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6ab034b828
Reland #120514 after 2f6e3df08a8b7cd29273980e47310cf09c6fdbd8 fixed iteration order issue and libstdc++/libc++ differences. --- Both options instruct the linker to optimize section layout with the following goals: * `--bp-compression-sort=[data|function|both]`: Improve Lempel-Ziv compression by grouping similar sections together, resulting in a smaller compressed app size. * `--bp-startup-sort=function --irpgo-profile=<file>`: Utilize a temporal profile file to reduce page faults during program startup. The linker determines the section order by considering three groups: * Function sections ordered according to the temporal profile (`--irpgo-profile=`), prioritizing early-accessed and frequently accessed functions. * Function sections. Sections containing similar functions are placed together, maximizing compression opportunities. * Data sections. Similar data sections are placed together. Within each group, the sections are ordered using the Balanced Partitioning algorithm. The linker constructs a bipartite graph with two sets of vertices: sections and utility vertices. * For profile-guided function sections: + The number of utility vertices is determined by the symbol order within the profile file. + If `--bp-compression-sort-startup-functions` is specified, extra utility vertices are allocated to prioritize nearby function similarity. * For sections ordered for compression: Utility vertices are determined by analyzing k-mers of the section content and relocations. The call graph profile is disabled during this optimization. When `--symbol-ordering-file=` is specified, sections described in that file are placed earlier. Co-authored-by: Pengying Xu <xpy66swsry@gmail.com>
105 lines
1.9 KiB
ArmAsm
105 lines
1.9 KiB
ArmAsm
# REQUIRES: aarch64
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## Generate a large test case and check that the output is deterministic.
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# RUN: %python %s %t.s %t.proftext
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# RUN: llvm-mc -filetype=obj -triple=aarch64 %t.s -o %t.o
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# RUN: llvm-profdata merge %t.proftext -o %t.profdata
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# RUN: ld.lld --icf=all -o %t1.o %t.o --irpgo-profile=%t.profdata --bp-startup-sort=function --bp-compression-sort-startup-functions --bp-compression-sort=both
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# RUN: ld.lld --icf=all -o %t2.o %t.o --irpgo-profile=%t.profdata --bp-startup-sort=function --bp-compression-sort-startup-functions --bp-compression-sort=both
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# RUN: cmp %t1.o %t2.o
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import random
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import sys
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assembly_filepath = sys.argv[1]
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proftext_filepath = sys.argv[2]
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random.seed(1234)
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num_functions = 1000
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num_data = 100
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num_traces = 10
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function_names = [f"f{n}" for n in range(num_functions)]
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data_names = [f"d{n}" for n in range(num_data)]
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profiled_functions = function_names[: int(num_functions / 2)]
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function_contents = [
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f"""
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{name}:
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add w0, w0, #{i % 4096}
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add w1, w1, #{i % 10}
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add w2, w0, #{i % 20}
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adrp x3, {name}
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ret
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"""
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for i, name in enumerate(function_names)
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]
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data_contents = [
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f"""
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{name}:
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.ascii "s{i % 2}-{i % 3}-{i % 5}"
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.xword {name}
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"""
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for i, name in enumerate(data_names)
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]
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trace_contents = [
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f"""
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# Weight
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1
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{", ".join(random.sample(profiled_functions, len(profiled_functions)))}
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"""
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for i in range(num_traces)
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]
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profile_contents = [
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f"""
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{name}
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# Func Hash:
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{i}
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# Num Counters:
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1
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# Counter Values:
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1
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"""
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for i, name in enumerate(profiled_functions)
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]
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with open(assembly_filepath, "w") as f:
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f.write(
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f"""
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.text
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.globl _start
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_start:
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ret
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{"".join(function_contents)}
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.data
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{"".join(data_contents)}
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"""
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)
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with open(proftext_filepath, "w") as f:
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f.write(
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f"""
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:ir
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:temporal_prof_traces
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# Num Traces
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{num_traces}
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# Trace Stream Size:
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{num_traces}
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{"".join(trace_contents)}
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{"".join(profile_contents)}
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"""
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)
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