[Numpy-discussion] memory usage question
Eric Firing
efiring@hawaii....
Sun Jun 6 20:00:16 CDT 2010
On 06/06/2010 02:17 PM, Tom Kuiper wrote:
> Greetings all.
>
> I have a feeling that, coming at this with a background in FORTRAN and
> C, I'm missing some subtlety, possibly of an OO nature. Basically, I'm
> looping over very large data arrays and memory usage just keeps growing
> even though I re-use the arrays. Below is a stripped down version of
> what I'm doing. You'll recognize it as gulping a great quantity of data
> (1 million complex samples), Fourier transforming these by 1000 sample
> blocks into spectra, co-adding the spectra, and doing this 255 times,
> for a grand 1000 point total spectrum. At iteration 108 of the outer
> loop, I get a memory error. By then, according to 'top', ipython (or
> python) is using around 85% of 3.5 GB of memory.
>
> P = zeros(fft_size)
> nsecs = 255
> fft_size = 1000
> for i in range(nsecs):
> header,data = get_raw_record(fd_in)
> num_bytes = len(data)
> label, reclen, recver, softver, spcid, vsrid, schanid,
> bits_per_sample, \
> ksamps_per_sec, sdplr, prdx_dss_id, prdx_sc_id, prdx_pass_num, \
> prdx_uplink_band,prdx_downlink_band, trk_mode, uplink_dss_id,
> ddc_lo, \
> rf_to_if_lo, data_error, year, doy, sec, data_time_offset, frov,
> fro, \
> frr, sfro,rf_freq, schan_accum_phase, (scpp0,scpp1,scpp2,scpp3), \
> schan_label = header
> # ksamp_per_sec = 1e3, number of complex samples in 'data' = 1e6
> num_32bit_words = len(data)*8/BITS_PER_32BIT_WORD
> cmplx_samp_per_word = (BITS_PER_32BIT_WORD/(2*bits_per_sample))
> cmplx_samples =
> unpack_vdr_data(num_32bit_words,cmplx_samp_per_word,data)
> del(data) # This makes no difference
> for j in range(0,ksamps_per_sec*1000/fft_size):
> index = int(j*fft_size)
> S = fft(cmplx_samples[index:index+fft_size])
> P += S*conjugate(S)
> del(cmplx_samples) # This makes no difference
> if (i % 20) == 0:
> gc.collect(0) # This makes no difference
> P /= nsecs
> sample_period = 1./ksamps_per_sec # kHz
> f = fftfreq(fft_size, d=sample_period)
>
> What am I missing?
I don't know, but I would suggest that you strip the example down
further: instead of reading data from a file, use numpy.random.randn to
generate fake data as needed. In other words, use only numpy
functions--no readers, no unpackers. Put this minimal script into a
file and run it from the command line, not in ipython. (Have you
verified that you get the same result running a standalone script from
the command line as running from ipython?) Put a memory-monitoring step
inside, maybe at each outer loop iteration. You can use the
matplotlib.cbook.report_memory function or similar:
def report_memory(i=0): # argument may go away
'return the memory consumed by process'
from subprocess import Popen, PIPE
pid = os.getpid()
if sys.platform=='sunos5':
a2 = Popen('ps -p %d -o osz' % pid, shell=True,
stdout=PIPE).stdout.readlines()
mem = int(a2[-1].strip())
elif sys.platform.startswith('linux'):
a2 = Popen('ps -p %d -o rss,sz' % pid, shell=True,
stdout=PIPE).stdout.readlines()
mem = int(a2[1].split()[1])
elif sys.platform.startswith('darwin'):
a2 = Popen('ps -p %d -o rss,vsz' % pid, shell=True,
stdout=PIPE).stdout.readlines()
mem = int(a2[1].split()[0])
return mem
I'm suspecting the problem may be in your data reader and/or unpacker,
not in the application of numpy functions. Also, ipython can confuse
the issue by keeping references to objects. In any case, with a simpler
test script and regular memory monitoring, it should be easier for you
to track down the problem.
Eric
>
> Best regards
>
> Tom
>
> p.s. Many of you will see this twice, for which I apologize.
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