Re: [hpx-users] [GSoC 2018] Histogram Performance Counter

[Simberg Mikael]

Hi Saurav,

I'd also be happy to hear more about HdrHistogram. It seems to me that the main 
feature is the HDR-ness. This means roughly the same as having exponentially 
spaced bins in the histogram?

More generally, if HdrHistogram offers compelling features over the one we 
already have, it is definitely a useful addition. Concurrency is in any case a 
must. If you haven't already seen the "More Arithmetic Performance Counters" 
and "Augment CSV Files" projects have a look at them as parts of those can 
(more likely should) be combined into one project. "More Arithmetic Performance 
Counters" has already been done by Hartmut in PR 2745, but more operations 
could potentially be useful (log, exp?). If you'd like to do more data analysis 
you should look at "Augment CSV Files". You could mix and match parts of these 
into a nice package.

Looking at the histogram implementation that Hartmut linked is a good place to 
start, as is the rest of the performance counter framework. That should give 
you a better idea of what we already have and what we might be lacking.

Hop onto IRC (#ste||ar on freenode) if you have more detailed questions!

Kind regards,
Mikael

From: hpx-users-boun...@stellar.cct.lsu.edu 
[hpx-users-boun...@stellar.cct.lsu.edu] on behalf of Hartmut Kaiser 
[hartmut.kai...@gmail.com]
Sent: Monday, February 19, 2018 10:57 PM
To: hpx-users@stellar.cct.lsu.edu; 'Saurav Sachidanand'
Subject: Re: [hpx-users] [GSoC 2018] Histogram Performance Counter

Hey Saurav,

> My name is Saurav Sachidanand and I wish to participate in GSoC 2018.
> I'm intrigued with the Histogram Performance Counter project. I've
> previously worked with HdrHistorgram [1], which is a histogram
> implementation that can record integer and float values with high range
> and precission, with fixed space and time costs. Implementations in
> several languages exist, but not in C++. The reference Java version [2]
> provides several more features, including a concurrent version of the
> histogram. Would implementing a generic C++ concurrent HdrHistgram
> perforamance counter, supporting all features from the Java version and
> utilizing HPX's APIs, be a useful addition?
>
> This idea came to mind beacuse I participated in GSoC last year [3], where
> I built a Performance Co-Pilot instrumentation library in Rust, and I had
> to integrate HdrHistogram into the API [4].
>
> Any guidance and feedback will be greatly appreciated.

I don't know anything about the HdrHistgram you're referring to. Would you care 
to elaborate?

We have a histogram implementation in HPX 
(https://github.com/STEllAR-GROUP/hpx/blob/master/hpx/util/histogram.hpp) which 
is currently used for a special performance counter in the parcel (message) 
coalescing layer. But this does not have to be used for a general purpose 
counter.

Regards Hartmut
---
http://boost-spirit.com
http://stellar.cct.lsu.edu

>
> Thanks,
> Saurav
>
> [1] - https://hdrhistogram.github.io/HdrHistogram/
> [2] - https://github.com/HdrHistogram/HdrHistogram
> [3] -
>  https://summerofcode.withgoogle.com/archive/2017/projects/479329678209843
> 2/
> [4] - https://github.com/performancecopilot/hornet#histogram

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[hpx-users] CUDA in HPX

[Tim Biedert]

Hi,

what's the correct way to use CUDA with HPX nowadays? I've seen the 
HPXCL GSoC project, but apparently there is also a HPX_WITH_CUDA option 
in CMake now.


I have a hard time finding clear documentation on this.

Thanks!

Tim





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Re: [hpx-users] CUDA in HPX

[Hartmut Kaiser]

Tim,

> what's the correct way to use CUDA with HPX nowadays? I've seen the
> HPXCL GSoC project, but apparently there is also a HPX_WITH_CUDA option
> in CMake now.
> 
> I have a hard time finding clear documentation on this.

That's because there is no documentation.

CUDA support in HPX is experimental at best. We appreciate any help we can get 
improving the picture.

We have two different ways of integrating with CUDA: HPXCL and HPX.Compute. 

HPXCL is a separate project providing a set of HPX components that allow to 
talk to a GPU device, even remotely. The API exposed resembles OpenCL to some 
extent, but if you look at the existing examples you should find your way 
around.

