Hello,
Apologies for not replying earlier, I've been quite busy these last two
days.
So far I have been exploring the advantages/disadvantages of using
QML/QtQuick vs traditional widget based GUI. QML has some great design
features that could improve the overall user experience and aren't easily
implemented when using widgets. I was originally planning to develop some
parts using QML ( animations and charts ) and integrate them with the main
widget based GUI. However I am now exploring the possibility of doing the
entire GUI in QML. Suggestions on which approach to choose are welcome.
Reading through your discussion about expert benchmark setting I see that I
probably should have spent more time studying the autotuner and benchmark
codes :/ I understand that there is a great need for expert benchmark
customization and I hope to succeed in making that part as detailed as
possible, but there should a certain limit to the extent of details. What
I'm saying is I'd rather not spend time developing features that will be
used only a couple of times. Surely there are some details that aren't of
critical importance?
It would be great if you guys could agree on what expert details are of
greatest priority. I'm going to start studying the autotuner and benchmark
codes so I can better understand what needs to be done.
Best regards,
Namik
On Tue, May 6, 2014 at 9:38 AM, Karl Rupp <r...@iue.tuwien.ac.at> wrote:
> Hi,
>
>
> > Why is data pointless? I'd rather have only a few datapoints on new
>
>> hardware out there rather than having absolutely no data at all.
>>
>>
>> I mean, the data is pretty useful because it tells us about the best
>> default kernel for large square matrices, but it is not very useful if
>> we want to build a general input-dependent model, as it requires in my
>> experience more than 1000 data points.
>>
>
> This is true. So this calls for an hierarchical approach:
> Level 1: Just a couple of known kernels for a given data size, which are
> compared on the target machine.
> Level 2: A full tuning set for one data size on the target
> Level 3: All ~1000 points for building a model
>
> Execution times between these levels vary significantly: While almost all
> users will go through Level 1 anyway, only a few will have the patience to
> wait for results on Level 2. Level 3 will be mostly for us to have a
> 'normalized' process for building performance models. Either way, if others
> will join (machine learning community?), that would be great!
>
>
>
> I'd rather refrain from running Python scripts from the benchmark
>> GUI. This is intended to be an end-user tool. Those interested in
>> running from Python should take the Python code (i.e. PyViennaCL)
>> directly.
>>
>>
>> Are you sure? It would not take a lot of efforts to have an optional way
>> to call the python script with the proper arguments from the auto-tuner,
>> as long as the user provides the path and that he has all the necessary
>> dependencies.
>>
>
> The second half of the last sentence is the problem. I expect >80% of
> users to run on Windows, where anything but a 'double click installer' is a
> non-standard process. If Namik has time left by the end of the summer, we
> can look into that, but we first need to focus on our target audience.
>
>
>
> Such cases are probably only interesting for the 'expert settings'
>> tab in the GUI, as these parameters only make sense to people who
>> *really* know what they are doing (and willing to invest the time).
>> For bloggers, journalists, etc., who just want to quickly get some
>> performance datapoints for the very latest hardware, this is usually
>> not of interest. We need to foc
>>
>> us on serving the main audience first and then watch out for
>> fruitful directions on how to extend it further.
>>
>>
>> Of course ! I've been referring to the "expert settings" tab from the
>> beginning :)
>>
>
> Ah, please say so :-)
>
> Best regards,
> Karli
>
>
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