions: a “vanilla” and a default built with function sections.
>>
>> High overheads you see with BOLT on Clang do not match our experience.
>>
>>
>>
>> Thanks for pointing that out in the Makefile. We double-checked and noticed a bug in our Makefile.  For clang, we noticed that we are BOLTING with basic block symbols which seems to affect the memory consumption of BOLT.  So, we  have re-measured with recent bolt and for *full transparency* we have uploaded the binaries,  BOLT's yaml files and perf.data files  and the commands so that you can independently verify our results and check the binaries.  We have gzipped all the files to keep it under 2G limit for git lfs, everything is here :   https://github.com/google/llvm-propeller/tree/plo-dev/clang-bolt-experiment  We have run our experiments on a 192G machine with Intel 18 core.
>>
>>
>>
>> We built llvm-bolt with most recent sources and is *pristine* with none of our patches and uploading the binary we used here, https://github.com/google/llvm-propeller/blob/plo-dev/clang-bolt-experiment/llvm-bolt  That's a very recent BOLT binary, git hash: 988a7e8819b882fd14e18d149f8d3f702b134680
>>
>>
>>
>> The  https://github.com/google/llvm-propeller/tree/plo-dev/clang-bolt-experiment/{v1,v2} contains two sets of binaries.  The first binary is pristine recent clang-10 built from 2 weeks ago with additionally only -Wl,-q.  v2 is another clang binary also only additionally built with -q.  There are no labels or sections or any other Propeller flags used to build these clang binaries.  Here is the command we are using to optimize with BOLT, all the commands have been checked in too.
>>
>>
>>
>> You should be able to run llvm-bolt now on these binaries as all the files are provided.  We have also provided the raw perf data files in case you want to independently convert.
>>
>>
>>
>> $ /usr/bin/time -v /llvm-bolt clang-10 -o pgo_relocs-bolt-compiler -b pgo_relocs-compiler.yaml -split-functions=3 -reorder-blocks=cache+ -reorder-functions=hfsort -relocs=1 --update-debug-sections
>>
>>
>>
>> For version 2, this is the number:
>>
>>
>>
>> Elapsed (wall clock) time (h:mm:ss or m:ss): 2:05.40
>>
>> Maximum resident set size (kbytes): 18742688
>>
>>
>>
>> That is 125 seconds and ~18G of RAM.
>>
>>
>>
>> For version 1, this hangs and we stopped it after several minutes and the maximum RSS size crossing 50G.  This is most likely just a bug and you should be able to reproduce this.  The binary seems to be running and printing update messages.
>>
>>
>>
>> We also measured without update-debug-sections too with the command :
>>
>>
>>
>> $ /usr/bin/time -v /llvm-bolt clang-10 -o pgo_relocs-bolt-compiler -b pgo_relocs-compiler.yaml -split-functions=3 -reorder-blocks=cache+ -reorder-functions=hfsort -relocs=1
>>
>>
>>
>> For version1 :
>>
>> Elapsed (wall clock) time (h:mm:ss or m:ss): 1:33.74
>>
>> Maximum resident set size (kbytes): 14824444
>>
>>
>>
>> 93 seconds and ~14G of RAM
>>
>>
>>
>> version 2 :
>>
>> Elapsed (wall clock) time (h:mm:ss or m:ss): 1:21.90
>>
>> Maximum resident set size (kbytes): 14511912
>>
>>
>>
>> similar 91 secs and ~14G
>>
>>
>>
>> Now, coming back to the bug in the Makefile, we originally reported ~35G.  That is *wrong* since the clang binary used to measure bolt overheads was built with basic block labels.  Our  *sincere apologies* for this, this showed BOLT as consuming more memory than is actual for clang.  We double-checked BOLT numbers with the internal benchmark search2 for sanity and that is built *without any labels* and only with "-Wl,-q".  We are checking the other large internal benchmarks too.  We cannot disclose internal benchmarks. So, we will get more large open-source benchmarks like Chrome or gcc built with bolt and share the binaries and results so you can independently verify.