HPX.Compute is part of the main repository. It mainly consists of a very low 
level facility that allows to spawn any CUDA kernel on a local GPU device from 
your C++ code and representing this as a future you can use to synchronize the 
kernel execution with your cpu work. It also has higher level facilities that 
use this facility to implement some of the parallel algorithms (including the 
use of parallel::copy for transparent and asynchronous data transfer). 
HPX.Compute also has some allocators and executors that help managing data and 
execution locality.

HTH
Regards Hartmut
---
http://boost-spirit.com
http://stellar.cct.lsu.edu


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Re: [hpx-users] NumFOCUS Proposals -- Zeng Liang

[Hartmut Kaiser]

>      My basic information is as follows:
>      *Name: Zeng Liang
>      *Location: China, Beijing
>      *University: Bachelor 2014-2018 : Department of Information and
> software engineering, University of Electronic Science and Technology of
> China(UESTC) , P.R.China
>                           Ph.D. 2018.09- : Department of Institute for
> Interdisciplinary Information and Science(IIIS),Tsinghua University,
> P.R.China
>     *Future Plans: Do some research work
>     *Email: zlp...@qq.com
>     *Languages: python(3 years), C++(4 years)
>     *Github Link: github.com/zlpure
>     *Things want to do:  Applying Machine Learning Techniques on HPX
> Parallel Algorithms
>     *Deep Learning Framework: pytorch, tensorflow, keras
>     *Machine Learning/Deep Learning courses taken:
>       1. Machine-Learning on Coursera
>       2.  CS231n: Convolutional Neural Networks for Visual Recognition
> Winter2016
>       3. CS224n: Natural Language Processing with Deep Learning
>       4. CS234: Reinforcement Learning
> 
>       The attachment is my resume. For more details, you can look at my
> cv.
>       Looking forward to your replay.

Please get in contact with concrete questions, proposals etc. either on IRC
or using the mailing list hpx-users@stellar.cct.lsu.edu (please see here for
details:
https://github.com/STEllAR-GROUP/hpx/blob/master/.github/SUPPORT.md). 
Others might want to join the discussions.

Thanks!
Regards Hartmut
---
http://boost-spirit.com
http://stellar.cct.lsu.edu



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[hpx-users] [GSOC 2018] Add More Arithmetic Performance Counters

[surya priy]

Hello all !

I , SURYA PRIY would like to work on *"Add More Arithmetic Performance
Counters and Adding PARALLEL ALGORITHMS " *in the coming GSOC 2018 with
STELLAR GROUP. I am B.TECH student with Computer Science stream and have
previously worked with Chapel  .
Sorry for being late in introducing myself in this group . Actually, I am
studying about HPX framework and learning them . I have introduced myself
on IRC with username *victor_ludorum*  .  So, As per the issue
 , there should be
implementation of some more arithmetic performance counters having
statistical properties. I have done some research on this and found the
statistical functions in BOOST

.
Some of them have been already implemented but many of them are left .I
would mention some of the functions that should be implemented here like

   - COUNT
   - COVARIANCE
   - DENSITY
   - ERROR OF MEANS
   - KURTOSIS
   - MOMENT
   - TAIL
   - SKEWNESS

and some important numeric functions too like

   - LOG
   - EXPONENT
   - ABS

I have also thought to implement some *PARALLEL ALGORITHMS* left
unimplemented from the List
 like

   - PARTIAL_SORT
   - NTH_ELEMENT
   - STABLE SORT

and more important should be suggested that should be work on .
Please suggest how do I start my work on this .

Thanks .

Regards
Surya Priy

Github profile Link - victor-ludorum 
My work in Chapel - PR

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[hpx-users] Generating Data for HPX smart executors

[Gabriel Laberge]

Hi,
I had a questions on the way data was generated in order to train the  
logistics regressions models talked about in [0]  
https://arxiv.org/pdf/1711.01519.pdf
For each of the training examples, the optimal execution  
policies,chunk sizes and prefetching distance had to be found before  
the training process in order to have good data. I wonder if the  
optimal parameters for the training examples were found by trial and  
error or if there is another technique.
Thank you..



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