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Re: GNURadio Leaking memory during repeated simulations


From: Geof Nieboer
Subject: Re: GNURadio Leaking memory during repeated simulations
Date: Thu, 13 Feb 2020 22:10:09 -0500

Roman,

I was able to run your code, and got a consistent 150-160 it/s through the whole run, with about 65MB of memory in use (as reporting by Task Manager).  This was on Windows 10 running GR 3.8.0.0.

I noticed there was another package installed (tqdm) that's not part of the GR install.  So I want to confirm... did you add that package to the python that installed as part of GR, or are you using a different python?

If you are using a different python install, a possible explanation is compilers; the GR packages and binaries are all built with VS 2015, but the py2.7 standard is VS 2008 (which won't build GR any more), and mixing (VS) compilers can cause odd issues.  So for GR, the entire python 2.7 and every package is built from scratch w/ 2015.  tqdm doesn't seem to be affected, I added that to the GR python install using a pip install, nothing fancy, and it seems to have worked.

If you are, however, running the script with the GR-installed python after opening with run_gr.bat, then I'm scratching my head what's happening.

Geof

On Thu, Feb 13, 2020 at 12:00 PM Roman A Sandler <address@hidden> wrote:
Hi Marcus,

Thanks for the reply!

My GNURadio version: 3.8.0.0 (Python 2.7.10)
It is the Windows 3.8.0.0 version downloaded from: http://www.gcndevelopment.com/gnuradio/downloads.htm

Complete reproducible code is below. I use the tqdm package to monitor iterations per second. On my PC, the it/sec declines very precipitously (starts at 85it/sec, then down to 22it/s after 40s and keeps dropping. Eventually as low as 1 it/sec).


```
import numpy as np
from gnuradio import gr, gr_unittest
from gnuradio import blocks
from gnuradio import digital
from gnuradio.filter import firdes
from gnuradio import channels
from tqdm import tqdm


def decode(channel_sigs):
tb = gr.top_block()

##################################################
# Variables
##################################################
nfilts = 32
sps = 4
timing_loop_bw = 3 * 6.28 / 100.0 # NOTE: this controls convergence speed!
constellation_scheme = digital.constellation_8psk().base()
rrc_taps = firdes.root_raised_cosine(nfilts, nfilts, 1.0 / float(sps), 0.35, 11 * sps * nfilts)

#### Actual blocks
channel_src = blocks.vector_source_c(channel_sigs, False)
digital_pfb_clock_sync = digital.pfb_clock_sync_ccf(sps, timing_loop_bw, rrc_taps, nfilts,
nfilts / 2, 1.5, 1)
constellation_soft_decoder = digital.constellation_soft_decoder_cf(constellation_scheme)
binary_slicer = digital.binary_slicer_fb()
blocks_char_to_float = blocks.char_to_float(1, 1)
recovered_bits_dst = blocks.vector_sink_f()

##################################################
# Connections
##################################################
tb.connect((channel_src, 0), (digital_pfb_clock_sync, 0))
tb.connect((digital_pfb_clock_sync, 0), (constellation_soft_decoder, 0))
tb.connect((constellation_soft_decoder, 0), (binary_slicer, 0))
tb.connect((binary_slicer, 0), (blocks_char_to_float, 0))
tb.connect((blocks_char_to_float, 0), (recovered_bits_dst, 0))

tb.run()
recovered_bits = np.array(recovered_bits_dst.data())

return recovered_bits


if __name__ == '__main__':
n_trls = 10000
n_samples = 9999
sig = np.random.normal(size=(n_samples,)) + 1j * np.random.normal(size=(n_samples,))

for n in tqdm(range(n_trls)):
decode(sig)
```


On Thu, Feb 13, 2020 at 2:11 AM Müller, Marcus (CEL) <address@hidden> wrote:
Hi,

huh. That looks hard to debug; also, the slow down is suspicious (as
long as there's memory available, it shouldn't take significantly
longer to get some – usually, memory fragmentation isn't *that* bad,
and this shouldn't be doing *that* much memory allocation).

Could you put all your code in one .py file (or a set of these) that
one can simply execute right away? That would allow us to reproduce.
Also, could you tell us your specific GNU Radio version (all four
digits of it?).

Best regards,
Marcus

On Tue, 2020-02-11 at 16:34 -0800, Roman A Sandler wrote:
> Hi,
>
> I am using GNURadio to decode a large amount of 1024-sample complex
> vectors of different modulation schemes. Thus, I have a for loop
> which runs gr.top_block.run() at each iteration and uses a vector
> sink to collect the results. The issue is that as the simulation
> keeps going, each itertion takes longer (e.g. starts of at 120it/sec,
> and then after 5000 iterations slows down to 10it/sec). I can see in
> task manager (I am on windows) that memory is increasing so clearly
> there is a memory leak where somehow the results of the iterations
> arent being deleted.
>
> Is there an explicit way to delete runs or is this a bug?
>
> CODE:
>
> calling code:
> ```
> for _ in range(10000):
>     decode(sig)
> ```
>
> decode func:
> ```
> def decode(channel_sigs):
>     tb = gr.top_block()
>
>     ##################################################
>     # Variables
>     ##################################################
>     nfilts = 32
>     sps = 4
>     timing_loop_bw = 3 * 6.28 / 100.0  # NOTE: this controls
> convergence speed!
>     constellation_scheme, bps = get_constellation_scheme(scheme)
>     rrc_taps = firdes.root_raised_cosine(nfilts, nfilts, 1.0 /
> float(sps), 0.35, 11 * sps * nfilts)
>     phase_bw = 6.28 / 100.0
>     eq_gain = 0.01
>     arity = 4
>
>     #### Actual blocks
>     channel_src = blocks.vector_source_c(channel_sigs, False)
>     digital_pfb_clock_sync = digital.pfb_clock_sync_ccf(sps,
> timing_loop_bw, rrc_taps, nfilts,
>                                                         nfilts / 2,
> 1.5, 1)
>     constellation_soft_decoder =
> digital.constellation_soft_decoder_cf(constellation_scheme)
>     binary_slicer = digital.binary_slicer_fb()
>     blocks_char_to_float = blocks.char_to_float(1, 1)
>     recovered_bits_dst = blocks.vector_sink_f()
>
>     ##################################################
>     # Connections
>     ##################################################
>     tb.connect((channel_src, 0), (digital_pfb_clock_sync, 0))
>     tb.connect((digital_pfb_clock_sync, 0),
> (constellation_soft_decoder, 0))
>     tb.connect((constellation_soft_decoder, 0), (binary_slicer, 0))
>     tb.connect((binary_slicer, 0), (blocks_char_to_float, 0))
>     tb.connect((blocks_char_to_float, 0), (recovered_bits_dst, 0))
>
>     tb.run()
>
>     recovered_bits = np.array(recovered_bits_dst.data())
>     return recovered_bits
> ```

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