Spinach vs spynach: noesy_strychnine (CPU)

run noesy_strychnine-numba, both sides present. Built by comparison/report.py; protocol in comparison/README.md.

Provenance

run date (UTC)	2026-10-26T13:23:31Z
spynach version	v0.2.0-21-g2e8843c-dirty
commit	2e8843cd088ebc17faf75fd8d06a6692f8507f71
local modifications	True
Python	3.12.5
NumPy	2.5.4
SciPy	1.18.1
Numba (compiled kernels)	0.68.0
host	jerschowlab25.chem.nyu.edu
platform	macOS-26.6.2-arm64-arm-64bit
cores	12
BLAS	{"numpy": "accelerate", "scipy": "Accelerate"}
thread settings	library defaults
GPU requested	False
GPU device
case options	{}
not run

stage	Spinach / s	spynach / s	spynach / Spinach
create	20.31	0.01	0.00x
basis	2.26	0.06	0.03x
liquid/noesy	88.66	68.32	0.77x
processing	0.12	0.04	0.35x
plot	1.86	0.11	0.06x
total (wall clock)	113.24	68.54	0.61x

variable	Spinach shape	spynach shape	relative error	max \|difference\|
fid_cos	(512, 512)	(512, 512)	2.187e-12	3.968e-16
fid_sin	(512, 512)	(512, 512)	2.267e-12	1.627e-16
spectrum	(2048, 2048)	(2048, 2048)	1.316e-12	1.268e-12

relative error = ||spynach - Spinach||_F / ||Spinach||_F, the metric of the oracle tests.

by self time

function	calls	self / s	total / s
ParforEngine>ParforEngine.getCompleteIntervals	492	79.10	80.67
ClusterClientSession>iWaitForAllWorkerServices	1	13.73	13.87
ParforEngine>ParforEngine.addInterval	1741	2.60	3.01
HTMLCanvasPlugin.HTMLCanvasPlugin>HTMLCanvasPlugin.createCanvas	1	1.05	1.10
hamiltonian	4	0.77	10.09
existentials	1	0.62	0.79
CJSKeyValueStore>iWriteDataFile	100	0.50	0.50
CJSKeyValueStore>iReadDataFile	25	0.45	0.45
DataQueue>DataQueue.dispatchContinuation	1483	0.36	0.47
redfield_integral_async	1	0.34	2.80
AbstractClusterPool.AbstractClusterPool>iPostLaunchSetup	1	0.34	0.43
cell2mat>concatenateCellContents	22	0.32	0.32
CJSKeyValueStore>CJSKeyValueStore.putSingleVariable	100	0.28	0.82
table.saveobj	1920	0.26	0.42
cheap_norm	718	0.25	0.26
evolution	12	0.25	61.98
report	2387	0.24	0.27
noesy_strychnine_spinach	1	0.22	113.21
clean_up	143	0.22	0.22
CJSKeyValueStore>CJSKeyValueStore.getSingleVariable	25	0.20	0.70
tabular.parenReference	244	0.20	0.39
step>(parfor body)	1160	0.19	0.23
autoexec	1	0.19	0.36
DataQueue>DataQueue.maybeDrainAndDispatchAllDataOnQueue	478	0.19	0.93
tabular.dotParenReference	159406	0.17	0.17

by total time (with callees)

function	calls	self / s	total / s
run	1	0.00	113.26
run_comparison	1	0.05	113.26
noesy_strychnine_spinach	1	0.22	113.21
liquid	1	0.02	88.65
parallel_function	47	0.04	85.57
parallel_function>distributed_execution	43	0.13	85.07
ParforEngine>ParforEngine.getCompleteIntervals	492	79.10	80.67
noesy	1	0.05	75.48
evolution	12	0.25	61.98
create	1	0.07	20.29
Cluster.parpool	1	0.00	17.53
doParpool	1	0.02	17.50
AbstractClusterPool.AbstractClusterPool>AbstractClusterPool.hBuildPool	1	0.03	17.33
AbstractClusterPool.AbstractClusterPool>iStartClient	1	0.00	16.46
AbstractInteractiveClient>AbstractInteractiveClient.start	1	0.03	16.45
ClusterClientSession>ClusterClientSession.waitForConnections	1	0.16	14.70
ClusterClientSession>iWaitForAllWorkerServices	1	13.73	13.87
step	47	0.09	13.22
relaxation	1	0.08	10.61
hamiltonian	4	0.77	10.09
ParforEngine>ParforEngine.addInterval	1741	2.60	3.01
redfield_integral_async	1	0.34	2.80
basis	1	0.05	2.25
unihash	1	0.00	2.02
plot_2d	1	0.06	1.74

