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Merge pull request #10 from s-sasaki-earthsea-wizard/bench/issue-21-e2e-pipeline
Add end-to-end CPU vs torch bench harness + 5-scene report (Issue #21)
2 parents 976949c + 0fbf71b commit 4fb9727

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‎.gitignore‎

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# Bench / profile runs produce machine-dependent artifacts (wall time,
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# RSS, VRAM-derived auto chunk sizes, hostnames, /home paths) that have
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# no meaning on a different machine and previously leaked host info
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# into the public repo (audited 2026-05-02). The whole logs_*/ tree is
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# into the public repo (audited 2026-05-02). Anything matching logs_*
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# (directories like logs_baseline/ as well as the end-to-end harness's
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# top-level diff files like logs_e2e_<scene>_walls_diff.json) is
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# therefore untracked.
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#
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# What stays tracked under benchmark/: harness scripts (scripts/),
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# Python utilities (tools/), test fixtures (fixtures/), reports
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# (reports/), the top-level Makefile, and requirements.txt.
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# Numerical findings are transcribed into the corresponding report
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# by hand; raw artifacts live only on the developer's machine.
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logs_*/
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logs_*
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__pycache__/

‎fixtures/FernandinaSenDT128_cpu.txt‎

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# CPU baseline fixture for FernandinaSenDT128 — byte-for-byte identical
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# to fixtures/FernandinaSenDT128_torch.txt except for the trailing
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# `mintpy.*.solver = torch` lines, which are omitted so MintPy uses the
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# default cpu solver for both invert_network and correct_topography.
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# Used by the Issue #21 end-to-end bench harness paired with the torch
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# fixture; the harness asserts that the cpu and torch fixtures differ
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# only in the 2 solver lines so the wall comparison is fair.
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# vim: set filetype=cfg:
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##------------------------------- ISCE/topsStack ----------------------##
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#ssaraopt = --platform=SENTINEL-1A,SENTINEL-1B -r 128 -f 589,590,591,592,593 -e 2017-07-01
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#sentinelStack.boundingBox = '-1 0.15 -91.6 -90.9'
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#sentinelStack.subswath = 1 2 # comment
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#sentinelStack.numConnections = 5 # comment
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#sentinelStack.azimuthLooks = 5 # comment
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#sentinelStack.rangeLooks = 15 # comment
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#sentinelStack.filtStrength = 0.2 # comment
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#sentinelStack.unwMethod = snaphu # comment
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#sentinelStack.coregistration = auto # comment
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#subset.y0:y1,x0:x1 = 1150:1600,1070:1670
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##-------------------------------- MintPy -----------------------------##
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########## 1. Load Data (--load to exit after this step)
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## load_data.py -H to check more details and example inputs.
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mintpy.load.processor = isce
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##---------for ISCE only:
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mintpy.load.metaFile = ../reference/IW*.xml
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mintpy.load.baselineDir = ../baselines
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##---------interferogram datasets:
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mintpy.load.unwFile = ../merged/interferograms/*/filt_*.unw
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mintpy.load.corFile = ../merged/interferograms/*/filt_*.cor
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mintpy.load.connCompFile = ../merged/interferograms/*/filt_*.unw.conncomp
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##---------geometry datasets:
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mintpy.load.demFile = ../merged/geom_reference/hgt.rdr
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mintpy.load.lookupYFile = ../merged/geom_reference/lat.rdr
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mintpy.load.lookupXFile = ../merged/geom_reference/lon.rdr
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mintpy.load.incAngleFile = ../merged/geom_reference/los.rdr
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mintpy.load.azAngleFile = ../merged/geom_reference/los.rdr
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mintpy.load.shadowMaskFile = ../merged/geom_reference/shadowMask.rdr
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mintpy.load.waterMaskFile = None
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mintpy.reference.lalo = -0.30,-91.43
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mintpy.topographicResidual.stepDate = 20170910,20180613 #eruption dates
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mintpy.deramp = linear

‎fixtures/FernandinaSenDT128_torch.txt‎

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# GPU benchmark template — copy of docs/templates/FernandinaSenDT128.txt
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# with mintpy.networkInversion.solver = torch added at the end so the
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# invert_network step uses the GPU-batched solver.
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# with the two opt-in GPU solver flags added at the end so both the
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# invert_network and correct_topography steps use the GPU-batched
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# solvers. Used by the Issue #21 end-to-end bench harness.
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# vim: set filetype=cfg:
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##------------------------------- ISCE/topsStack ----------------------##
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#ssaraopt = --platform=SENTINEL-1A,SENTINEL-1B -r 128 -f 589,590,591,592,593 -e 2017-07-01
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mintpy.topographicResidual.stepDate = 20170910,20180613 #eruption dates
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mintpy.deramp = linear
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##---------GPU acceleration of invert_network (fork-only, opt-in):
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##---------GPU acceleration (fork-only, opt-in):
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mintpy.networkInversion.solver = torch
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mintpy.topographicResidual.solver = torch

