Page History
...
| number of cores | old algorithm | ←Ratio t1/tN | new algorithm v2 | ←Ratio t1/tN | new algorithm v2 with loop | ←Ratio t1/tN |
|---|---|---|---|---|---|---|
1 | 11102 | 1 | 8425 | 1 | 8570 | 1 |
| 80 | 281 | 175.4 | 170.6 | 50.2 | ||
| Ratio t1/t80 | 39.5 | 48.0 | ||||
80 - better start-stop time def, times for 3 jobs: average: | 418.3 408.4 401.0 409.2 | 157.4 165.1 168.4 163.6 | 1 | 148.6 148.2 147.7 148.2 | ||
Ratio t1/t80 better start-stop | 27.1 | 51.5 | 57.8 of 80 | |||
120 times for 3 jobs: average: | 252.3 250.4 266.7 256.5 | 131.2 132.0 132.0 131.7 | 120.4 120.5 121.6 120.8 | |||
Ratio t1/t120 | 43.3 | 64.0 | 70.9 of 120 |
2024-03-08 time consumption by
...
the calib_jungfrau_v2 algorithm
Description
...
Code of calib_jungfrau_v2 is interlaces with timestamps. The list of timestamps is returned along with calibrated array.
| Code Block | ||||
|---|---|---|---|---|
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def calib_jungfrau_v2(det, evt, cmpars=(7,3,200,10), **kwa):
"""
v2 - improving performance, reduce time and memory consumption, use peds-offset constants
Returns calibrated jungfrau data
- gets constants
- gets raw data
- evaluates (code - pedestal - offset)
- applys common mode correction if turned on
- apply gain factor
Parameters
- det (psana.Detector) - Detector object
- evt (psana.Event) - Event object
- cmpars (tuple) - common mode parameters
- cmpars[0] - algorithm # 7-for jungfrau
- cmpars[1] - control bit-word 1-in rows, 2-in columns
- cmpars[2] - maximal applied correction
- **kwa - used here and passed to det.mask_v2 or det.mask_comb
- nda_raw - if not None, substitutes evt.raw()
- mbits - DEPRECATED parameter of the det.mask_comb(...)
- mask - user defined mask passed as optional parameter
"""
t00 = time()
src = det.source # - src (psana.Source) - Source object
nda_raw = kwa.get('nda_raw', None)
loop_segs = kwa.get('loop_segs', False)
arr = det.raw(evt) if nda_raw is None else nda_raw # shape:(<npanels>, 512, 1024) dtype:uint16
if arr is None: return None
t01 = time()
detname = string_from_source(det.source)
t02 = time()
odc = cache.detcache_for_detname(detname)
t03 = time()
first_entry = odc is None
if first_entry:
odc = cache.add_detcache(det, evt)
odc.cmps = det.common_mode(evt) if cmpars is None else cmpars
odc.mask = det.mask_total(evt, **kwa)
poff = odc.poff # 4d pedestals + offset shape:(3, 1, 512, 1024) dtype:float32
gfac = odc.gfac # 4d gain factors evaluated form gains
mask = odc.mask
outa = odc.outa
cmps = odc.cmps
t04 = time()
if first_entry:
logger.info('\n ====================== det.name: %s' % det.name\
+'\n detname from source: %s' % string_from_source(det.source)\
+info_ndarr(arr, '\n calib_jungfrau arr ')\
+info_ndarr(poff, '\n calib_jungfrau peds+off')\
+info_ndarr(gfac, '\n calib_jungfrau gfac')\
+info_ndarr(mask, '\n calib_jungfrau mask')\
+info_ndarr(outa, '\n calib_jungfrau outa')\
+info_ndarr(cmps, '\n calib_jungfrau common mode parameters ')
+'\n loop over segments: %s' % loop_segs)
if loop_segs:
nsegs = arr.shape[0]
shseg = arr.shape[-2:] # (512, 1024)
for i in range(nsegs):
arr1 = arr[i,:]
mask1 = mask[i,:]
gfac1 = gfac[:,i,:,:]
poff1 = poff[:,i,:,:]
arr1.shape = (1,) + shseg
mask1.shape = (1,) + shseg
gfac1.shape = (3,1,) + shseg
poff1.shape = (3,1,) + shseg
out1, times = calib_jungfrau_single_panel(arr1, gfac1, poff1, mask1, cmps)
outa[i,:] = out1[0,:]
resp = outa
else:
resp, times = calib_jungfrau_single_panel(arr, gfac, poff, mask, cmps)
t17 = time()
times = (t00, t01, t02, t03, t04) + times + (t17,)
return resp, np.array(times, dtype=np.float64)
def calib_jungfrau_single_panel(arr, gfac, poff, mask, cmps):
""" Arrays should have a single panel shape, example for 8-panel detector
arr: shape:(8, 512, 1024) size:4194304 dtype:uint16 [2906 2945 2813 2861 3093...]
poff: shape:(3, 8, 512, 1024) size:12582912 dtype:float32 [2922.283 2938.098 2827.207 2855.296 3080.415...]
gfac: shape:(3, 8, 512, 1024) size:12582912 dtype:float32 [0.02490437 0.02543429 0.02541406 0.02539831 0.02544083...]
mask: shape:(8, 512, 1024) size:4194304 dtype:uint8 [1 1 1 1 1...]
cmps: shape:(16,) size:16 dtype:float64 [ 7. 1. 100. 0. 0....]
"""
t05 = time()
# Define bool arrays of ranges
gr0 = arr < BW1 # 490 us
gr1 =(arr >= BW1) & (arr<BW2) # 714 us
gr2 = arr >= BW3 # 400 us
t06 = time()
factor = np.select((gr0, gr1, gr2), (gfac[0,:], gfac[1,:], gfac[2,:]), default=1) # 2msec
t07 = time()
pedoff = np.select((gr0, gr1, gr2), (poff[0,:], poff[1,:], poff[2,:]), default=0)
t08 = time()
# Subtract offset-corrected pedestals
arrf = np.array(arr & MSK, dtype=np.float32)
t09 = time()
arrf -= pedoff
t10 = time()
if cmps is not None:
mode, cormax = int(cmps[1]), cmps[2]
npixmin = cmps[3] if len(cmps)>3 else 10
if mode>0:
#arr1 = store.arr1
#grhg = np.select((gr0, gr1), (arr1, arr1), default=0)
logger.debug(info_ndarr(gr0, 'gain group0'))
logger.debug(info_ndarr(mask, 'mask'))
t0_sec_cm = time()
t11 = time()
gmask = np.bitwise_and(gr0, mask) if mask is not None else gr0
t12 = time()
#sh = (nsegs, 512, 1024)
hrows = 256 #512/2
for s in range(arrf.shape[0]):
t13 = time()
if mode & 4: # in banks: (512/2,1024/16) = (256,64) pixels # 100 ms
common_mode_2d_hsplit_nbanks(arrf[s,:hrows,:], mask=gmask[s,:hrows,:], nbanks=16, cormax=cormax, npix_min=npixmin)
common_mode_2d_hsplit_nbanks(arrf[s,hrows:,:], mask=gmask[s,hrows:,:], nbanks=16, cormax=cormax, npix_min=npixmin)
t14 = time()
if mode & 1: # in rows per bank: 1024/16 = 64 pixels # 275 ms
common_mode_rows_hsplit_nbanks(arrf[s,], mask=gmask[s,], nbanks=16, cormax=cormax, npix_min=npixmin)
t15 = time()
if mode & 2: # in cols per bank: 512/2 = 256 pixels # 290 ms
common_mode_cols(arrf[s,:hrows,:], mask=gmask[s,:hrows,:], cormax=cormax, npix_min=npixmin)
common_mode_cols(arrf[s,hrows:,:], mask=gmask[s,hrows:,:], cormax=cormax, npix_min=npixmin)
logger.debug('TIME: common-mode correction time = %.6f sec' % (time()-t0_sec_cm))
t16 = time()
arrf = arrf * factor if mask is None else arrf * factor * mask # gain correction
return arrf, (t05, t06, t07, t08, t09, t10, t11, t12, t13, t14, t15, t16)
|
Results
time differences between timestamps are shown in msec, as dt[i] = t[i]-t[i-1]
| Code Block | ||||
|---|---|---|---|---|
