how to use ddr bandwidth efficiently on xavier platform
it’s been my experience that the cpu alone cannot achieve the rated ddr bandwidth on the agx xavier; presumably you need to use both the gpu and the cpu to saturate the bandwidth.

perhaps the gpu alone could do this, too, with the right workload? i haven’t checked.
if you do a web search for flex sdr-1000 you will find alot of info.
http://kc.flexradio.com/knowledgebasearticle50108.aspx

i am pretty sure you are going to need a pc with a parallel port, once upon a time there was a usb adapter available that worked for this radio, the driver is available but you will most likely not find the adapter.
i had to run windows 7 32 bit to get mine to work.
i used the version of powersdr from the ke9ns website called excalibur, if you can't find it i have it backed up on my nas. A couple of notes to get started:



> 
> .apply(literal\_eval)
> 
> 
> 


Wouldn't this be better as `map`?



> 
> I then re-create the Dask DF:
> 
> 
> ddf = dd.from\_pandas(ddf\_out, npartitions=nCores)
> 
> 
> 


`ddf_out` was already a dask dataframe, I don't know why you had to do this.



> 
> The columns in result seem to match those in metadir.
> 
> 
> 


The value of `result.columns` is taken from the meta you provided, no computing happens until you ask for it (dask is lazy in most operations). Does the ValueError exception not provide further information?


Here is a full example



```
x = ({'F9_07_PZ_COMP_DIRECT': '0',
  'F9_07_PZ_DIRTRSTKEY_NAME': 'DEBRA MEALY',
  'F9_07_PZ_COMP_OTHER': '0',
  'F9_07_PZ_COMP_RELATED': '0',
  'F9_07_PZ_TITLE': 'CHAIR PERSON',
  'F9_07_PZ_AVE_HOURS_WEEK': '1.00',
  'F9_07_PC_TRUSTEE_INDIVIDUAL': 'X'},
 {'F9_07_PZ_COMP_DIRECT': '0',
  'F9_07_PZ_DIRTRSTKEY_NAME': 'HELEN GORDON',
  'F9_07_PZ_COMP_OTHER': '0',
  'F9_07_PZ_COMP_RELATED': '0',
  'F9_07_PZ_TITLE': 'VICE CHAIR',
  'F9_07_PZ_AVE_HOURS_WEEK': '1.00',
  'F9_07_PC_TRUSTEE_INDIVIDUAL': 'X'})
df = pd.DataFrame({'a': x})
d = dd.from_pandas(df, 1)
meta = pd.DataFrame(columns=['F9_07_PZ_COMP_DIRECT', 
       'F9_07_PZ_DIRTRSTKEY_NAME',
       'F9_07_PZ_COMP_OTHER', 'F9_07_PZ_COMP_RELATED', 'F9_07_PZ_TITLE',
       'F9_07_PZ_AVE_HOURS_WEEK', 'F9_07_PC_TRUSTEE_INDIVIDUAL'], dtype="O")
d.map_partitions(lambda df: df.a.apply(pd.Series), meta=meta).compute()

```

How did I know what `meta` to use? I applied the function to the pandas dataframe - you might use a small piece of the dataframe to do this.


Some additional notes:


* it is an anti-pattern to load the data with pandas, pass to dask workers and then gather the whole result back to a pandas (in memory) dataframe, you are very unlikely to see a speedup this way and may incur a lot of overhead. You are better off loading with something like `dd.read_csv`, and aggregating or writing with dask functions too. Only `compute()` on something that will be small or not return anything (because it involves writing the output). The official examples do not use from\_pandas.
* string and dict processing are python methods, and so hold the interpreter lock (GIL) of any python function: threads will not actually run in parallel. To get parallelism, you need to run in processes, which is most easily achieved using <https://docs.dask.org/en/latest/setup/single-distributed.html>
* the distributed scheduler also gives you access to the dashboard, which has a lot of useful information to diagnose how your system is running. You can also configure a lot about its behaviour, in case you have firewall rules you need to obey. your information is incomplete. you need to show the smartsdr cat configuration too.
try changing flowcontrol to none in vqlog you could use kangaroo, but i don’t think it’s needed here.

instead i’d use inverse distance weighting to make a height field like this.