>>
>>
>>
>> Thanks
>>
>> Sri
>>
>>
>>
>>
>>
>> Thanks,
>>
>> Maksim
>>
>>
>>
>> On 10/14/19, 11:44 AM, "llvm-dev on behalf of Sriraman Tallam via llvm-dev" <llvm-dev-bounces at lists.llvm.org on behalf of llvm-dev at lists.llvm.org> wrote:
>>
>>
>>
>> Hello,
>>
>>
>>
>> I wanted to consolidate all the discussions and our final thoughts on the concerns raised.  I have attached a document consolidating it.
>>
>>
>>
>> BOLT’s performance gains inspired this work and we believe BOLT
>>
>> is a great piece of engineering.  However, there are build environments where
>> scalability is critical and memory limits per process are tight :
>>
>> * Debug Fission,  https://gcc.gnu.org/wiki/DebugFission was primarily
>> invented to achieve scalability and better incremental build times while
>> building large binaries with debug information.
>>
>> * ThinLTO,
>> http://blog.llvm.org/2016/06/thinlto-scalable-and-incremental-lto.html was
>> primarily invented to make LLVM’s full LTO scalable and keep the memory and
>> time overheads low.  ThinLTO has enabled much broader adoption of whole
>> program optimization, by making it non-monolithic.
>>
>> * For Chromium builds,
>> https://chromium-review.googlesource.com/c/chromium/src/+/695714/3/build/toolcha
>> in/concurrent_links.gni, the linker process memory is set to 10GB with ThinLTO.
>> It was 26GB with Full LTO before that and individual processes will run of out
>> of memory beyond that.
>>
>> * Here,
>> https://gotocon.com/dl/goto-chicago-2016/slides/AysyluGreenberg_BuildingADistrib
>> utedBuildSystemAtGoogleScale.pdf, a distributed build system at Google scale
>> is shown where 5 million binary and test builds are performed every day on
>> several thousands of machines, each  with a limitation of 12G of memory per
>> process and 15 minute time-out on tests. Memory overheads of 35G (clang) are
>> well above these thresholds.
>>
>> We have developed Propeller like ThinLTO that can be used to obtain similar
>> performance gains like BOLT in such environments.
>>
>>
>>
>> Thanks
>>
>> Sri
>>
>>
>>
>>
>>
>> On Fri, Oct 11, 2019 at 11:25 AM Xinliang David Li via llvm-dev <llvm-dev at lists.llvm.org> wrote:
>>
>>
>>
>>
>>
>> On Fri, Oct 11, 2019 at 10:46 AM James Y Knight via llvm-dev <llvm-dev at lists.llvm.org> wrote:
>>
>> Is there large value from deferring the block ordering to link time? That is, does the block layout algorithm need to consider global layout issues when deciding which blocks to put together and which to relegate to the far-away part of the code?
>>
>>
>>
>> Or, could the propellor-optimized compile step instead split each function into only 2 pieces -- one containing an "optimally-ordered" set of hot blocks from the function, and another containing the cold blocks? The linker would have less flexibility in placement, but maybe it doesn't actually need that flexibility?
>>
>>
>>
>> Apologies if this is obvious for those who actually know what they're talking about here. :)
>>
>>
>>
>> It is a fair question.
>>
>>
>>
>> We believe the flexibility to do fine grained layout in whole program context is important. PostLinkOptimization is aimed at getting as much performance improvement as possible (usually applied on top of ThinLTO+PGO), so the framework is designed to enable it.
>>
>>
>>
>> In particular, it allows the linker to stitch hot bb traces from different functions to be stitched together. It also allows hot trace duplication across procedure boundaries (kind of interprocedural tailDup). Besides, code alignment decisions to minimize branch mispredictions  may require global context (e.g, too conflicting branches residing in two different functions).  Other micro-arch specific optimizations to improve processor front-end throughput may also require global context.