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ana-4.0.59-py3 [dubrovin@sdfmilan204:~/LCLS/con-py3]$ mpirun -n 1 python Detector/examples/test-scaling-mpi.py 6 execute command: ['Detector/examples/test-scaling-mpi.py', '6'] rank:000 cpu_num:000 size:01 [I] L0398: test-scaling-mpi.py ====================== det.name: CxiDs1.0:Jungfrau.0 detname from source: CxiDs1.0:Jungfrau.0 calib_jungfrau arr shape:(8, 512, 1024) size:4194304 dtype:uint16 [2906 2945 2813 2861 3093...] calib_jungfrau peds+off shape:(3, 8, 512, 1024) size:12582912 dtype:float32 [2922.283 2938.098 2827.207 2855.296 3080.415...] calib_jungfrau gfac shape:(3, 8, 512, 1024) size:12582912 dtype:float32 [0.02490437 0.02543429 0.02541406 0.02539831 0.02544083...] calib_jungfrau mask shape:(8, 512, 1024) size:4194304 dtype:uint8 [1 1 1 1 1...] calib_jungfrau outa shape:(8, 512, 1024) size:4194304 dtype:float32 [0. 0. 0. 0. 0....]ndarray from tuple: calib_jungfrau common mode parameters shape:(4,) size:4 dtype:int64 [ 7 7 200 10] loop over segments: False rank:000 cpu_num:000 nevt:0000 time:10.075316 [I] L0607: test-scaling-mpi.py rank:000 job 2-nd evt time:1710175768.956751 saveed in file: figs/mpi-job-2nd-evt-time.txt rank:000 cpu_num:000 nevt:0010 time:0.578157 rank:000 cpu_num:000 nevt:0020 time:0.541390 rank:000 cpu_num:000 nevt:0030 time:0.518435 rank:000 cpu_num:000 nevt:0040 time:0.575237 rank:000 cpu_num:000 nevt:0050 time:0.577683 rank:000 cpu_num:000 nevt:0060 time:0.580110 rank:000 cpu_num:000 nevt:0070 time:0.579042 rank:000 cpu_num:000 nevt:0080 time:0.577012 rank:000 cpu_num:000 nevt:0090 time:0.577242 rank:000 cpu_num:000 nevt:0100 time:0.575845 [I] L0640: test-scaling-mpi.py Summary for rank:000 job 2-nd evt time:1710175768.956751 time total (sec):64.189028 ... QStandardPaths: XDG_RUNTIME_DIR not set, defaulting to '/tmp/runtime-dubrovin' hostname:sdfmilan204 rank:000 cpu:000 cmt:8p-v6 proc time (sec) mean: 0.5668 +/- 0.0125 rms: 0.0186 +/- 0.0088 plot_figs_v2 fnprefix: figs/fig-mpi-data-8p-v6-sdfmilan204-ncores01 title: sdfmilan204 rank 000 of 001 cpu_num 000 ... 000 1709951462.307 0.896 0.019 0.008 0.003 0.001 1.485 5.141 4.795 1.873 1.081 0.389 0.712 552.819 11.156 31.449 35.934 3.994 001 1709951462.963 0.883 0.015000 1709951462.307 0.896 0.019 0.008 0.003 0.001 1.485 5.141 4.795 1.873 1.081 0.389 0.712 552.819 11.156 31.449 35.934 3.994 001 1709951462.963 0.883 0.015 0.003 0.002 0.001 1.478 5.195 4.963 1.878 1.216 0.269 0.686 553.074 11.234 31.461 36.158 3.678 002 1709951463.620 0.736 0.013 0.003 0.001 0.000 1.325 5.175 4.901 1.848 1.186 0.268 0.700 556.101 10.969 31.402 35.841 2.868 003 1709951464.278 0.672 0.014 0.003 0.001 0.001 1.491 5.275 4.833 1.874 1.156 0.271 0.695 555.187 11.171 31.454 36.028 3.687 004 1709951464.935 0.672 0.015 0.003 0.001 0.001 1.407 5.255 4.980 1.860 1.188 0.265 0.681 556.062 11.451 31.451 35.880 4.575 005 1709951465.599 1.828 0.014 0.004 0.001 0.000 2.680 5.197 5.440 3.114 1.231 0.275 1.372 552.135 11.523 31.402 35.908 3.678 006 1709951466.259 0.675 0.016 0.003 0.001 0.001 1.370 5.214 4.860 1.832 1.193 0.278 0.691 552.219 11.224 31.394 36.250 3.223 007 1709951466.914 0.873 0.022 0.008 0.002 0.001 1.367 5.303 5.026 1.864 1.209 0.363 0.718 551.416 11.165 31.385 36.066 4.027 008 1709951467.569 0.842 0.014 0.003 0.001 0.001 1.389 5.209 4.796 1.854 1.119 0.264 0.699 551.404 11.150 31.370 36.021 2.912 009 1709951468.223 0.838 0.014 0.003 0.001 0.001 1.381 5.234 4.893 1.874 1.191 0.264 0.690 554.368 11.403 31.414 35.849 5.744 010 1709951468.886 1.976 0.014 0.003 0.001 0.001 3.203 5.134 5.291 3.050 1.211 0.310 0.857 556.652 11.083 31.411 35.851 2.866 011 1709951469.550 0.858 0.016 0.003 0.001 0.001 1.345 5.208 4.876 1.868 1.172 0.266 0.656 554.571 11.404 31.372 35.806 3.667 012 1709951470.207 0.782 0.015 0.003 0.001 0.000 1.321 5.140 4.890 1.849 1.178 0.276 0.671 552.930 11.304 31.376 35.908 3.159 013 1709951470.863 0.902 0.021 0.009 0.002 0.001 1.363 5.316 5.044 1.849 1.206 0.368 0.675 551.805 11.463 31.375 35.844 3.764 014 1709951471.518 0.880 0.014 0.003 0.001 0.001 1.352 5.144 4.876 1.856 1.161 0.269 0.696 551.738 11.189 31.328 36.213 2.871 015 1709951472.172 0.675 0.015 0.003 0.001 0.001 1.365 5.187 4.965 1.848 1.222 0.276 0.686 554.584 11.385 31.463 35.826 5.604 016 1709951472.835 1.795 0.014 0.003 0.001 0.001 3.175 5.209 5.346 3.035 1.211 0.305 1.116 553.065 11.606 31.393 35.818 2.867 017 1709951473.495 0.679 0.014 0.003 0.001 0.001 1.370 5.263 4.933 1.799 1.271 0.279 0.690 555.862 11.446 31.403 36.174 3.659 018 1709951474.154 0.793 0.013 0.003 0.001 0.000 1.343 5.124 4.980 1.845 1.195 0.269 0.686 553.584 11.318 31.364 35.820 2.865 019 1709951474.810 0.783 0.014 0.004 0.001 0.001 1.342 5.148 4.929 1.868 1.203 0.270 0.695 554.589 11.329 31.414 36.334 3.758 020 1709951475.469 0.759 0.021 0.010 0.002 0.001 1.384 5.295 5.002 1.847 1.193 0.370 0.680 552.323 10.995 31.373 36.190 3.232 021 1709951476.124 0.789 0.014 0.003 0.001 0.001 1.334 5.149 4.863 1.873 1.159 0.262 0.694 551.174 11.586 31.429 36.093 3.683 022 1709951476.778 0.678 0.017 0.003 0.001 0.001 1.357 5.206 4.942 1.861 1.139 0.263 0.607 552.973 11.487 31.450 36.206 2.861 023 1709951477.434 0.829 0.014 0.004 0.001 0.001 1.348 5.168 4.786 1.909 1.113 0.262 0.706 556.439 11.474 31.396 35.960 5.616 024 1709951478.098 1.973 0.014 0.003 0.001 0.001 3.199 5.158 5.316 3.041 1.199 0.301 0.712 552.752 11.263 31.464 35.887 2.860 025 1709951478.758 0.863 0.014 0.003 0.001 0.001 1.356 5.180 4.890 1.876 1.167 0.269 0.661 554.007 11.324 31.501 35.965 3.695 026 1709951479.415 0.732 0.014 0.004 0.001 0.001 1.335 5.185 4.794 1.839 1.143 0.273 0.693 556.165 11.355 31.592 35.691 2.909 027 1709951480.073 0.819 0.014 0.003 0.001 0.001 1.350 5.151 4.818 1.882 1.267 0.249 0.675 554.184 11.207 31.441 37.435 3.780 028 1709951480.732 0.796 0.021 0.009 0.002 0.001 1.341 5.367 5.031 1.855 1.180 0.367 0.683 552.998 11.480 31.440 36.413 3.181 029 1709951481.389 0.770 0.014 0.003 0.001 0.001 1.347 5.169 4.633 1.886 1.028 0.263 0.737 551.741 11.409 31.451 36.130 3.669 030 1709951482.044 0.798 0.014 0.002 0.001 0.001 1.347 5.197 4.551 1.838 0.988 0.286 0.742 552.585 11.286 31.398 36.214 2.938 031 1709951482.705 0.780 0.013 0.003 0.001 0.001 1.319 5.225 4.844 1.896 1.138 0.273 0.686 551.471 11.422 31.405 36.418 3.673 032 1709951483.360 0.744 0.015 0.003 0.001 0.001 1.364 5.174 5.049 1.848 1.197 0.271 0.690 555.717 11.567 31.450 36.004 4.597 033 1709951484.024 1.865 0.014 0.003 0.001 0.001 3.177 5.151 5.408 3.073 1.192 0.302 0.859 551.751 11.420 31.518 36.076 3.935 034 1709951484.684 0.763 0.013 0.003 0.001 0.001 1.366 5.156 