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48aa7a5162
Since callgrind allows to control stats collection from the guest, this allows us to reset the collection right before the benchmark starts. This change exposes this to the benchmark runner and integrates callgrind data parsing into bench.py, so that we can run bench.py with --callgrind argument and, as long as the runner was built with callgrind support, we get instruction counts from the run. We convert instruction counts to seconds using 10G instructions/second rate; there's no correct way to do this without simulating the full CPU pipeline but it results in time units on a similar scale to real runs.
60 lines
1.5 KiB
Lua
60 lines
1.5 KiB
Lua
-- This file is part of the Luau programming language and is licensed under MIT License; see LICENSE.txt for details
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local bench = {}
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bench.runs = 20
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bench.extraRuns = 4
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function bench.runCode(f, description)
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-- Under Callgrind, run the test only once and measure just the execution cost
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if callgrind and callgrind("running") then
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if collectgarbage then collectgarbage() end
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callgrind("zero")
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f() -- unfortunately we can't easily separate setup cost from runtime cost in f unless it calls callgrind()
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callgrind("dump", description)
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return
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end
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local timeTable = {}
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for i = 1,bench.runs + bench.extraRuns do
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-- try to run GC if it's available
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if collectgarbage then
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pcall(function()
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collectgarbage()
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end)
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end
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local ts0 = os.clock()
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local result = f()
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local ts1 = os.clock()
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-- If test case doesn't return a duration (if only a part of code is measured) we will measure full execution time here
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if not result then
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result = ts1 - ts0
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end
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table.insert(timeTable, result)
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end
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table.sort(timeTable)
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for i = 1,bench.extraRuns do
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table.remove(timeTable, #timeTable)
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end
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-- Output test name followed by each result
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local report = "|><|"..description
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for _,v in ipairs(timeTable) do
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report = report .. "|><|" .. (v * 1000)
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end
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report = report .. "||_||"
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print(report)
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end
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return bench
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