<html><head><meta http-equiv="Content-Type" content="text/html charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;" class=""><br class=""><div><blockquote type="cite" class=""><div class="">On Aug 19, 2015, at 3:39 PM, Rong Xu via llvm-dev <<a href="mailto:llvm-dev@lists.llvm.org" class="">llvm-dev@lists.llvm.org</a>> wrote:</div><br class="Apple-interchange-newline"><div class=""><div dir="ltr" class=""><div class="">We collected more data to address some of the questions from the reviewers. Note this time we use clang itself as the benchmark. We choose clang because we think it's a typical C++ program and the reviewers here have good knowledge of the code base.</div><div class=""><br class=""></div><div class="">What we measure is running time for clang to compile a large preprocessed source file (4.98M lines of .ii file), using different compilation modes. All the numbers reported here are the average running time of 5 runs in seconds.</div><div class=""><br class=""></div><div class=""><b class="">(1) Performance b/w late instrumentation v.s. not instrumenting single BB functions</b></div><div class=""><br class=""></div><div class="">We first compare various instrumentation performance.</div><div class=""><font face="monospace, monospace" class="">----------------------------------------------------------------------------</font></div><div class=""><font face="monospace, monospace" class=""> Config wall_time_for_instr ratio_vs_base profile_size</font></div><div class=""><font face="monospace, monospace" class="">(1) base O2 80.386 100.0% --</font></div><div class=""><font face="monospace, monospace" class="">(2) FE-based Instr 201.658 250.8% 65238880</font></div><div class=""><font face="monospace, monospace" class="">(3) late Instr 103.662 129.0% 14860144</font></div><div class=""><font face="monospace, monospace" class="">(4) (3) + w/o pre-inline 199.924 248.7% 70762720</font></div><div class=""><font face="monospace, monospace" class="">(5) (4) + Silva 119.904 149.2% 24499528</font></div><div class=""><br class=""></div><div class="">Config(5) used the simple heuristic that Sean Silva proposed: not instrumenting single BB functions that contain less than 10 instructions (excluding debug and phi stmts).</div><div class=""><br class=""></div><div class="">We can see:</div><div class="">1) Simple heuristic of not instrumenting small single BB functions improves instrumentation performance as expected.</div><div class="">2) Using simple heuristic is still slower than late instrumentation with pre-inlining: the later is 15% faster.</div><div class="">3) Late instrumentation produces the smallest profile size: it's 39% smaller than using the simple heuristic.</div><div class=""><br class=""></div><div class="">The result is expected as pre-inlining can handle more cases than the simple heuristic. There is significant performance gap between the simple heuristic (5) and late instrumentation (2).</div><div class=""><br class=""></div><div class="">We also used a few larger internal benchmarks to further validate the above result. The following table shows the slowdown compared to the base O2. The labels (2) to (5) refer to the same config as in the previous table.</div><div class=""><font face="monospace, monospace" class="">------------------------------------------------------</font></div><div class=""><font face="monospace, monospace" class="">Program (2) (3) (4) (5)</font></div><div class=""><font face="monospace, monospace" class="">C++benchmark16 -45.24% -12.93% -43.84% -24.74%</font></div><div class=""><font face="monospace, monospace" class="">C++benchmark17 -90.86% -58.19% -87.77% -80.62%</font></div><div class=""><font face="monospace, monospace" class="">C++benchmark18 -95.32% -54.75% -91.21% -82.56%</font></div><div class=""><br class=""></div><div class=""><br class=""></div><div class="">We can see the same trend as the clang benchmark: the simple heuristic (5) recovers a lot of performance loss compared with FE base instrumentation, but is still significantly worse than late instrumentation (3).</div><div class=""><br class=""></div><div class=""><b class="">(2) Performance impact of context sensitivity</b></div><div class=""><br class=""></div><div class="">LLVM does not use the profile information fully in the back-end optimizations, for instance, inlining does not fully use the profile counts -- it only marks hot/cold function attribute based on function entry counts. To evaluate the impact of profile context sensitivity, GCC is used in the experiment. Note that GCC PGO improves clang performance a lot more than clang PGO.