Spinach vs spynach: zulf_pf6_1d (CPU)

run zulf_pf6_1d-numba, both sides present. Built by comparison/report.py; protocol in comparison/README.md.

Provenance

run date (UTC)	2026-10-26T13:24:14Z
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	1.27	0.00	0.00x
basis	2.01	0.01	0.01x
hamiltonian	0.51	0.05	0.09x
operators	0.76	0.03	0.04x
pulse	0.32	0.05	0.17x
acquisition	46.68	42.19	0.90x
processing	0.03	0.00	0.01x
plot	0.09	0.04	0.40x
total (wall clock)	51.83	42.39	0.82x

variable	Spinach shape	spynach shape	relative error	max \|difference\|
coil_x	(16384, 1)	(16384,)	0.000e+00	0.000e+00	compared after squeezing singleton dimensions
coil_z	(16384, 1)	(16384,)	0.000e+00	0.000e+00	compared after squeezing singleton dimensions
fid	(16384, 1)	(16384,)	1.521e-12	1.446e-17	compared after squeezing singleton dimensions
rho0	(16384, 1)	(16384,)	0.000e+00	0.000e+00	compared after squeezing singleton dimensions
rho_pulse	(16384, 1)	(16384,)	2.419e-22	5.295e-23	compared after squeezing singleton dimensions
spectrum	(16384, 1)	(16384,)	8.075e-11	2.039e-14	compared after squeezing singleton dimensions

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

by self time

function	calls	self / s	total / s
evolution>(parfor body)	8	43.28	46.62
propagator	1	3.15	3.34
ParforEngine>ParforEngine.getCompleteIntervals	153	2.58	2.69
existentials	1	0.80	0.80
clean_up	19	0.19	0.19
create	1	0.16	1.26
ParforEngine>ParforEngine.addInterval	213	0.10	0.19
run_comparison	1	0.09	51.84
ParforEngine>ParforEngine.buildParforController	58	0.08	0.10
table.saveobj	200	0.07	0.09
zulf_pf6_1d_spinach	1	0.07	51.75
dlmread	1	0.05	0.07
scomponents	2	0.05	0.08
report	385	0.05	0.06
operator	49	0.05	0.74
parallel_function>distributed_execution	58	0.05	3.13
hamiltonian	1	0.03	0.50
sparse2csr	2	0.03	0.03
tabular.parenReference	26	0.03	0.05
AbstractDataQueue>AbstractDataQueue.get.QueueLength	9	0.02	0.02
ParforEngine>ParforEngine.ParforEngine	58	0.02	0.13
apodisation	1	0.02	0.03
parallel_function	59	0.02	49.99
num2str	512	0.02	0.04
ParforEngine>ParforEngine.allIntervalsAdded	58	0.02	0.02

by total time (with callees)

function	calls	self / s	total / s
run	1	0.00	51.84
run_comparison	1	0.09	51.84
zulf_pf6_1d_spinach	1	0.07	51.75
parallel_function	59	0.02	49.99
evolution	2	0.01	47.00
evolution>(parfor body)	8	43.28	46.62
propagator	1	3.15	3.34
parallel_function>distributed_execution	58	0.05	3.13
ParforEngine>ParforEngine.getCompleteIntervals	153	2.58	2.69
basis	1	0.01	2.01
unihash	1	0.00	1.95
create	1	0.16	1.26
existentials	1	0.80	0.80
operator	49	0.05	0.74
hamiltonian	1	0.03	0.50
state	21	0.00	0.39
reduce	2	0.00	0.22
ParforEngine>ParforEngine.addInterval	213	0.10	0.19
clean_up	19	0.19	0.19
path_trace	2	0.01	0.18
parallel_function>@(initData,F)createEngine(M,initData,F,N)	58	0.00	0.14
PoolOptions>PoolOptions.createEngine	58	0.00	0.14
ClusterPoolEngine>ClusterPoolEngine.createParforEngine	58	0.00	0.14
ParforEngine>ParforEngine.ParforEngine	58	0.02	0.13
VariableBuffer>@(varargin)obj.supply(varargin{:})	214	0.00	0.11

