Scott Wolchok swolchok

swolchok / untrusted-lvl13-lazy-solution.js

Created July 24, 2015 16:16

	/*
	* robotMaze.js
	*
	* The blue key is inside a labyrinth, and extracting
	* it will not be easy.
	*
	* It's a good thing that you're a AI expert, or
	* we would have to leave empty-handed.
	*/

swolchok / prof100k.txt

Created October 28, 2016 05:27

	Flat profile:

	Each sample counts as 0.01 seconds.
	% cumulative self self total
	time seconds seconds calls s/call s/call name
	72.28 21.82 21.82 47800000 0.00 0.00 camlDefault_Tar__Layout__layoutNodeImpl_1039
	10.67 25.04 3.22 13800000 0.00 0.00 camlDefault_Tar__Layout__layoutNode_1161
	6.64 27.05 2.01 40900001 0.00 0.00 caml_obj_dup
	4.27 28.34 1.29 61600000 0.00 0.00 camlDefault_Tar__Layout__layoutNodeInternal_1038
	2.48 29.09 0.75 100000 0.00 0.00 camlDefault_Tar__LayoutTestFixedEncoding__fun_3905

swolchok / baseline.txt

Created March 8, 2023 18:45

baseline output for nested_annotation_str benchmark

	Nesting level: 1
	output: Dict[str, Tuple[str, str]]
	<torch.utils.benchmark.utils.common.Measurement object at 0x7f7bcb854f40>
	x.annotation_str
	Median: 19.34 us
	IQR: 0.81 us (19.02 to 19.83)
	101 measurements, 100 runs per measurement, 1 thread
	Nesting level: 3
	output: Dict[str, Tuple[Dict[str, Tuple[Dict[str, Tuple[str, str]], Dict[str,
	<torch.utils.benchmark.utils.common.Measurement object at 0x7f7bcb854e80>

swolchok / tupleopt.txt

Created March 8, 2023 19:13

output for annotation_str benchmark with https://github.com/pytorch/pytorch/pull/96215

	Nesting level: 1
	output: Dict[str, Tuple[str, str]]
	<torch.utils.benchmark.utils.common.Measurement object at 0x7f6969bb1f40>
	x.annotation_str
	Median: 18.42 us
	IQR: 1.04 us (17.86 to 18.90)
	108 measurements, 100 runs per measurement, 1 thread
	Nesting level: 3
	output: Dict[str, Tuple[Dict[str, Tuple[Dict[str, Tuple[str, str]], Dict[str,
	<torch.utils.benchmark.utils.common.Measurement object at 0x7f6969bb1e80>

swolchok / gist:8bd4ca61e156827f23bb54ca3b48d54f

Created March 8, 2023 20:18

benchmark output for dict annotation_str optimization

	Nesting level: 1
	output: Dict[str, Tuple[str, str]]
	<torch.utils.benchmark.utils.common.Measurement object at 0x7f8a3a31ff40>
	x.annotation_str
	Median: 20.85 us
	IQR: 0.67 us (20.55 to 21.22)
	95 measurements, 100 runs per measurement, 1 thread
	Nesting level: 3
	output: Dict[str, Tuple[Dict[str, Tuple[Dict[str, Tuple[str, str]], Dict[str,
	<torch.utils.benchmark.utils.common.Measurement object at 0x7f8a3a31fe80>