4.923 1.836 1.180 0.270 0.654 553.017 11.105 31.419 39.409 2.934 035 1709951485.343 0.744 0.013 0.007 0.001 0.001 1.339 5.207 4.864 1.884 1.127 0.268 0.715 552.273 11.389 31.421 36.091 3.676 036 1709951485.998 0.783 0.015 0.002 0.001 0.000 1.353 5.147 4.905 1.826 1.190 0.268 0.676 553.218 11.577 31.411 36.138 2.858 037 1709951486.654 0.825 0.014 0.003 0.001002 0.001 1.353478 5.150195 4.901963 1.883878 1.196216 0.278269 0.677686 553.971074 11.355234 31.408461 36.152158 3.771678 038002 17099514871709951463.312620 0.751736 0.023013 0.009003 0.002001 0.001000 1.380325 5.332175 54.054901 1.862848 1.162186 0.368268 0.659700 552556.360101 1110.561969 31.421402 35.896841 32.161868 039003 17099514871709951464.967278 0.674672 0.013014 0.004003 0.001 0.000001 1.400491 5.185275 4.996833 1.900874 1.302156 0.254271 0.668695 551555.565187 11.267171 31.442454 36.180028 3.667687 040004 17099514881709951464.622935 0.834672 0.014015 0.003 0.001 0.001 1.353407 5.120255 4.946980 1.814860 1.219188 0.290265 0.681 552556.905062 11.643451 31.436451 35.873880 24.932575 041005 17099514891709951465.277599 01.678828 0.014 0.003004 0.001 0.001000 12.372680 5.282197 45.882440 13.873114 1.193231 0.274275 01.710372 552.397135 11.231523 31.636402 35.957908 3.679678 042006 17099514891709951466.933259 0.833675 0.014016 0.003 0.001 0.001 1.350370 5.175214 4.920860 1.853832 1.181193 0.268278 0.666691 556552.858219 11.618224 31.441394 36.003250 23.847223 043007 17099514901709951466.593914 0.840873 0.014022 0.003008 0.001002 0.001 1.358367 5.231303 45.746026 1.896864 1.068209 0.262363 0.703718 555551.706416 11.318165 31.440385 3536.805066 54.592027 044008 17099514911709951467.256569 10.952842 0.014 0.003 0.002001 0.005001 31.188389 5.193209 54.317796 31.031854 1.187119 0.306264 0.767699 552551.667404 11.568150 31.327370 3536.856021 2.917912 045009 17099514911709951468.918223 0.880838 0.014 0.004003 0.001 0.001 1.378381 5.135234 4.664893 1.875874 1.036191 0.266264 0.731690 551554.976368 11.368403 31.469414 3635.103849 35.685744 046010 17099514921709951468.573886 01.842976 0.014 0.003 0.001 0.001 13.362203 5.220134 45.928291 13.821050 1.194211 0.281310 0.673857 553556.601652 11.359083 31.435411 35.888851 2.857866 047011 17099514931709951469.229550 0.771858 0.013016 0.003 0.001 0.001 1.374345 5.173208 4.786876 1.860868 1.136172 0.269266 0.694656 552554.346571 11.322404 31.404372 35.964806 3.676667 048012 17099514931709951470.884207 0.738782 0.015 0.003 0.001 0.001000 1.352321 5.152140 4.954890 1.839849 1.420178 0.273276 0.638671 552.455930 11.343304 31.405376 35.867908 23.919159 049013 17099514941709951470.539863 0.735902 0.014021 0.003009 0.001002 0.001 1.350363 5.213316 45.919044 1.858849 1.203206 0.267368 0.684675 555551.511805 11.320463 31.425375 3635.101844 3.734764 050014 17099514951709951471.199518 0.792880 0.021014 0.010003 0.002001 0.001 1.350352 5.295144 4.967876 1.875856 1.159161 0.362269 0.695696 555551.633738 11.365189 31.427328 3536.871213 32.206871 051015 17099514951709951472.858172 0.725675 0.014015 0.003 0.001 0.001 1.355365 5.179187 4.966965 1.855848 1.193222 0.267276 0.663686 552554.571584 11.290385 31.442463 3635.051826 35.706604 052016 17099514961709951472.513835 01.732795 0.014 0.003 0.001 0.001 13.351175 5.148209 45.894346 13.835035 1.204211 0.268305 01.691116 553.878065 11.168606 31.355393 35.997818 2.867 053017 17099514971709951473.169495 0.884679 0.015014 0.003 0.001 0.000001 1.359370 5.130263 4.803933 1.873799 1.126271 0.268279 0.679690 552555.045862 11.216446 31.336403 3536.805174 3.684659 054018 17099514971709951474.824154 0.789793 0.014013 0.004003 0.001 0.001000 1.342343 5.153124 4.969980 1.838845 1.198195 0.277269 0.669686 552553.754584 11.168318 31.354364 35.906820 2.863865 055019 17099514981709951474.479810 0.699783 0.014 0.003004 0.001 0.001 1.379342 5.230148 4.929 1.879868 1.177203 0.276270 0.626695 551554.721589 11.107329 31.259414 3536.795334 3.709758 056020 17099514991709951475.133469 0.754759 0.013021 0.003010 0.001002 0.001 1.346384 5.166295 45.931002 1.846847 1.201193 0.276370 0.641680 553552.559323 1110.420995 31.302373 36.263190 43.534232 057021 17099514991709951476.795124 10.892789 0.014 0.004003 0.001 0.001 31.194334 5.144149 54.213863 31.070873 1.102159 0.313262 0.883694 552551.342174 11.358586 31.356429 3536.834093 3.947683 058022 17099515001709951476.455778 0.805678 0.013017 0.003 0.002001 0.001 1.321357 5.237206 4.941942 1.871861 1.189139 0.272263 0.689607 553552.453973 11.569487 31.407450 3536.818206 2.918861 059023 17099515011709951477.111434 0.843829 0.015014 0.003004 0.001 0.001 1.343348 5.250168 4.936786 1.879909 1.151113 0.264262 0.641706 555556.424439 11.339474 3231.551396 35.822960 35.683616 060024 17099515011709951478.775098 01.839973 0.013014 0.003 0.001 0.001 13.346199 5.242158 45.981316 13.867041 1.173199 0.274301 0.686712 553552.788752 11.635263 31.384464 35.812887 2.853860 061025 17099515021709951478.432758 0.756863 0.013014 0.003 0.001 0.001 1.360356 5.247180 4.971890 1.871876 1.191167 0.264269 0.652661 551554.001007 11.677324 31.382501 3635.142965 3.688695 062026 17099515031709951479.087415 0.831732 0.015014 0.004 0.001 0.001 1.340335 5.271185 4.916794 1.837839 1.315143 0.244273 0.620693 551556.165 11.508355 31.385592 35.914691 2.868909 063027 17099515031709951480.741073 0.812819 0.015014 0.003 0.002001 0.000001 1.352350 5.172151 4.941818 21.346882 1.167267 0.271249 0.684675 553554.406184 11.084207 31.473441 3537.871435 3.790780 064028 17099515041709951480.398732 0.852796 0.020021 0.010009 0.002 0.001 1.330341 5.253367 45.844031 1.855 1.074180 0.364367 0.710683 551552.497998 11.369480 31.465440 3536.813413 3.201181 065029 17099515051709951481.052389 0.858770 0.013014 0.003 0.001 0.001 1.350347 5.165169 4.972633 1.875886 1.328028 0.241263 0.646737 550551.681741 11.250409 31.382451 3536.781130 3.694669 066030 17099515051709951482.706044 0.758798 0.015014 0.003002 0.001 0.001 1.349347 5.157197 4.908551 1.836838 10.194988 0.274286 0.675742 551552.458585 11.241286 3231.197398 36.151214 2.866938 067031 17099515061709951482.361705 0.741780 0.014013 0.004003 0.001 0.001 1.340319 5.150225 4.874844 1.872896 1.164138 0.268273 0.691686 553551.973471 11.016422 31.438405 36.056418 3.660673 068032 17099515071709951483.018360 0.807744 0.015 0.003 0.001 0.001 1.329364 5.279174 45.790049 1.841848 1.065197 0.263271 0.660690 553555.581717 11.266567 31.376450 3536.834004 24.949597 