‎fixtures/GalapagosSenDT128_cpu.txt‎

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# CPU baseline fixture for GalapagosSenDT128 — derived from the
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# dataset's embedded mintpy/inputs/GalapagosSenDT128.template (Zenodo
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# record 4743058). Differs from fixtures/GalapagosSenDT128_torch.txt
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# only by the trailing `mintpy.*.solver = torch` lines, which are
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# omitted so MintPy uses the default cpu solver for both invert_network
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# and correct_topography. Used by the Issue #21 end-to-end bench
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# harness paired with the torch fixture.
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#
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# Raw ISCE inputs referenced under $GALAPAGOS_DIR/... no longer exist
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# on disk (the Issue #6 large-scene bench cleaned them up after load).
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# The harness pre-seeds ifgramStack.h5 + geometryRadar.h5 symlinks
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# into the workdir's inputs/ from the original mintpy/inputs/ so
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# smallbaselineApp skips load_data via update-mode; the $GALAPAGOS_DIR
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# variable just needs to resolve to a valid path string for parsing.
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# vim: set filetype=cfg:
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##----------------------------- SentinelStack/ISCE ---------------------##
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cleanopt = 1 # [ 0 / 1 / 2 / 3 4] 0: no cleaning, 1: largest files, 2: merged/etc, PROCESS dirs, 3: SLC,RAW, 4: everything
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ssaraopt = --platform=SENTINEL-1A,SENTINEL-1B -r 128 -f 587,588,589,590,591,592,593 -e 2018-07-01
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processor = isce
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sentinelStack.demDir = /nethome/famelung/Sentinel/GalapagosT128SenVVD/DEM
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sentinelStack.boundingBox = '-1 0.15 -91.6 -90.9'
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sentinelStack.subswath = 1 2 # comment
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sentinelStack.numConnections = 5 # comment
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sentinelStack.azimuthLooks = 5 # comment
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sentinelStack.rangeLooks = 15 # comment
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sentinelStack.filtStrength = 0.2 # comment
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sentinelStack.unwMethod = snaphu # comment
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sentinelStack.coregistration = auto # comment
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##-------------------------------- MintPy -----------------------------##
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mintpy.load.processor = isce
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##---------for ISCE only:
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mintpy.load.metaFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/master/IW*.xml
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mintpy.load.baselineDir = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/baselines
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##---------interferogram datasets:
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mintpy.load.unwFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/merged/interferograms/*/filt_*.unw
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mintpy.load.corFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/merged/interferograms/*/filt_*.cor
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mintpy.load.connCompFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/merged/interferograms/*/filt_*.unw.conncomp
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##---------geometry datasets:
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mintpy.load.demFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/merged/geom_master/hgt.rdr
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mintpy.load.lookupYFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/merged/geom_master/lat.rdr
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mintpy.load.lookupXFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/merged/geom_master/lon.rdr
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mintpy.load.incAngleFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/merged/geom_master/los.rdr
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mintpy.load.azAngleFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/merged/geom_master/los.rdr
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mintpy.load.shadowMaskFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/merged/geom_master/shadowMask.rdr
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mintpy.load.waterMaskFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/merged/geom_master/waterMask.rdr
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mintpy.load.bperpFile = $GALAPAGOS_DIR/GalapagosSenDT128/ISCE/merged/baseline_grid/*/bperp.rdr
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mintpy.subset.yx = 400:2400,0:1700
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mintpy.reference.lalo = -0.31,-91.22
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mintpy.network.endDate = 20180626
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mintpy.unwrapError.method = bridging+phase_closure #[bridging / phase_closure / no], auto for no
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mintpy.topographicResidual.stepFuncDate = 20150524,20150616,20170321,20170910,20180613 #Wolf,Wolf,CerroAzul,Fernandina,Fernandina
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mintpy.deramp = linear