</div><div class=""><br class=""></div><div class="">First we summarize the methodology used in the experiment:</div><div class="">0) build clang with GCC O2 without early inlining and measure clang's performance. GCC early inlining (einline) is similar to pre-inline used by late instrumentation.<br class=""></div><div class="">1) build clang with GCC O2 with early inlining and measure performance.</div><div class=""><br class=""></div><div class="">The performance difference of 1) and 0) is denoted as E which measures the contribution of early inlining.</div><div class=""><br class=""></div><div class="">2) build clang with GCC O2 + PGO without early inlining.</div><div class="">3) build clang with GCC O2 + PGO with early inlining.</div><div class=""><br class=""></div><div class="">The performance difference of 3) and 2) is denoted as EC. It constitutes roughly two parts a) early inlining contribution b) context sensitive profiling enabled with early inlining.</div><div class=""><br class=""></div><div class="">The contribution of context sensitive profiling can be estimated by EC - E above.</div><div class=""><font face="monospace, monospace" class="">-------------------------------------------------------------------------------</font></div><div class=""><font face="monospace, monospace" class="">Config wall_time_for_use speedup_vs_(0) speedup_vs_(1)</font></div><div class=""><font face="monospace, monospace" class="">(0) base w/o einline 84.946 1.000 0.934</font></div><div class=""><font face="monospace, monospace" class="">(1) base O2 79.310 1.071 1.000</font></div><div class=""><font face="monospace, monospace" class="">(2) profile-arcs w/o einline 63.518 1.337 1.249</font></div><div class=""><font face="monospace, monospace" class="">(3) profile-arcs 48.364 1.756 1.640</font></div></div></div></blockquote><div><br class=""></div>Sorry for digging up an old thread.</div><div><br class=""></div><div>The data looks interesting and promising. How did you measure clang’s performance? On which benchmarks?</div><div>Do you have similar performance data on Clang PGO + late instrumentation that you can share?</div><div><br class=""></div><div>Thanks,</div><div>Manman</div><div><br class=""></div><div><br class=""><blockquote type="cite" class=""><div class=""><div dir="ltr" class=""><div class=""><br class=""></div><div class="">We see the following:</div><div class="">1) GCC PGO with early inlining improves clang performance by 64.0% (v.s. base O2 w/ early inline).</div><div class="">2) GCC PGO w/o early inlining improves clang performance by 33.7% (v.s. base O2 w/o early inline).</div><div class="">3) Early inlining performance contribution is about 7.1%.</div><div class="">4) Profile context sensitivity contribution is estimated to be 22.2% (i.e. 64.0% -33.7% - 7.1%), which is pretty significant.</div><div class=""><br class=""></div><div class=""><b class="">(3) Pre-inline pass impact on the value profiling</b></div><div class=""><br class=""></div><div class="">Again, we use GCC as the platform to estimate:</div><div class=""><br class=""></div><div class=""><font face="monospace, monospace" class="">--------------------------------------------------------</font></div><div class=""><font face="monospace, monospace" class=""> Config wall_time for_instr</font></div><div class=""><font face="monospace, monospace" class="">(2) profile-arcs 115.720</font></div><div class=""><font face="monospace, monospace" class="">(3) profile-arcs w/o einline 310.560</font></div><div class=""><font face="monospace, monospace" class="">(4) profile-generate 139.952</font></div><div class=""><font face="monospace, monospace" class="">(5) profile-generate w/o einline 680.910</font></div><div class=""><br class=""></div><div class="">In GCC, -fprofile-generate does -fprofile-arcs as well as the value profiling. The above table shows that with value profile, the impact of pre-inlining is even larger for instrumented binary performance. Without value profiling, disabling pre-inlining increases runtime by 1.7x, while with value profiling, its impact is 3.9x increase in runtime.</div><div class=""><br class=""></div></div><div class="gmail_extra"><br class=""><div class="gmail_quote">On Tue, Aug 11, 2015 at 10:11 PM, Sean Silva via llvm-dev <span dir="ltr" class=""><<a href="mailto:llvm-dev@lists.llvm.org" target="_blank" class="">llvm-dev@lists.llvm.org</a>></span> wrote:<br class=""><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr" class=""><br class=""><div class="gmail_extra"><br class=""><div class="gmail_quote"><span class="">On Tue, Aug 11, 2015 at 11:07 AM, Diego Novillo via llvm-dev <span dir="ltr" class=""><<a href="mailto:llvm-dev@lists.llvm.org" target="_blank" class="">llvm-dev@lists.llvm.org</a>></span> wrote:<br class=""><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr" class=""><div class="gmail_extra">One aspect of this that I have not seen discussed is that middle-end instrumentation enables PGO optimizations to front-ends other than Clang.