069033 17099515071709951484.674024 01.787865 0.014 0.003 0.001 0.000001 13.334177 5.126151 45.892408 13.881073 1.157192 0.268302 0.637859 554551.552751 11.103420 31.501518 36.175076 3.676935 070034 17099515081709951484.331684 0.734763 0.014013 0.003 0.001 0.001 1.357366 5.187156 4.900923 1.847836 1.182180 0.290270 0.680654 552553.080017 11.193105 31.472419 3539.965409 2.924934 071035 17099515081709951485.986343 0.918744 0.014013 0.003007 0.001 0.001 1.399339 5.111207 4.948864 1.878884 1.180127 0.273268 0.678715 552.339273 11.160389 31.449421 3836.325091 53.545676 072036 17099515091709951485.648998 10.889783 0.016015 0.003002 0.001 0.001000 31.175353 5.211147 54.360905 31.035826 1.554190 0.392268 10.726676 552553.022218 11.255577 31.424411 36.042138 2.904858 073037 17099515101709951486.309654 0.870825 0.015014 0.003 0.001 0.001 1.353 5.160150 4.832901 1.892883 1.129196 0.276278 0.626677 551553.263971 11.202355 31.387408 36.047152 3.684771 074038 17099515101709951487.963312 0.827751 0.013023 0.003009 0.001002 0.001 1.338380 5.287332 45.996054 1.806862 1.233162 0.277368 0.686659 552.124360 11.490561 31.376421 35.786896 23.860161 075039 17099515111709951487.618967 0.840674 0.014013 0.003004 0.001 0.001000 1.317400 5.194185 4.713996 1.892900 1.059302 0.266254 0.633668 556551.157565 11.050267 31.419442 3536.863180 3.686667 076040 17099515121709951488.276622 0.793834 0.013014 0.002003 0.001 0.001 1.337353 5.218120 4.824946 1.847814 1.163219 0.273290 0.627681 553552.038905 11.441643 31.399436 3635.186873 2.922932 077041 17099515121709951489.932277 0.860678 0.013014 0.003 0.002001 0.001 1.341372 5.239282 4.809882 1.879873 1.132193 0.264274 0.633710 553552.340397 11.380231 31.325636 35.777957 3.681679 078042 17099515131709951489.588933 0.806833 0.013014 0.003 0.001 0.001 1.337350 5.150175 4.858920 1.859853 1.164181 0.271268 0.625666 552556.434858 11.174618 31.342441 3536.875003 2.861847 079043 17099515141709951490.242593 0.770840 0.014 0.003 0.001 0.001 1.328358 5.261231 4.904746 1.884896 1.201068 0.278262 0.675703 552555.390706 11.277318 31.314440 3635.119805 35.900592 080044 17099515141709951491.898256 01.819952 0.013014 0.003 0.001002 0.001005 13.318188 5.177193 45.884317 13.860031 1.179187 0.265306 0.685767 552.436667 11.178568 31.398327 3635.157856 32.146917 081045 17099515151709951491.553918 0.855880 0.020014 0.009004 0.002001 0.001 1.342378 5.287135 54.025664 1.875 1.196036 0.365266 0.692731 551.800976 11.389368 31.425469 36.143103 3.788685 082046 17099515161709951492.208573 0.848842 0.013014 0.003 0.001 0.001 1.342362 5.195220 4.701928 1.853821 1.053194 0.286281 0.722673 552553.828601 11.556359 31.430435 3635.204888 2.869857 083047 17099515161709951493.864229 0.887771 0.014013 0.003 0.001 0.001 1.360374 5.119173 4.796786 1.879860 01.997136 0.262269 0.692694 556552.199346 11.292322 31.449404 3635.011964 3.677676 084048 17099515171709951493.523884 0.803738 0.013015 0.003 0.001 0.001 1.320352 5.174152 4.836954 1.863839 1.142420 0.268273 0.697638 554552.992455 11.651343 31.435405 3635.045867 2.867919 085049 17099515181709951494.180539 0.872735 0.014 0.003 0.001 0.001 1.356350 5.264213 4.562919 1.873858 1.000203 0.256267 0.732684 552555.314511 11.364320 31.386425 36.011101 3.661734 086050 17099515181709951495.835199 0.752792 0.014021 0.003010 0.001002 0.000001 1.347350 5.221295 4.898967 1.843875 1.160159 0.260362 0.624695 552555.631633 11.247365 31.341427 35.831871 23.864206 087051 17099515191709951495.490858 0.789725 0.013014 0.003 0.002001 0.000001 1.338355 5.193179 4.737966 1.894855 1.049193 0.262267 0.695663 552.360571 11.667290 31.430442 36.007051 3.717706 088052 17099515201709951496.145513 0.778732 0.015014 0.003 0.001 0.001 1.374351 5.197148 4.914894 1.870835 1.151204 0.273268 0.693691 551553.128878 11.247168 31.365355 3635.042997 2.918867 089053 17099515201709951497.799169 0.780884 0.014015 0.003 0.001 0.001000 1.332359 5.203130 4.920803 1.879873 1.057126 0.261268 0.717679 552.486045 11.064216 31.368336 35.817805 3.692684 090054 17099515211709951497.454824 0.809789 0.014 0.003004 0.001 0.001 1.336342 5.174153 4.876969 1.850838 1.136198 0.291277 0.638669 552.596754 11.177168 31.420354 35.792906 42.503863 091055 17099515221709951498.114479 10.924699 0.014 0.003 0.001 0.001 31.204379 5.130230 54.308929 31.101879 1.187177 0.305276 10.203626 552551.445721 1511.121107 31.392259 3635.129795 3.931709 092056 17099515221709951499.779133 0.776754 0.014013 0.003 0.001 0.001 1.350346 5.167166 4.806931 1.852846 1.130201 0.266276 0.650641 552553.923559 11.227420 31.414302 3536.848263 24.917534 093057 17099515231709951499.434795 01.863892 0.015014 0.003004 0.001 0.001 13.354194 5.155144 45.900213 13.870070 1.303102 0.237313 0.600883 553552.494342 11.353358 31.414356 35.815834 3.677947 094058 17099515241709951500.090455 0.699805 0.015013 0.003 0.001002 0.001 1.344321 5.153237 4.925941 1.835871 1.213189 0.278272 0.668689 550553.517453 11.584569 31.425407 35.820818 2.866918 095059 17099515241709951501.744111 0.896843 0.013015 0.003 0.002001 0.001 1.376343 5.210250 54.044936 1.849879 1.206151 0.274264 0.690641 551555.603424 11.192339 3132.399551 3635.161822 3.706683 096060 17099515251709951501.399775 0.849839 0.013 0.003 0.001 0.001 1.333346 5.183242 4.539981 1.844867 01.974173 0.251274 0.723686 552553.818788 11.143635 31.449384 35.790812 2.860853 097061 17099515261709951502.054432 0.858756 0.014013 0.003 0.001 0.001 1.331360 5.178247 4.903971 1.882871 1.147191 0.266264 0.648652 552551.017001 11.579677 31.375382 3536.923142 3.667688 098062 17099515261709951503.709087 0.861831 0.013015 0.003004 0.001 0.001 1.337340 5.155271 4.961916 1.824837 1.199315 0.274244 0.676620 551.453165 11.124508 31.403385 35.890914 2.867868 099063 17099515271709951503.363741 0.871812 0.015 0.003 0.001002 0.001000 1.380352 5.244172 4.905941 12.872346 1.201167 0.279271 0.670684 551553.980406 11.592084 31.406473 3635.138871 3.706790 evt064 t00(sec)1709951504.398 0.852 ms: t010.020 0.010 t02 0.002 0.001 t03 1.330 t045.253 4.844 t05 1.855 1.074 t06 0.364 t07 0.710 551.497 11.369 t0831.465 35.813 t093.201 065 1709951505.052 t100.858 0.013 t11 0.003 0.001 t12 0.001 t131.350 5.165 t14 4.972 1.875 t15 1.328 t160.241 0.646 t17 median: 0.808550.681 11.250 31.382 35.781 3.694 066 1709951505.706 0.758 0.014015 0.003 0.001 0.001 1.352349 5.186157 4.907908 1.868836 1.180194 0.271274 0.686675 552551.710458 11.327 31.412 35.965 3.667 |
...
