8000 Extend async tree benchmarks to cover eager task execution (#279) · python/pyperformance@3615f18 · GitHub
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Extend async tree benchmarks to cover eager task execution (#279)
Co-authored-by: Kumar Aditya <59607654+kumaraditya303@users.noreply.github.com>
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‎doc/benchmarks.rst

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@@ -66,12 +66,15 @@ Async workload benchmark, which calls ``asyncio.gather()`` on a tree (6 levels d
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* ``async_tree``: no actual async work at any leaf node.
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* ``async_tree_io``: all leaf nodes simulate async IO workload (async sleep 50ms).
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* ``async_tree_memoization``: all leaf nodes simulate async IO workload with 90% of
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* ``async_tree_memoization``: all leaf nodes simulate async IO workload with 90% of
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the data memoized.
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* ``async_tree_cpu_io_mixed``: half of the leaf nodes simulate CPU-bound workload
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(``math.factorial(500)``) and the other half simulate the same workload as the
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(``math.factorial(500)``) and the other half simulate the same workload as the
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``async_tree_memoization`` variant.
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These benchmarks also have an "eager" flavor that uses asyncio eager task factory,
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if available.
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chameleon
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---------

‎pyperformance/data-files/benchmarks/MANIFEST

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@@ -7,6 +7,10 @@ async_tree <local>
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async_tree_cpu_io_mixed <local:async_tree>
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async_tree_io <local:async_tree>
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async_tree_memoization <local:async_tree>
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async_tree_eager <local:async_tree>
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async_tree_eager_cpu_io_mixed <local:async_tree>
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async_tree_eager_io <local:async_tree>
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async_tree_eager_memoization <local:async_tree>
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asyncio_tcp <local>
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asyncio_tcp_ssl <local:asyncio_tcp>
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concurrent_imap <local>

‎pyperformance/data-files/benchmarks/bm_async_tree/bm_async_tree_cpu_io_mixed.toml

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[tool.pyperformance]
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name = "async_tree_cpu_io_mixed"
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extra_opts = ["cpu_io_mixed"]
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‎pyperformance/data-files/benchmarks/bm_async_tree/bm_async_tree_eager.toml

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[tool.pyperformance]
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name = "async_tree_eager"
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extra_opts = ["eager"]

‎pyperformance/data-files/benchmarks/bm_async_tree/bm_async_tree_eager_cpu_io_mixed.toml

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[tool.pyperformance]
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name = "async_tree_eager_cpu_io_mixed"
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extra_opts = ["eager_cpu_io_mixed"]

‎pyperformance/data-files/benchmarks/bm_async_tree/bm_async_tree_eager_io.toml

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[tool.pyperformance]
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name = "async_tree_eager_io"
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extra_opts = ["eager_io"]

‎pyperformance/data-files/benchmarks/bm_async_tree/bm_async_tree_eager_memoization.toml

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[tool.pyperformance]
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name = "async_tree_eager_memoization"
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extra_opts = ["eager_memoization"]

‎pyperformance/data-files/benchmarks/bm_async_tree/bm_async_tree_io.toml

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[tool.pyperformance]
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name = "async_tree_io"
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extra_opts = ["io"]
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‎pyperformance/data-files/benchmarks/bm_async_tree/bm_async_tree_memoization.toml

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[tool.pyperformance]
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name = "async_tree_memoization"
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extra_opts = ["memoization"]
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‎pyperformance/data-files/benchmarks/bm_async_tree/run_benchmark.py

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1) "none": No actual async work in the async tree.
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2) "io": All leaf nodes simulate async IO workload (async sleep 50ms).
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3) "memoization": All leaf nodes simulate async IO workload with 90% of
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3) "memoization": All leaf nodes simulate async IO workload with 90% of
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the data memoized
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4) "cpu_io_mixed": Half of the leaf nodes simulate CPU-bound workload and
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the other half simulate the same workload as the
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4) "cpu_io_mixed": Half of the leaf nodes simulate CPU-bound workload and
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the other half simulate the same workload as the
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"memoization" variant.
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All variants also have an "eager" flavor that uses
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the asyncio eager task factory (if available).
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"""
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await self.recurse(NUM_RECURSE_LEVELS)
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class EagerMixin:
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async def run(self):
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loop = asyncio.get_running_loop()
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if hasattr(asyncio, 'eager_task_factory'):
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loop.set_task_factory(asyncio.eager_task_factory)
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return await super().run()
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class NoneAsyncTree(AsyncTree):
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async def workload_func(self):
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return
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class EagerAsyncTree(EagerMixin, NoneAsyncTree):
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pass
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class IOAsyncTree(AsyncTree):
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async def workload_func(self):
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await self.mock_io_call()
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class EagerIOAsyncTree(EagerMixin, IOAsyncTree):
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pass
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class MemoizationAsyncTree(AsyncTree):
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async def workload_func(self):
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# deterministic random, seed set in AsyncTree.__init__()
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return data
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class EagerMemoizationAsyncTree(EagerMixin, MemoizationAsyncTree):
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pass
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class CpuIoMixedAsyncTree(MemoizationAsyncTree):
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async def workload_func(self):
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# deterministic random, seed set in AsyncTree.__init__()
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return await MemoizationAsyncTree.workload_func(self)
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class EagerCpuIoMixedAsyncTree(EagerMixin, CpuIoMixedAsyncTree):
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pass
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def add_metadata(runner):
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runner.metadata["description"] = "Async tree workloads."
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runner.metadata["async_tree_recurse_levels"] = NUM_RECURSE_LEVELS
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Determines which benchmark to run. Options:
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1) "none": No actual async work in the async tree.
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2) "io": All leaf nodes simulate async IO workload (async sleep 50ms).
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3) "memoization": All leaf nodes simulate async IO workload with 90% of
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3) "memoization": All leaf nodes simulate async IO workload with 90% of
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the data memoized
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4) "cpu_io_mixed": Half of the leaf nodes simulate CPU-bound workload and
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the other half simulate the same workload as the
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4) "cpu_io_mixed": Half of the leaf nodes simulate CPU-bound workload and
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the other half simulate the same workload as the
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"memoization" variant.
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""",
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)
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BENCHMARKS = {
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"none": NoneAsyncTree,
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"eager": EagerAsyncTree,
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"io": IOAsyncTree,
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"eager_io": EagerIOAsyncTree,
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"memoization": MemoizationAsyncTree,
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"eager_memoization": EagerMemoizationAsyncTree,
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"cpu_io_mixed": CpuIoMixedAsyncTree,
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"eager_cpu_io_mixed": EagerCpuIoMixedAsyncTree,
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}
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async_tree_class = BENCHMARKS[benchmark]
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async_tree = async_tree_class()
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runner.bench_async_func(f"async_tree_{benchmark}", async_tree.run)
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