</div><div class="gmail_extra"><br class=""></div><div class="gmail_extra">While I agree that FE instrumentation could be improved, it still requires every FE to implement essentially the same common functionality. Having PGO instrumentation generated in the middle-end, allows us every FE to automatically take advantage of PGO.</div></div></blockquote><div class=""><br class=""></div></span><div class="">This is a really good point, and I agree with it. We may have gotten off on the wrong foot since Rong's email focused so heavily on comparing with the frontend instrumentation. As far as I see it, Rong's proposal has a couple different parts:</div><div class=""><br class=""></div><div class="">1. Infrastructure for IR-level instrumentation-based PGO</div><div class="">2. Changes to the pass pipeline so that a hypothetical IR-level instrumentation-based PGO is more effective</div><div class="">3. MST algorithm with profile feedback for optimal placement of counter updates.</div><div class=""><br class=""></div><div class="">I think 1. is a no-brainer, if only so that all LLVM clients can benefit from PGO, and also (as you pointed out below) so that it can have an exclusive focus on performance. If it is sufficiently flexible, it may even make sense to restrict clang's frontend instrumentation-based profiling to non-performance stuff, and have clang directly interoperate with the IR-level PGO for performance-related PGO use cases, just like any other frontend would.</div><div class=""><br class=""></div><div class="">Philip and Sanjoy, out of curiosity do you guys use your own instrumentation placement for PGO? Is an IR-level PGO infrastructure upstream something you guys would be interested in?</div><div class=""><br class=""></div><div class="">I think that 2. is something that once we have 1. we will be able to evaluate better, but for now my opinion is that we should be able to make good progress without digging into that.</div><div class=""><br class=""></div><div class="">I think that 3. is a no-brainer if it provides a really significant win, but without 1. we can't really measure its effect in isolation. It also has a usability problem since it requires feeding in an existing profile for the *instrumented* build, but if the benefit is very significant this may be worth it for some users. We will probably be able to easily refactor 1. as needed into an MST approach that degrades gracefully to using static heuristics in the absence of real profile information, so is not a maintenance burden (maybe even helps by providing a good framework in which to develop effective static heuristics).</div><div class=""><br class=""></div><div class="">For the time being, I think we can avoid discussion of 2. and 3. until we have more of 1. working. So I think it would be most productive if we focus this discussion on 1.</div><span class=""><div class=""><br class=""></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr" class=""><div class="gmail_extra"><br class=""></div><div class="gmail_extra">Additionally, some of the overhead imposed by FE instrumentation is not really all that easy to get rid of. You end up duplicating functionality that is more naturally implemented in the middle end.</div></div></blockquote><div class=""><br class=""></div></span><div class="">Yeah, I was looking into a couple of other simple approaches and quickly found out that I was basically replicating much of the sort of logic that the inliner already has.</div><div class=""><br class=""></div><div class="">-- Sean Silva</div><div class=""> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><span class=""><div dir="ltr" class=""><div class="gmail_extra"><br class=""></div><div class="gmail_extra">I see the two approaches as supplementary, rather than complementary. One does not negate the other. Some of the optimizations we'd do in the FE, may hurt coverage. Instead, by instrumenting in the middle end, you can focus exclusively on performance (coverage be damned).</div><div class="gmail_extra"><br class=""></div><div class="gmail_extra"><br class=""></div><div class="gmail_extra">Diego.</div></div>
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