.241 32.197 36.151 2.866
067 1709951506.361 0.741 0.014 0.004 0.001 0.001 1.340 5.150 4.874 1.872 1.164 0.268 0.691 553.973 11.016 31.438 36.056 3.660
068 1709951507.018 0.807 0.015 0.003 0.001 0.001 1.329 5.279 4.790 1.841 1.065 0.263 0.660 553.581 11.266 31.376 35.834 2.949
069 1709951507.674 0.787 0.014 0.003 0.001 0.000 1.334 5.126 4.892 1.881 1.157 0.268 0.637 554.552 11.103 31.501 36.175 3.676
070 1709951508.331 0.734 0.014 0.003 0.001 0.001 1.357 5.187 4.900 1.847 1.182 0.290 0.680 552.080 11.193 31.472 35.965 2.924
071 1709951508.986 0.918 0.014 0.003 0.001 0.001 1.399 5.111 4.948 1.878 1.180 0.273 0.678 552.339 11.160 31.449 38.325 5.545
072 1709951509.648 1.889 0.016 0.003 0.001 0.001 3.175 5.211 5.360 3.035 1.554 0.392 1.726 552.022 11.255 31.424 36.042 2.904
073 1709951510.309 0.870 0.015 0.003 0.001 0.001 1.353 5.160 4.832 1.892 1.129 0.276 0.626 551.263 11.202 31.387 36.047 3.684
074 1709951510.963 0.827 0.013 0.003 0.001 0.001 1.338 5.287 4.996 1.806 1.233 0.277 0.686 552.124 11.490 31.376 35.786 2.860
075 1709951511.618 0.840 0.014 0.003 0.001 0.001 1.317 5.194 4.713 1.892 1.059 0.266 0.633 556.157 11.050 31.419 35.863 3.686
076 1709951512.276 0.793 0.013 0.002 0.001 0.001 1.337 5.218 4.824 1.847 1.163 0.273 0.627 553.038 11.441 31.399 36.186 2.922
077 1709951512.932 0.860 0.013 0.003 0.002 0.001 1.341 5.239 4.809 1.879 1.132 0.264 0.633 553.340 11.380 31.325 35.777 3.681
078 1709951513.588 0.806 0.013 0.003 0.001 0.001 1.337 5.150 4.858 1.859 1.164 0.271 0.625 552.434 11.174 31.342 35.875 2.861
079 1709951514.242 0.770 0.014 0.003 0.001 0.001 1.328 5.261 4.904 1.884 1.201 0.278 0.675 552.390 11.277 31.314 36.119 3.900
080 1709951514.898 0.819 0.013 0.003 0.001 0.001 1.318 5.177 4.884 1.860 1.179 0.265 0.685 552.436 11.178 31.398 36.157 3.146
081 1709951515.553 0.855 0.020 0.009 0.002 0.001 1.342 5.287 5.025 1.875 1.196 0.365 0.692 551.800 11.389 31.425 36.143 3.788
082 1709951516.208 0.848 0.013 0.003 0.001 0.001 1.342 5.195 4.701 1.853 1.053 0.286 0.722 552.828 11.556 31.430 36.204 2.869
083 1709951516.864 0.887 0.014 0.003 0.001 0.001 1.360 5.119 4.796 1.879 0.997 0.262 0.692 556.199 11.292 31.449 36.011 3.677
084 1709951517.523 0.803 0.013 0.003 0.001 0.001 1.320 5.174 4.836 1.863 1.142 0.268 0.697 554.992 11.651 31.435 36.045 2.867
085 1709951518.180 0.872 0.014 0.003 0.001 0.001 1.356 5.264 4.562 1.873 1.000 0.256 0.732 552.314 11.364 31.386 36.011 3.661
086 1709951518.835 0.752 0.014 0.003 0.001 0.000 1.347 5.221 4.898 1.843 1.160 0.260 0.624 552.631 11.247 31.341 35.831 2.864
087 1709951519.490 0.789 0.013 0.003 0.002 0.000 1.338 5.193 4.737 1.894 1.049 0.262 0.695 552.360 11.667 31.430 36.007 3.717
088 1709951520.145 0.778 0.015 0.003 0.001 0.001 1.374 5.197 4.914 1.870 1.151 0.273 0.693 551.128 11.247 31.365 36.042 2.918
089 1709951520.799 0.780 0.014 0.003 0.001 0.001 1.332 5.203 4.920 1.879 1.057 0.261 0.717 552.486 11.064 31.368 35.817 3.692
090 1709951521.454 0.809 0.014 0.003 0.001 0.001 1.336 5.174 4.876 1.850 1.136 0.291 0.638 552.596 11.177 31.420 35.792 4.503
091 1709951522.114 1.924 0.014 0.003 0.001 0.001 3.204 5.130 5.308 3.101 1.187 0.305 1.203 552.445 15.121 31.392 36.129 3.931
092 1709951522.779 0.776 0.014 0.003 0.001 0.001 1.350 5.167 4.806 1.852 1.130 0.266 0.650 552.923 11.227 31.414 35.848 2.917
093 1709951523.434 0.863 0.015 0.003 0.001 0.001 1.354 5.155 4.900 1.870 1.303 0.237 0.600 553.494 11.353 31.414 35.815 3.677
094 1709951524.090 0.699 0.015 0.003 0.001 0.001 1.344 5.153 4.925 1.835 1.213 0.278 0.668 550.517 11.584 31.425 35.820 2.866
095 1709951524.744 0.896 0.013 0.003 0.002 0.001 1.376 5.210 5.044 1.849 1.206 0.274 0.690 551.603 11.192 31.399 36.161 3.706
096 1709951525.399 0.849 0.013 0.003 0.001 0.001 1.333 5.183 4.539 1.844 0.974 0.251 0.723 552.818 11.143 31.449 35.790 2.860
097 1709951526.054 0.858 0.014 0.003 0.001 0.001 1.331 5.178 4.903 1.882 1.147 0.266 0.648 552.017 11.579 31.375 35.923 3.667
098 1709951526.709 0.861 0.013 0.003 0.001 0.001 1.337 5.155 4.961 1.824 1.199 0.274 0.676 551.453 11.124 31.403 35.890 2.867
099 1709951527.363 0.871 0.015 0.003 0.001 0.001 1.380 5.244 4.905 1.872 1.201 0.279 0.670 551.980 11.592 31.406 36.138 3.706
evt t00(sec) ms: t01 t02 t03 t04 t05 t06 t07 t08 t09 t10 t11 t12 t13 t14 t15 t16 t17
job #1 median: 0.808 0.014 0.003 0.001 0.001 1.352 5.186 4.907 1.868 1.180 0.271 0.686 552.710 11.327 31.412 35.965 3.667
other jobs
job #2 median: 0.820 0.013 0.003 0.001 0.001 1.324 5.312 4.984 1.796 1.146 0.231 0.681 551.307 11.269 31.356 35.863 2.910
job #3 median: 0.773 0.014 0.003 0.001 0.001 1.355 5.325 4.983 1.720 1.158 0.267 0.677 548.701 11.053 31.174 35.818 3.001
job #4 median: 0.664 0.015 0.003 0.002 0.001 1.408 5.339 5.003 1.726 1.194 0.272 0.685 548.775 11.112 31.168 35.857 3.215
job #5 median: 0.804 0.014 0.003 0.001 0.001 1.315 5.293 4.972 1.736 1.153 0.274 0.674 551.204 11.219 31.334 35.955 2.873 |
| Code Block | ||||
|---|---|---|---|---|
| ||||
ana-4.0.59-py3 [dubrovin@sdfmilan204:~/LCLS/con-py3]$ mpirun -n 1 python Detector/examples/test-scaling-mpi.py 7
rank:000 cpu_num:000 size:01
[I] L0398: test-scaling-mpi.py
====================== det.name: CxiDs1.0:Jungfrau.0
detname from source: CxiDs1.0:Jungfrau.0
calib_jungfrau arr shape:(8, 512, 1024) size:4194304 dtype:uint16 [2906 2945 2813 2861 3093...]