‎fixtures/GalapagosSenDT128_torch.txt‎

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# GPU benchmark template for GalapagosSenDT128 — copy of the dataset's
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# embedded inputs/GalapagosSenDT128.template (Zenodo record 4743058) with
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# mintpy.networkInversion.solver = torch added at the end so the
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# invert_network step uses the GPU-batched solver. The cpu baseline run
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# uses the embedded template directly (no solver line, defaults to cpu).
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# the two opt-in GPU solver flags added at the end so both the
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# invert_network and correct_topography steps use the GPU-batched
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# solvers. The cpu baseline run uses the embedded template directly
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# (no solver lines, defaults to cpu). Used by the Issue #21 end-to-end
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# bench harness.
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# vim: set filetype=cfg:
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##----------------------------- SentinelStack/ISCE ---------------------##
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cleanopt = 1 # [ 0 / 1 / 2 / 3 4] 0: no cleaning, 1: largest files, 2: merged/etc, PROCESS dirs, 3: SLC,RAW, 4: everything
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mintpy.topographicResidual.stepFuncDate = 20150524,20150616,20170321,20170910,20180613 #Wolf,Wolf,CerroAzul,Fernandina,Fernandina
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mintpy.deramp = linear
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##---------GPU acceleration of invert_network (fork-only, opt-in):
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##---------GPU acceleration (fork-only, opt-in):
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mintpy.networkInversion.solver = torch
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mintpy.topographicResidual.solver = torch

‎fixtures/KujuAlosAT422F650_cpu.txt‎

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# CPU baseline fixture for KujuAlosAT422F650 — derived from
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# docs/templates/KujuAlosAT422F650.txt with the same Issue #19 bench
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# overrides as fixtures/KujuAlosAT422F650_torch.txt
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# (`troposphericDelay.method = no` to skip ERA5 download for ALOS-1
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# L-band 2007-2011 data, `save.hdfEos5 = no` to skip HE5 packaging).
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# Differs from the torch fixture only by the trailing
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# `mintpy.*.solver = torch` lines so the wall comparison is fair.
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# vim: set filetype=cfg:
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########## 1. Load Data (--load to exit after this step)
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## auto - automatic path pattern for Univ of Miami file structure
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## load_data.py -H to check more details and example inputs.
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mintpy.load.processor = roipac #[isce,roipac,gamma,], auto for isce
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##---------interferogram datasets:
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mintpy.load.unwFile = ./../interferograms/*/filt_*.unw #[path2unw_file]
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mintpy.load.corFile = ./../interferograms/*/filt_*.cor #[path2cor_file]
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mintpy.load.connCompFile = None #[path2conn_file]
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mintpy.load.intFile = None #[path2int_file]
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##---------geometry datasets:
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mintpy.load.demFile = ./../geometry/radar*.hgt #[path2hgt_file]
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mintpy.load.lookupYFile = ./../geometry/geomap*.trans #[path2lookup_lat/y_file]
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mintpy.load.lookupXFile = ./../geometry/geomap*.trans #[path2lookup_lon/x_file]
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mintpy.load.incAngleFile = None #[path2los_file]
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mintpy.load.azAngleFile = None #[path2los_file]
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mintpy.load.shadowMaskFile = None #[path2shadow_file]
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mintpy.load.waterMaskFile = None #[path2water_mask_file]
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##————————————————————————————— PROCESSING Options ——-----————————————————————##
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mintpy.reference.lalo = 33.0655, 131.2076
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mintpy.troposphericDelay.method = no # bench: run on raw timeseries.h5
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mintpy.deramp = no
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mintpy.save.hdfEos5 = no # bench: skip HE5 packaging

‎fixtures/KujuAlosAT422F650_torch.txt‎

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# GPU benchmark template — copy of docs/templates/KujuAlosAT422F650.txt
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# with mintpy.networkInversion.solver = torch added at the end so the
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# invert_network step uses the GPU-batched solver. Used by Issue #19
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# Tier 1 bench survey (extends Fernandina + Galapagos with an ALOS
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# L-band / ROI_PAC ingest path).
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# with the two opt-in GPU solver flags added at the end so both the
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# invert_network and correct_topography steps use the GPU-batched
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# solvers. Used by Issue #19 Tier 1 bench survey (ALOS L-band /
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# ROI_PAC ingest path) and the Issue #21 end-to-end bench harness.
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#
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# Differences from docs/templates beyond the solver line:
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# - mintpy.troposphericDelay.method = no (skip ERA5 download;
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mintpy.deramp = no
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mintpy.save.hdfEos5 = no # bench: skip HE5 packaging
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##---------GPU acceleration of invert_network (fork-only, opt-in):
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##---------GPU acceleration (fork-only, opt-in):
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mintpy.networkInversion.solver = torch
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mintpy.topographicResidual.solver = torch

