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Message-ID: <20240228005230.287113-1-namhyung@kernel.org>
Date: Tue, 27 Feb 2024 16:52:26 -0800
From: Namhyung Kim <namhyung@...nel.org>
To: Arnaldo Carvalho de Melo <acme@...nel.org>,
Ian Rogers <irogers@...gle.com>
Cc: Jiri Olsa <jolsa@...nel.org>,
Adrian Hunter <adrian.hunter@...el.com>,
Peter Zijlstra <peterz@...radead.org>,
Ingo Molnar <mingo@...nel.org>,
LKML <linux-kernel@...r.kernel.org>,
linux-perf-users@...r.kernel.org,
Andi Kleen <ak@...ux.intel.com>
Subject: [PATCH 0/4] perf annotate: Improve memory usage for symbol histogram
Hello,
This is another series of memory optimization in perf annotate.
When perf annotate (or perf report/top with TUI) processes samples, it
needs to save the sample period (overhead) at instruction level. For
now, it allocates an array to do that for the whole symbol when it
hits any new symbol. This comes with a lot of waste since samples can
be very few and instructions span to multiple bytes.
For example, when a sample hits symbol 'foo' that has size of 100 and
that's the only sample falls into the symbol. Then it needs to
allocate a symbol histogram (sym_hist) and the its size would be
16 (header) + 16 (sym_hist_entry) * 100 (symbol_size) = 1616
But actually it just needs 32 (header + sym_hist_entry) bytes. Things
get worse if the symbol size is bigger (and it doesn't have many
samples in different places). Also note that it needs separate
histogram for each event.
Let's split the sym_hist_entry and have it in a hash table so that it
can allocate only necessary entries.
No functional change intended.
Thanks,
Namhyung
Namhyung Kim (4):
perf annotate: Add a hashmap for symbol histogram
perf annotate: Calculate instruction overhead using hashmap
perf annotate: Remove sym_hist.addr[] array
perf annotate: Add comments in the data structures
tools/perf/ui/gtk/annotate.c | 14 ++++-
tools/perf/util/annotate.c | 114 ++++++++++++++++++++++-------------
tools/perf/util/annotate.h | 86 +++++++++++++++++++++++---
3 files changed, 158 insertions(+), 56 deletions(-)
--
2.44.0.rc1.240.g4c46232300-goog
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