calib_jungfrau peds+off shape:(3, 8, 512, 1024) size:12582912 dtype:float32 [2922.283 2938.098 2827.207 2855.296 3080.415...]
calib_jungfrau gfac shape:(3, 8, 512, 1024) size:12582912 dtype:float32 [0.02490437 0.02543429 0.02541406 0.02539831 0.02544083...]
calib_jungfrau mask shape:(8, 512, 1024) size:4194304 dtype:uint8 [1 1 1 1 1...]
calib_jungfrau outa shape:(8, 512, 1024) size:4194304 dtype:float32 [0. 0. 0. 0. 0....]ndarray from tuple:
calib_jungfrau common mode parameters shape:(4,) size:4 dtype:int64 [ 7 7 200 10]
loop over segments: True
rank:000 cpu_num:000 nevt:0000 time:10.117775
[I] L0604: test-scaling-mpi.py rank:000 job 2-nd evt time:1710176925.177748 saveed in file: figs/mpi-job-2nd-evt-time.txt
rank:000 cpu_num:000 nevt:0010 time:0.586269
rank:000 cpu_num:000 nevt:0020 time:0.581187
rank:000 cpu_num:000 nevt:0030 time:0.579508
rank:000 cpu_num:000 nevt:0040 time:0.566666
rank:000 cpu_num:000 nevt:0050 time:0.580894
rank:000 cpu_num:000 nevt:0060 time:0.580464
rank:000 cpu_num:000 nevt:0070 time:0.579505
rank:000 cpu_num:000 nevt:0080 time:0.582394
rank:000 cpu_num:000 nevt:0090 time:0.579650
rank:000 cpu_num:000 nevt:0100 time:0.580779
[I] L0637: test-scaling-mpi.py Summary for rank:000 job 2-nd evt time:1710176925.177748 time total (sec):65.284561
rank:000 times: shape:(100,) size:100 dtype:float64
...
QStandardPaths: XDG_RUNTIME_DIR not set, defaulting to '/tmp/runtime-dubrovin'
hostname:sdfmilan204 rank:000 cpu:000 cmt:8p-v7 proc time (sec) mean: 0.5819 +/- 0.0051 rms: 0.0066 +/- 0.0036
plot_figs_v2
fnprefix: figs/fig-mpi-data-8p-v7-sdfmilan204-ncores01 title: sdfmilan204 rank 000 of 001 cpu_num 000
arrts[msec]: shape:(100, 18) size:1800 dtype:float64
...
000 1710176925.179 0.666 0.017 0.008 0.002 569.880 0.195 0.680 0.623 0.117 0.084 0.127 0.052 0.001 11.087 31.610 36.165 0.397
001 1710176925.835 0.638 0.018 0.003 0.002 571.524 0.194 0.707 0.637 0.119 0.083 0.132 0.054 0.001 11.149 31.636 35.907 0.396
002 1710176926.492 0.639 0.019 0.003 0.002 568.908 0.196 0.649 0.592 0.127 0.094 0.140 0.057 0.001 10.950 31.537 35.854 0.398
003 1710176927.147 0.633 0.019 0.003 0.002 568.563 0.201 0.649 0.601 0.117 0.083 0.130 0.056 0.001 10.769 31.551 36.225 0.401
004 1710176927.801 0.644 0.019 0.003 0.002 574.708 0.356 0.669 0.618 0.119 0.083 0.133 0.140 0.001 11.196 31.468 36.211 0.675
005 1710176928.465 1.722 0.018 0.003 0.001 568.464 0.195 0.674 0.592 0.117 0.083 0.132 0.056 0.001 11.315 31.503 35.888 0.401
006 1710176929.121 0.640 0.018 0.003 0.002 568.027 0.195 0.692 0.658 0.118 0.084 0.128 0.062 0.001 10.968 31.407 36.123 0.394
007 1710176929.775 0.638 0.023 0.012 0.002 569.959 0.192 0.603 0.678 0.118 0.083 0.130 0.056 0.001 10.919 31.392 35.939 0.395
008 1710176930.430 0.640 0.018 0.003 0.002 570.235 0.196 0.625 0.637 0.118 0.083 0.126 0.055 0.001 10.902 31.453 36.084 0.405
009 1710176931.086 0.637 0.018 0.003 0.001 572.507 0.353 0.658 0.603 0.118 0.084 0.133 0.140 0.002 11.013 31.432 37.417 0.677
010 1710176931.749 1.720 0.017 0.003 0.002 569.025 0.193 0.633 0.605 0.117 0.084 0.133 0.057 0.001 10.891 31.381 36.222 0.401
011 1710176932.405 0.635 0.017 0.003 0.001 567.340 0.194 0.634 0.612 0.125 0.083 0.127 0.056 0.001 11.194 31.417 35.845 0.399
012 1710176933.058 0.635 0.017 0.002 0.002 567.859 0.196 0.678 0.635 0.117 0.084 0.126 0.057 0.001 11.080 31.371 36.001 0.394
013 1710176933.712 0.636 0.024 0.013 0.002 566.197 0.194 0.657 0.603 0.117 0.083 0.133 0.058 0.001 11.233 31.371 36.247 0.403
014 1710176934.364 0.643 0.022 0.003 0.002 566.952 0.197 0.664 0.612 0.117 0.083 0.129 0.057 0.001 10.935 31.378 35.875 0.394
015 1710176935.016 0.635 0.018 0.003 0.002 569.362 0.510 0.626 0.861 0.127 0.083 0.154 0.136 0.001 11.003 31.729 36.294 0.671
016 1710176935.676 1.736 0.025 0.004 0.002 570.226 0.195 0.636 0.596 0.118 0.083 0.127 0.062 0.001 11.322 31.442 35.914 0.399
017 1710176936.337 0.637 0.018 0.002 0.002 567.089 0.192 0.650 0.592 0.300 0.083 0.129 0.057 0.001 11.218 31.405 35.914 0.393
018 1710176936.990 0.640 0.017 0.004 0.002 568.821 0.194 0.618 0.598 0.117 0.084 0.125 0.057 0.001 11.068 31.435 36.364 0.397
019 1710176937.645 0.642 0.017 0.003 0.002 567.523 0.196 0.665 0.636 0.119 0.083 0.127 0.057 0.001 11.117 31.384 35.998 0.395
020 1710176938.299 0.638 0.023 0.012 0.002 566.782 0.197 0.680 0.628 0.118 0.084 0.124 0.056 0.001 10.767 31.521 35.868 0.398
021 1710176938.951 0.636 0.018 0.003 0.001 565.609 0.193 0.610 0.607 0.118 0.084 0.126 0.057 0.002 11.339 31.319 36.009 0.394
022 1710176939.602 0.638 0.017 0.003 0.001 567.499 0.209 0.604 0.621 0.117 0.084 0.127 0.057 0.001 11.328 31.393 36.040 0.396
023 1710176940.256 0.635 0.019 0.004 0.002 573.105 0.353 0.679 0.609 0.118 0.091 0.127 0.141 0.001 11.096 31.372 36.344 0.681
024 1710176940.918 1.740 0.016 0.003 0.001 572.266 0.195 0.651 0.597 0.116 0.083 0.133 0.057 0.001 11.027 31.364 36.005 0.396
025 1710176941.577 0.631 0.018 0.003 0.001 567.538 0.192 0.631 0.591 0.125 0.084 0.125 0.057 0.001 11.003 31.337 35.894 0.396
026 1710176942.230 0.636 0.018 0.003 0.002 568.037 0.195 0.621 0.670 0.117 0.083 0.133 0.055 0.001 11.123 31.391 36.233 0.401
027 1710176942.884 0.636 0.018 0.003 0.001 567.989 0.193 0.635 0.677 0.127 0.084 0.126 0.056 0.001 10.962 31.390 35.895 0.398
028 1710176943.538 0.641 0.025 0.012 0.003 566.633 0.196 0.628 0.597 0.118 0.084 0.126 0.056 0.001 11.214 31.428 35.818 0.403
029 1710176944.190 0.635 0.017 0.003 0.002 565.870 0.192 0.648 0.610 0.117 0.089 0.124 0.057 0.001 11.144 31.362 35.860 0.395
030 1710176944.842 0.634 0.017 0.004 0.002 567.096 0.202 0.658 0.613 0.117 0.083 0.123 0.056 0.001 11.078 31.397 35.869 0.394
