Benchmark Python Snippets Online

Perfpy allows you to benchmark python snippets online and share them with other people. You can measure the performance and memory usage of code on different python versions in this sandbox environment.

The tool can compare and test the speed of 3rd-party libraries or library versions, so you can find out which is the fastest or consumes less memory between numpy, opencv, scipy and pandas for a certain function.

Packages

Setup

Runs before the benchmark, and it is not measured. Good place to import libraries and prepare data. Learn more

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import random
import asyncio
FUTURE_NO = 10000
BATCH_SIZE = 100 # 100 seems okay with crude testing
MAX_RAND = 3
async def _async_data():
    return random.randint(0, MAX_RAND)
def _data():
    return random.randint(0, MAX_RAND)
    
 
 
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Code 1

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asyncio.new_event_loop().run_until_complete(gather())
 
 
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Code 2

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asyncio.new_event_loop().run_until_complete(async_sequential())
 
 
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Code 3

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asyncio.new_event_loop().run_until_complete(sync_sequential())
 
 
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Code 4

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asyncio.new_event_loop().run_until_complete(gather_batch())
 
 
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Teardown

Results

Fastest

sequential

17.3 ms

57.9 op/s

Speed

Benchmark	Runtime	Operations	VS	Visual
sequential	17.3 ms	57.9 op/s	1×	0.017263421167929966
await sequential	18.6 ms	53.7 op/s	1.08×	0.018638089299201965
gather batch(100)	228 ms	4.39 op/s	13×	0.2280491590499878
gather	230 ms	4.34 op/s	13×	0.2304191216826439

Memory

Benchmark	Peak Memory	Score	VS	Visual
sequential	< 1 KB	9.76	1×	0
await sequential	< 1 KB	9.73	1×	0
gather batch(100)	4.06 MB	5.03	4060×	4060
gather	10.5 MB	4.61	10544×	10544

Output

gather

No output

await sequential

No output

sequential

Error: Exec command 3d2f6a51076fd04337f40408d7c26af06efa6b65a67301b2b40c3882e240fa56 is already running

gather batch(100)

No output