by self time

function	calls	self / s	total / s
matmul (utilities.py:72)	16413	37.90	37.93
matmul_clean (utilities.py:62)	12	3.21	3.84
<built-in method numpy._core._multiarray_umath.count_nonzero>	40	0.36	0.36
<built-in method scipy.sparse._sparsetools.csr_matvecs>	4	0.23	0.23
_detect (propagation.py:447)	16384	0.07	0.14
<built-in method numpy.array>	1096	0.06	0.06
<method 'nonzero' of 'numpy.ndarray' objects>	916	0.06	0.06
<method 'conj' of 'numpy.ndarray' objects>	16384	0.06	0.06
_add (propagation.py:452)	9	0.04	0.06
_evolve_observable (propagation.py:379)	1	0.04	42.18
taylor_term (kernels.py:315)	148	0.03	0.03
round_array (kernels.py:231)	24	0.03	0.10
<built-in method scipy.sparse._sparsetools.csr_todense>	26	0.02	0.02
<built-in method scipy.sparse._sparsetools.csr_sort_indices>	4	0.02	0.02
_superop_side_build (operators.py:290)	99	0.02	0.04
<method 'searchsorted' of 'numpy.ndarray' objects>	672	0.01	0.01
<built-in method scipy.sparse._sparsetools.csr_matmat>	15	0.01	0.01
<built-in method builtins.isinstance>	38479	0.01	0.02
<method 'sort' of 'numpy.ndarray' objects>	2	0.01	0.01
<built-in method _abc._abc_instancecheck>	17673	0.01	0.01
<built-in method numpy.zeros>	1062	0.01	0.01
<method 'reshape' of 'numpy.ndarray' objects>	16633	0.01	0.01
<built-in method scipy.sparse._sparsetools.csr_plus_csr>	27	0.01	0.01
<lambda> (kernels.py:278)	48	0.01	0.00
matlab_round (kernels.py:63)	45	0.01	0.01

by total time (with callees)

function	calls	self / s	total / s
run (zulf_pf6_1d_spynach.py:14)	1	0.00	42.39
simulate (zulf_pf6_1d.py:73)	1	0.00	42.35
evolution (propagation.py:245)	2	0.00	42.25
_evolve_observable (propagation.py:379)	1	0.04	42.18
matmul (utilities.py:72)	16413	37.90	37.93
propagator (propagation.py:25)	2	0.00	4.08
matmul_clean (utilities.py:62)	12	3.21	3.84
clean_up (utilities.py:25)	17	0.00	0.41
count_nonzero (numeric.py:483)	41	0.00	0.36
<built-in method numpy._core._multiarray_umath.count_nonzero>	40	0.36	0.36
_choose_storage (utilities.py:50)	18	0.00	0.33
_matmul_dispatch (_base.py:875)	62	0.00	0.26
__matmul__ (_base.py:1006)	57	0.00	0.26
nnz (utilities.py:20)	28	0.00	0.25
_matmul_multivector (_compressed.py:399)	4	0.00	0.24
<built-in method scipy.sparse._sparsetools.csr_matvecs>	4	0.23	0.23
_detect (propagation.py:447)	16384	0.07	0.14
nnz (backend.py:73)	10	0.00	0.12
round_array (backend.py:77)	24	0.00	0.10
round_array (kernels.py:231)	24	0.03	0.10
__init__ (_compressed.py:30)	361	0.00	0.08
<built-in method numpy.array>	1096	0.06	0.06
<method 'nonzero' of 'numpy.ndarray' objects>	916	0.06	0.06
<method 'conj' of 'numpy.ndarray' objects>	16384	0.06	0.06
__init__ (_coo.py:32)	56	0.00	0.06