031 1710176945.494 0.629 0.018 0.003 0.001 566.641 0.195 0.649 0.607 0.118 0.083 0.133 0.057 0.001 11.119 31.346 36.274 0.401
032 1710176946.147 0.638 0.018 0.003 0.001 576.210 0.353 0.639 0.625 0.118 0.083 0.127 0.138 0.001 11.161 31.395 35.792 0.682
033 1710176946.812 1.721 0.018 0.003 0.002 567.965 0.192 0.614 0.622 0.117 0.083 0.131 0.057 0.001 11.197 31.366 35.955 0.394
034 1710176947.467 0.634 0.017 0.003 0.002 567.574 0.195 0.615 0.596 0.117 0.091 0.127 0.055 0.001 10.901 31.393 35.964 0.395
035 1710176948.120 0.635 0.017 0.003 0.001 540.194 0.195 0.662 0.509 0.100 0.070 0.107 0.050 0.001 10.365 28.301 34.414 0.394
036 1710176948.740 0.638 0.018 0.003 0.001 552.474 0.171 0.576 0.513 0.101 0.070 0.107 0.051 0.001 10.327 28.882 32.906 0.363
037 1710176949.372 0.607 0.017 0.003 0.002 547.162 0.194 0.664 0.616 0.118 0.083 0.136 0.065 0.001 10.803 31.229 35.949 0.402
038 1710176950.004 0.650 0.025 0.013 0.002 539.100 0.195 0.609 0.637 0.117 0.084 0.126 0.056 0.001 11.068 31.485 33.966 0.349
039 1710176950.627 0.606 0.016 0.002 0.002 554.036 0.169 0.542 0.518 0.105 0.071 0.120 0.065 0.001 10.410 29.793 32.797 0.351
040 1710176951.261 0.642 0.017 0.003 0.001 554.199 0.195 0.613 0.619 0.118 0.083 0.126 0.056 0.001 11.444 31.373 36.057 0.397
041 1710176951.902 0.631 0.024 0.003 0.002 570.335 0.194 0.641 0.595 0.118 0.083 0.135 0.057 0.001 10.968 31.413 35.979 0.393
042 1710176952.557 0.638 0.016 0.003 0.002 568.253 0.205 0.659 0.617 0.117 0.083 0.125 0.055 0.002 11.354 31.421 36.379 0.403
043 1710176953.212 0.636 0.017 0.003 0.002 570.688 0.349 0.694 0.612 0.127 0.084 0.125 0.137 0.001 10.998 31.364 35.868 0.677
044 1710176953.872 1.725 0.018 0.003 0.002 568.433 0.195 0.644 0.601 0.117 0.084 0.125 0.055 0.001 11.340 31.365 35.909 0.393
045 1710176954.528 0.631 0.018 0.003 0.001 566.346 0.194 0.655 0.607 0.117 0.083 0.128 0.061 0.001 11.143 31.315 35.884 0.393
046 1710176955.179 0.638 0.017 0.003 0.002 566.295 0.196 0.653 0.603 0.118 0.084 0.124 0.056 0.001 11.112 31.403 36.420 0.394
047 1710176955.832 0.632 0.017 0.004 0.002 565.378 0.193 0.613 0.597 0.117 0.088 0.128 0.056 0.001 11.151 31.322 36.042 0.398
048 1710176956.483 0.637 0.017 0.003 0.001 566.095 0.196 0.617 0.639 0.116 0.084 0.125 0.056 0.001 11.119 31.394 35.955 0.397
049 1710176957.135 0.633 0.018 0.003 0.002 567.317 0.193 0.696 0.609 0.118 0.089 0.128 0.057 0.001 11.030 31.297 35.939 0.395
050 1710176957.788 0.638 0.024 0.013 0.002 570.480 0.194 0.650 0.613 0.124 0.083 0.126 0.056 0.001 11.094 31.349 36.069 0.401
051 1710176958.444 0.634 0.017 0.003 0.001 567.719 0.195 0.686 0.591 0.117 0.083 0.132 0.057 0.001 11.028 31.395 35.928 0.394
052 1710176959.097 0.643 0.017 0.003 0.002 566.287 0.201 0.640 0.597 0.118 0.084 0.126 0.057 0.001 10.899 31.312 35.987 0.405
053 1710176959.749 0.637 0.017 0.003 0.002 567.780 0.196 0.647 0.611 0.118 0.084 0.131 0.057 0.002 10.977 31.435 35.997 0.395
054 1710176960.402 0.642 0.018 0.003 0.002 566.576 0.196 0.659 0.598 0.118 0.083 0.125 0.063 0.001 10.988 31.387 35.980 0.394
055 1710176961.055 0.627 0.017 0.003 0.002 566.366 0.194 0.659 0.613 0.118 0.084 0.135 0.056 0.001 10.914 31.310 36.169 0.396
056 1710176961.707 0.636 0.019 0.003 0.001 569.009 0.503 0.641 0.903 0.118 0.091 0.156 0.135 0.001 11.073 31.615 36.288 0.661
057 1710176962.366 1.732 0.019 0.002 0.002 570.110 0.196 0.658 0.647 0.118 0.083 0.134 0.057 0.001 11.127 35.682 35.929 0.401
058 1710176963.027 0.644 0.018 0.003 0.002 567.595 0.194 0.651 0.635 0.117 0.083 0.127 0.056 0.001 11.326 31.369 36.038 0.396
059 1710176963.681 0.628 0.019 0.002 0.002 566.851 0.194 0.634 0.625 0.118 0.084 0.128 0.055 0.001 11.123 31.468 35.948 0.393
060 1710176964.333 0.642 0.017 0.003 0.002 566.545 0.205 0.656 0.594 0.117 0.083 0.129 0.055 0.001 11.380 31.452 36.488 0.396
061 1710176964.987 0.630 0.018 0.003 0.002 567.141 0.231 0.631 0.615 0.117 0.084 0.125 0.055 0.001 11.449 31.284 35.898 0.393
062 1710176965.640 0.635 0.017 0.003 0.001 566.420 0.194 0.602 0.623 0.118 0.083 0.121 0.057 0.006 11.244 31.312 36.203 0.401
063 1710176966.292 0.636 0.018 0.003 0.001 566.424 0.196 0.646 0.604 0.119 0.084 0.127 0.056 0.001 10.810 31.245 36.966 0.411
064 1710176966.945 0.630 0.024 0.013 0.002 566.426 0.194 0.675 0.612 0.118 0.094 0.128 0.057 0.001 11.130 31.371 36.999 0.401
065 1710176967.598 0.634 0.018 0.003 0.002 569.700 0.204 0.633 0.603 0.117 0.083 0.125 0.056 0.002 11.036 31.356 36.108 0.394
066 1710176968.253 0.640 0.018 0.003 0.002 567.129 0.195 0.607 0.688 0.124 0.084 0.124 0.056 0.001 11.009 31.371 36.027 0.401
067 1710176968.906 0.633 0.018 0.003 0.002 565.612 0.203 0.671 0.599 0.116 0.083 0.125 0.056 0.001 10.705 31.379 36.182 0.396
068 1710176969.557 0.642 0.017 0.003 0.001 568.389 0.201 0.625 0.654 0.119 0.084 0.131 0.057 0.001 11.058 31.396 36.111 0.395
069 1710176970.212 0.628 0.018 0.002 0.002 566.199 0.195 0.646 0.595 0.117 0.083 0.126 0.061 0.001 10.833 31.327 36.125 0.395
070 1710176970.863 0.642 0.017 0.003 0.002 566.881 0.197 0.664 0.591 0.117 0.090 0.124 0.057 0.001 10.981 31.328 35.953 0.392
071 1710176971.516 0.628 0.017 0.003 0.002 566.241 0.194 0.659 0.633 0.118 0.084 0.123 0.062 0.001 10.926 31.365 36.098 0.394
072 1710176972.167 0.639 0.017 0.003 0.002 569.547 0.356 0.654 0.613 0.118 0.084 0.131 0.141 0.001 11.005 31.306 35.999 0.685
073 1710176972.826 1.742 0.017 0.003 0.001 568.023 0.194 0.629 0.602 0.117 0.084 0.123 0.056 0.002 10.927 31.313 35.976 0.394
074 1710176973.480 0.641 0.017 0.003 0.001 569.769 0.195 0.636 0.678 0.117 0.083 0.124 0.061 0.001 11.282 31.428 35.903 0.399