‎fixtures/SanFranBaySenD42_cpu.txt‎

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# CPU baseline fixture for SanFranBaySenD42 — derived from
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# docs/templates/SanFranBaySenD42.txt with the Issue #19 bench
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# `troposphericDelay.method = no` override applied to match
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# fixtures/SanFranBaySenD42_torch.txt (skip ERA5 download for GMTSAR
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# bench). Differs from the torch fixture only by the trailing
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# `mintpy.*.solver = torch` lines so the wall comparison is fair.
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# vim: set filetype=cfg:
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## manually add the following metadata
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HEADING = -168
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ALOOKS = 8
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RLOOKS = 32
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########## computing resource configuration
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mintpy.compute.maxMemory = 8
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mintpy.compute.cluster = local
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mintpy.compute.numWorker = 4
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########## 1. Load Data (--load to exit after this step)
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mintpy.load.processor = gmtsar #[isce, aria, hyp3, gmtsar, snap, gamma, roipac], auto for isce
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mintpy.load.metaFile = ../supermaster.PRM
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mintpy.load.baselineDir = ../baseline_table.dat
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##---------interferogram datasets:
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mintpy.load.unwFile = ../interferograms/*/unwrap_ll*.grd
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mintpy.load.corFile = ../interferograms/*/corr_ll*.grd
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##---------geometry datasets:
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mintpy.load.demFile = ../geometry/dem.grd
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mintpy.load.incAngleFile = ../geometry/incidence_angle.grd
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mintpy.load.azAngleFile = ../geometry/azimuth_angle.grd
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mintpy.load.waterMaskFile = ../geometry/water_mask.grd
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mintpy.reference.lalo = 37.69, -122.07
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mintpy.troposphericDelay.method = no
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mintpy.deramp = no
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mintpy.topographicResidual = yes

‎fixtures/SanFranBaySenD42_torch.txt‎

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# GPU benchmark template — copy of docs/templates/SanFranBaySenD42.txt
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# with mintpy.networkInversion.solver = torch added at the end so the
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# invert_network step uses the GPU-batched solver. Used by Issue #19
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# Tier 1 bench survey (extends Fernandina + Galapagos with a GMTSAR
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# ingest path + D = 333).
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# with the two opt-in GPU solver flags added at the end so both the
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# invert_network and correct_topography steps use the GPU-batched
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# solvers. Used by Issue #19 Tier 1 bench survey (GMTSAR ingest path
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# + D = 333) and the Issue #21 end-to-end bench harness.
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# vim: set filetype=cfg:
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## manually add the following metadata
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HEADING = -168
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mintpy.deramp = no
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mintpy.topographicResidual = yes
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##---------GPU acceleration of invert_network (fork-only, opt-in):
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##---------GPU acceleration (fork-only, opt-in):
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mintpy.networkInversion.solver = torch
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mintpy.topographicResidual.solver = torch

‎fixtures/SanFranSenDT42_cpu.txt‎

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# CPU baseline fixture for SanFranSenDT42 — derived from
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# docs/templates/SanFranSenDT42.txt with the `weightFunc = no`
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# inversion speed-up retained to match
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# fixtures/SanFranSenDT42_torch.txt. Differs from the torch fixture
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# only by the trailing `mintpy.*.solver = torch` lines so the wall
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# comparison is fair. Tropo defaults to pyaps (ERA5) on both sides
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# matching the upstream docs template.
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# vim: set filetype=cfg:
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mintpy.load.processor = aria #[isce, aria, snap, gamma, roipac], auto for isce
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##---------interferogram datasets:
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mintpy.load.unwFile = ../stack/unwrapStack.vrt
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mintpy.load.corFile = ../stack/cohStack.vrt
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mintpy.load.connCompFile = ../stack/connCompStack.vrt
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##---------geometry datasets:
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mintpy.load.demFile = ../DEM/*.dem
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mintpy.load.incAngleFile = ../incidenceAngle/*.vrt
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mintpy.load.azAngleFile = ../azimuthAngle/*.vrt
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mintpy.load.waterMaskFile = ../mask/watermask.msk
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mintpy.reference.lalo = 37.69, -122.07
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mintpy.unwrapError.method = bridging
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mintpy.deramp = no
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## options to speedup the processing (fast but not the best)
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mintpy.networkInversion.weightFunc = no

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