>>
>>
>>
>> It is conceivable to have an option to control the level of granularity at the possible cost of performance.
>>
>>
>>
>> thanks,
>>
>>
>>
>> David
>>
>>
>>
>>
>>
>>
>>
>> On Wed, Oct 2, 2019 at 6:18 PM Rafael Auler <rafaelauler at fb.com> wrote:
>>
>> You’re correct, except that, in Propeller, CFI duplication happens for every basic block as it operates with the conservative assumption that a block can be put anywhere by the linker. That’s a significant bloat that is not cleaned up later. So, during link time, if N blocks from the same function are contiguous in the final layout, as it should happen most of the time for any sane BB order, we would have several FDEs for a region that only needs one. The bloat goes to the final binary (a lot more FDEs, specifically, one FDE per basic block).
>>
>> BOLT will only split a function in two parts, and only if it has profile. Most of the time, a function is not split. It also has an option not to split at all. For internally reordered basic blocks of a given function, it has CFI deduplication logic (it will interpret and build the CFI states for each block and rewrite the CFIs in a way that uses the minimum number of instructions to encode the states for each block).
>>
>>
>>
>> From: llvm-dev <llvm-dev-bounces at lists.llvm.org> on behalf of James Y Knight via llvm-dev <llvm-dev at lists.llvm.org>
>> Reply-To: James Y Knight <jyknight at google.com>
>> Date: Wednesday, October 2, 2019 at 1:59 PM
>> To: Maksim Panchenko <maks at fb.com>
>> Cc: "llvm-dev at lists.llvm.org" <llvm-dev at lists.llvm.org>
>> Subject: Re: [llvm-dev] [RFC] Propeller: A frame work for Post Link Optimizations
>>
>>
>>
>> I'm a bit confused by this subthread -- doesn't BOLT have the exact same CFI bloat issue? From my cursory reading of the propellor doc, the CFI duplication is _necessary_ to represent discontiguous functions, not anything particular to the way Propellor happens to generate those discontiguous functions.
>>
>>
>>
>> And emitting discontiguous functions is a fundamental goal of this, right?
>>
>>
>>
>> On Wed, Oct 2, 2019 at 4:25 PM Maksim Panchenko via llvm-dev <llvm-dev at lists.llvm.org> wrote:
>>
>> Thanks for clarifying. This means once you move to the next basic block (or any other basic
>>
>> block in the function) you have to execute an entirely new set of CFI instructions
>>
>> except for the common CIE part. While indeed this is not as bad, on average, the overall
>>
>> active memory footprint will increase.
>>
>>
>>
>> Creating one FDE per basic block means that .eh_frame_hdr, an allocatable section,
>>
>> will be bloated too. This will increase the FDE lookup time. I don’t see .eh_frame_hdr
>>
>> being mentioned in the proposal.
>>
>>
>>
>> Maksim
>>
>>
>>
>> On 10/2/19, 12:20 PM, "Krzysztof Pszeniczny" <kpszeniczny at google.com> wrote:
>>
>>
>>
>>
>>
>>
>>
>> On Wed, Oct 2, 2019 at 8:41 PM Maksim Panchenko via llvm-dev <llvm-dev at lists.llvm.org> wrote:
>>
>> *Pessimization/overhead for stack unwinding used by system-wide profilers and
>> for exception handling*
>>
>> Larger CFI programs put an extra burden on unwinding at runtime as more CFI
>> (and thus native) instructions have to be executed. This will cause more
>> overhead for any profiler that records stack traces, and, as you correctly note
>> in the proposal, for any program that heavily uses exceptions.
>>
>>
>>
>> The number of CFI instructions that have to be executed when unwinding any given stack stays the same. The CFI instructions for a function have to be duplicated in every basic block section, but when performing unwinding only one such a set is executed -- the copy for the current basic block. However, this copy contains precisely the same CFI instructions as the ones that would have to be executed if there were no basic block sections.
>>
>>
>>
>> --
>>
>> Krzysztof Pszeniczny
>>
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