075 1710176974.136 0.636 0.017 0.003 0.002 566.796 0.194 0.653 0.627 0.117 0.084 0.124 0.073 0.002 10.784 31.387 36.141 0.393
076 1710176974.788 0.641 0.018 0.003 0.002 566.806 0.196 0.656 0.595 0.118 0.083 0.131 0.056 0.001 11.171 31.349 35.968 0.396
077 1710176975.441 0.630 0.023 0.003 0.002 567.456 0.194 0.654 0.627 0.125 0.085 0.126 0.056 0.001 11.124 31.371 36.198 0.397
078 1710176976.094 0.643 0.017 0.003 0.002 566.938 0.195 0.613 0.596 0.117 0.083 0.124 0.056 0.001 10.970 31.337 36.105 0.393
079 1710176976.747 0.631 0.017 0.003 0.002 568.831 0.194 0.609 0.648 0.118 0.083 0.125 0.055 0.001 11.077 31.336 35.977 0.397
080 1710176977.401 0.638 0.018 0.003 0.001 564.980 0.195 0.685 0.601 0.117 0.084 0.122 0.063 0.001 10.931 31.312 36.033 0.396
081 1710176978.052 0.630 0.018 0.003 0.002 566.748 0.194 0.676 0.622 0.118 0.083 0.125 0.057 0.002 11.122 31.409 36.197 0.394
082 1710176978.704 0.641 0.026 0.012 0.002 566.579 0.197 0.665 0.650 0.118 0.083 0.125 0.061 0.001 11.342 31.363 40.708 0.396
083 1710176979.362 0.630 0.017 0.003 0.002 567.053 0.203 0.644 0.600 0.118 0.083 0.126 0.056 0.001 10.994 31.350 36.342 0.396
084 1710176980.014 0.648 0.018 0.003 0.001 567.508 0.201 0.628 0.676 0.116 0.083 0.126 0.055 0.001 11.326 31.353 35.999 0.395
085 1710176980.668 0.633 0.016 0.003 0.002 566.638 0.194 0.640 0.597 0.118 0.083 0.127 0.054 0.001 11.118 31.345 35.914 0.400
086 1710176981.320 0.646 0.017 0.003 0.002 566.983 0.195 0.702 0.590 0.121 0.087 0.124 0.056 0.001 11.220 31.319 35.979 0.398
087 1710176981.973 0.634 0.017 0.003 0.001 567.545 0.195 0.602 0.595 0.118 0.084 0.124 0.057 0.001 11.417 31.365 36.243 0.405
088 1710176982.626 0.640 0.017 0.003 0.002 565.968 0.195 0.637 0.599 0.119 0.083 0.123 0.056 0.001 10.989 31.346 35.936 0.401
089 1710176983.277 0.628 0.015 0.003 0.002 566.326 0.196 0.645 0.602 0.118 0.083 0.126 0.057 0.001 10.849 31.537 36.117 0.393
090 1710176983.929 0.637 0.016 0.003 0.002 569.852 0.194 0.683 0.611 0.117 0.084 0.126 0.055 0.001 10.798 31.367 35.928 0.401
091 1710176984.584 0.631 0.017 0.002 0.001 571.955 0.352 0.689 0.596 0.117 0.084 0.131 0.139 0.001 11.187 31.335 36.047 0.682
092 1710176985.244 1.727 0.016 0.004 0.001 566.733 0.194 0.612 0.616 0.117 0.090 0.124 0.055 0.001 10.952 31.308 36.219 0.400
093 1710176985.898 0.628 0.018 0.004 0.002 566.060 0.200 0.602 0.627 0.117 0.083 0.125 0.056 0.002 11.088 32.352 36.037 0.394
094 1710176986.550 0.635 0.017 0.003 0.002 566.814 0.195 0.640 0.622 0.117 0.083 0.123 0.056 0.001 11.324 31.351 36.345 0.393
095 1710176987.202 0.629 0.017 0.003 0.002 566.586 0.194 0.652 0.672 0.117 0.083 0.131 0.057 0.001 10.958 31.435 36.180 0.401
096 1710176987.854 0.644 0.017 0.003 0.002 566.027 0.194 0.669 0.596 0.117 0.083 0.126 0.056 0.001 10.909 31.394 36.098 0.399
097 1710176988.506 0.636 0.017 0.003 0.001 566.741 0.192 0.636 0.651 0.118 0.084 0.125 0.056 0.002 11.258 31.340 35.949 0.392
098 1710176989.158 0.640 0.017 0.003 0.002 569.880 0.201 0.642 0.664 0.117 0.083 0.123 0.055 0.001 10.825 31.324 35.999 0.404
099 1710176989.813 0.628 0.016 0.003 0.001 566.983 0.202 0.645 0.596 0.118 0.083 0.124 0.056 0.001 11.322 31.347 36.264 0.399
evt t00(sec) ms: t01 t02 t03 t04 t05 t06 t07 t08 t09 t10 t11 t12 t13 t14 t15 t16 t17
job #1 median: 0.637 0.018 0.003 0.002 567.135 0.195 0.646 0.611 0.118 0.083 0.126 0.056 0.001 11.075 31.371 36.003 0.396 other
other jobs
job #2 median: 0.628 0.017 0.003 0.001 566.815 0.194 0.638 0.639 0.117 0.084 0.128 0.055 0.001 11.071 31.411 36.020 0.391
job #3 median: 0.628 0.016 0.003 0.001 567.021 0.194 0.689 0.601 0.118 0.084 0.125 0.055 0.001 11.060 31.398 35.948 0.395
job #4 median: 0.638 0.016 0.003 0.002 567.018 0.195 0.627 0.629 0.117 0.083 0.127 0.057 0.001 11.080 31.404 35.974 0.396
job #5 median: 0.805 0.014 0.003 0.001 567.475 0.212 0.654 0.624 0.120 0.085 0.125 0.057 0.001 11.093 31.389 35.989 0.400 |
Summary
| w/o loops over panels | with loops over panels | |||||
|---|---|---|---|---|---|---|
| dt | meaning | array size in (512,1024) | msec | array size in (512,1024) | msec | comment |
| 01 | get det.raw(evt) | 8 | 0.664 | 8 | 0.638 | |
| 02 | get detector name from source | 0.015 | 0.016 | |||
| 03 | access cache object for detname | 0.003 | 0.003 | |||
| 04 | get peds, gfac, mask, out, cmps | 3x8 | 0.002 | 3x8 | 0.002 | |
| 05 | single panel begin processing | begin processing entire array | 0.001 | begin processing last panel | 567 | meaningless |
| 06 | make gain range indices, gr0, 1, 2 | 8 | 1.408 | 1 | 0.195 | |
| 07 | np.select for gain factor | 8 | 5.34 | 1 | 0.627 | |
| 08 | np.select for gain peds+offset | 8 | 5.00 | 1 | 0.629 | |
| 09 | apply mask for data bits, arr & MSK | 8 | 1.73 | 1 | 0.117 | |
| 10 | subtract pedestals | 8 | 1.19 | 1 | 0.083 | |
| 11 | massaging common mode parameters | 0.272 | 0.127 | |||
| 12 | apply pixel mask for gr0 | 8 | 0.674 | 1 | 0.057 | |
| 13 | begin loop over panels for CMC | time to work with last panel | 549 | time to work with current panel | 0.001 | meaningless |
| 14 | CMC in banks: (512/2,1024/16) = (256,64) | 1 | 11.2 | 1 | 11.1 | CMC always loop over panels |
| 15 | CMC in rows per bank: 1024/16 = 64 pixels | 1 | 31.3 | 1 | 31.4 | |
| 16 | CMC in cols per bank: 512/2 = 256 pixels | 1 | 35.9 | 1 | 36.0 | |
| 17 | Apply gain correction and mask | 8 | 2.87 | 1 | 0.40 | |
| Total time per event | 8 | 576 | 8 | 582 | ||
- where CMC stands for common mode correction
References
- Scaling behavior of psana1 - Part 1 - det.calib method in multicore processing with mpi
- Scaling behavior of psana1 - Part 2 - test with command perf stat
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