by self time

function	calls	self / s	total / s
matmul (utilities.py:72)	16919	51.71	52.64
taylor_term (kernels.py:315)	756	6.77	6.78
<built-in method scipy.sparse._sparsetools.csr_matmat>	1285	1.35	1.35
matlab_round (kernels.py:63)	3654	1.04	1.04
<built-in method scipy.sparse._sparsetools.csr_matmat_maxnnz>	1285	0.85	0.85
csr_times_dense (kernels.py:297)	4211	0.82	0.83
<built-in method scipy.sparse._sparsetools.csr_sort_indices>	737	0.80	0.80
_reordered_taylor (propagation.py:126)	47	0.75	7.70
<built-in method numpy._core._multiarray_umath.count_nonzero>	3901	0.42	0.42
<lambda> (kernels.py:278)	1152	0.30	0.02
_detect (propagation.py:447)	4096	0.29	0.30
_superop_side_build (operators.py:290)	1490	0.29	0.63
<built-in method scipy.sparse._sparsetools.csr_plus_csr>	743	0.24	0.24
<built-in method scipy.sparse._sparsetools.csr_eliminate_zeros>	1171	0.20	0.20
step (propagation.py:78)	47	0.16	7.89
<built-in method numpy.array>	24704	0.16	0.16
_evolve_trajectory (propagation.py:426)	24	0.10	5.89
<method 'nonzero' of 'numpy.ndarray' objects>	36157	0.09	0.09
evolution (propagation.py:245)	12	0.09	57.22
<method 'reduce' of 'numpy.ufunc' objects>	80982	0.09	0.09
<method 'copy' of 'numpy.ndarray' objects>	11087	0.08	0.08
_round_numpy (kernels.py:70)	1218	0.07	1.12
<method 'searchsorted' of 'numpy.ndarray' objects>	19196	0.07	0.07
<built-in method scipy.sparse._sparsetools.coo_tocsr>	103	0.06	0.06
<built-in method scipy.sparse._sparsetools.csr_sum_duplicates>	738	0.06	0.06

by total time (with callees)

function	calls	self / s	total / s
run (noesy_strychnine_spynach.py:13)	1	0.00	68.54
simulate (noesy_strychnine.py:65)	1	0.00	68.43
liquid (contexts.py:32)	1	0.00	68.32
noesy (sequences.py:96)	1	0.00	65.08
evolution (propagation.py:245)	12	0.09	57.22
matmul (utilities.py:72)	16919	51.71	52.64
_evolve_observable (propagation.py:379)	8	0.01	50.64
step (propagation.py:78)	47	0.16	7.89
_reordered_taylor (propagation.py:126)	47	0.75	7.70
taylor_term (backend.py:94)	756	0.00	6.78
taylor_term (kernels.py:315)	756	6.77	6.78
_evolve_trajectory (propagation.py:426)	24	0.10	5.89
propagator (propagation.py:25)	61	0.00	4.97
matmul_clean (utilities.py:62)	984	0.02	4.48
_dissipators (contexts.py:310)	1	0.00	3.06
relaxation (relaxation.py:51)	1	0.00	3.06
_redfield (relaxation.py:231)	1	0.00	2.98
clean_up (utilities.py:25)	1199	0.01	2.49
__matmul__ (_base.py:1006)	3972	0.00	2.45
_matmul_dispatch (_base.py:875)	4048	0.00	2.45
_matmul_sparse (_compressed.py:415)	1285	0.01	2.28
result_iterator (_base.py:612)	1176	0.00	2.22
expmint (relaxation.py:213)	25	0.00	1.39
<built-in method scipy.sparse._sparsetools.csr_matmat>	1285	1.35	1.35
round_array (backend.py:77)	1275	0.00	1.32