[sharp\_curves.gh](/uploads/short-url/a5wdkm9xiiwabwl6s0jpgf6ry7j.gh) (11.4 kb)

[[image]](https://aws1.discourse-cdn.com/mcneel/uploads/default/original/3x/d/c/dc85a5f42096a341409c4b4727a7d5066bd68745.png "image.png")

the key step here is making a mesh with vertices on the curves you want to keep sharp. one way of doing this is to split a s… stick with the 2500k but still use your lower end mid-range 5770. the gaming performance is not any better when one looks at them side by side http://www.anandtech.com/bench/product/288?vs=287 . when only 100mhz separates the two overclocking the 2500k makes up the negligible difference and can then be pushed up 1ghz more just on air. continue saving for a better card. the 6870 is still settling for mid range located smack dab in the center of mediocreville. wait for a 670 or 680 keplar series gpu's from nvidia or invest in the 7970. any of those cards with 3gb of vram is going to add a huge future proof cushion. buy a z68 http://www.techpowerup.com/161073/msi-s-entire-z68-(g3)-h61-(g3)-series-motherboard-supports-intel-s-22-nm-processors.html or http://www.gigabyte.com/press-center/news-page.aspx?nid=1048 which will support their newer pci 3.0 system requirements.after you have your next generation keplar or southern islands video cards like the 7970 it's time to save up for the ivy bridge upgrade along with the necessary bios revision. you are going to be saving $100 off the top by going for a 2500k instead of the 2600k that you can immediately apply to your stash for your better gfx card in the future. i would skip right over the 6970's, 580's and 90's and well anything except the newest cards. while your saving it also gives the newly released cards or processors time to show any bugs like sandy had. if you are thinking about overclocking like a science lab it is simply

enter bios via delete, f1 or whatever your default key is to enter "setup"/bios, click huge button that says overclocking, change the multiplier from 33 to 44 and then f10 to save and exit. got jumper cables ? ... know how to use them correctly? hi @

yes. this technology sends power and data over just 2 wires.

(despite having the name 1 wire).

<https://www.maximintegrated.com/en/design/technical-documents/tutorials/1/1796.html>

rv mineirin it looks like your intent is to use host networking, can you add

network\_mode: host

also you can try netstat -tupln | grep 32400

to see if you are listening on 32400 for plex @pankajkumardas, 

converting geophysical data from the space (or time) domain to the frequency domain (via fast fourier transform) is common practice in geophysics, particularly when wanting to apply some sort of filter to that data. 

we do this because it is more computationally efficient to filter data in the frequency domain compared to the space domain. for example, applying a filter such as a first vertical derivative to a grid of magnetic data in the space domain requires convolution using a two-dimensional filter. depending on the type of filter, that process can be complex and therefore computationally expensive. 

transforming the data and the filter to the frequency domain allows us to simply multiply the two to get a filtered spectrum, which is then transformed back to a filtered grid in the space domain. this process is far more computationally efficient than convolution.

the following diagram describes the distinction between space and frequency domain filtering. it is taken from reeves (2005), which you can access here: http://www.geosoft.com/media/uploads/resources/technical-papers/aeromagnetic\_survey\_reeves.pdf




i'd urge you to download the document and review chapter 6, from which the diagram is taken.

best regards, Dapper just wraps raw ADO.NET; raw ADO.NET doesn't offer a facility for this, therefore dapper does not. What you want is `SqlBulkCopy`. You could *also* use a table-valued-parameter, but this really feels like a `SqlBulkCopy` job.


In a pinch, you can *use* dapper here - `Execute` will unroll an `IEnumerable<T>` into a series of commands about `T` - but it will be lots of commands; and unless you explicitly enable async-pipelining, it will suffer from latency per-command (the pipelined mode avoids this, but it will still be n commands). But `SqlBulkCopy` will be *much* more efficient.


If the input data is an `IEnumerable<T>`, you might want to use `ObjectReader` from FastMember; for example:



```
IEnumerable<SomeType> data = ...
using(var bcp = new SqlBulkCopy(connection))
using(var reader = ObjectReader.Create(data, "Id", "Name", "Description"))
{
    bcp.DestinationTableName = "SomeTable";
    bcp.WriteToServer(reader);
}

```