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-- -- mathalope search search for: skip to content blog small projects deep learning on intel nervana ai cluster (aka colfax hpc cluster) intel colfax cluster – notes – index page intel edison workshop tutorials accelerated c++ solutions exploratory data analysis – diamonds github – opencv-python tutorial walkthrough google earth engine projects ipython notebook – tentative numpy tutorial small project – sampling distribution – mean dice score swirl – r programming – console log index page video tracking project – with opencv project full-stack study notes udacity – authentication and authorization – oauth – notes udacity – configuring linux web servers – notes udacity – data analysis with r – notes udacity – full stack foundations udacity – intro to algorithm – notes udacity – intro to artificial intelligence – notes udacity – intro to computer science – study notes udacity – intro to data science – notes udacity – intro to descriptive statistics – study notes udacity – intro to html and css – notes udacity – intro to inferential statistics – study notes udacity – intro to relational databases – notes udacity – javascript basics – notes udacity – linux command basics udacity – programming foundations with python – notes udacity – web development – notes about useful resources blogging 3.5 years later… 140,000 visitors from 180+ countries. 2017 year-end reflection uncategorized atlas7.githib.io javascript javascript – bind intuition – devil taking over jim example deep learning how to setup tensorflow jupyter notebook on intel nervana ai cluster (colfax) for deep learning deep learning how to setup pytorch jupyter notebook on intel nervana ai cluster (colfax) for deep learning numpy initialize numpy arrays with tuple unpacking technique – np.random.rand and np.zeros examples blogging 3.5 years later… 140,000 visitors from 180+ countries. 2017 year-end reflection january 9, 2018 johnny leave a comment view the code on gist . google analytics reflection web traffic uncategorized atlas7.githib.io november 24, 2017 johnny leave a comment view the code on gist . javascript javascript – bind intuition – devil taking over jim example november 3, 2017 johnny leave a comment view the code on gist . bind call javascript deep learning how to setup tensorflow jupyter notebook on intel nervana ai cluster (colfax) for deep learning september 25, 2017 johnny leave a comment view the code on gist . colfax cluster conda deep learning hpc intel distribution python intel nervana jupyter jupyter notebook optimization tensorflow xeon xeon phi deep learning how to setup pytorch jupyter notebook on intel nervana ai cluster (colfax) for deep learning september 25, 2017 johnny 1 comment view the code on gist . conda deep learning hpc intel distribution python intel nervana jupyter jupyter notebok optimization pytorch xeon xeon phi numpy initialize numpy arrays with tuple unpacking technique – np.random.rand and np.zeros examples september 13, 2017 johnny leave a comment view the code on gist . array initialization numpy python trick tuple unpacking hpc high performance computing (hpc) running intel xeon phi: n-body simulation example september 7, 2017 johnny leave a comment written an article on implementing a toy o(n^2) n-body simulation algorithm with high performance computing (hpc) and intel xeon phi architecture. in part 1, we describe the code optimization journey to boost performance from 3.2 to 2831 gflops on a single node. in part 2, we distribute workload across 16 cluster nodes to further boost performance to 33208 gflops. end result: capable of performing over 1 trillion (1,099,510,579,200) particle-to-particle interactions per time-step at sub-second level (~662 ms). view the code on gist . c++ colfax cluster distributed computing high performance computing hpc modern code optimization parallel programming xeon phi colfax intel colfax cluster – targeting a specific instruction set / intel processor architecture august 22, 2017 johnny leave a comment view the code on gist . intel c++ compiler optimization xeon phi colfax intel colfax cluster – estimate theoretical peak flops for intel xeon phi processors august 21, 2017 johnny leave a comment view the code on gist . colfax distributed computing flops hpc intel parallel programming performance xeon phi colfax intel colfax cluster – optimize a numerical integration implementation with parallel programming and distributed computing august 17, 2017 johnny leave a comment c++ colfax cluster data center distributed computing high performance computing hpc intel knights landing modern code mpi multi threading openmp optimization parallel programming xeon xeon phi posts navigation 1 2 … 31 next → author johnny chan consultant, data analyst, developer. personal links mathalope.co.uk github - atlas7 stackoverflow - atlas7 kaggle - atlas7 udacity - atlas7 linkedin hackerrank - atlas7 youtube devpost - atlas7 meetup twitter - jatlas7 freecodecamp codepen instructables - atlas7 instagram - jatlas7 dribble - atlas7 quora medium - jatlas7 soundcloud - jatlas7 npm disqus - jatlas7 klout score - jatlas7 codewars - atlas7 intel devmesh tumblr - jatlas7 disqus - jatlas7 portfolio view full profile → google ads follow me on twitter my tweets google ads top articles opencv-python - how to install opencv-python package to anaconda (windows) swirl - r programming - lesson 9 - functions swirl - r programming - console log index page accelerated c++ solutions swirl - r programming - lesson 2 - workspace and files swirl - r programming - lesson 1 - basic building blocks swirl - r programming - lesson 4 - vectors swirl - r programming - lesson 7 - matrices and data fames swirl - r programming - lesson 6 - subsetting vectors ipython jupyter notebook trick - auto module reload and inline chart plotting recent comments anonymous on accelerated c++ solution to exercise 1-4 anonymous on block comment python code in sublime text editor jerome on swirl – r programming – lesson 9 – functions vijaya123456 on block comment python code in sublime text editor homework tutorial help on code4startup – project code4newbies – ruby-on-rails app – note anonymous on udacity – intro to computer science – python – crypto analysis – letter frequency sureshkumar on udacity – intro to computer science – a clever trick to round numbers (with python) oana on swirl – r programming – lesson 9 – functions anonymous on udacity – intro to computer science – python – compute number of days between two dates (including leap years) anonymous on accelerated c++ solution to exercise 3-2 google ads recent posts 3.5 years later… 140,000 visitors from 180+ countries. 2017 year-end reflection january 9, 2018 atlas7.githib.io november 24, 2017 javascript – bind intuition – devil taking over jim example november 3, 2017 how to setup tensorflow jupyter notebook on intel nervana ai cluster (colfax) for deep learning september 25, 2017 how to setup pytorch jupyter notebook on intel nervana ai cluster (colfax) for deep learning september 25, 2017 initialize numpy arrays with tuple unpacking technique – np.random.rand and np.zeros examples september 13, 2017 high performance computing (hpc) running intel xeon phi: n-body simulation example september 7, 2017 intel colfax cluster – targeting a specific instruction set / intel processor architecture august 22, 2017 intel colfax cluster – estimate theoretical peak flops for intel xeon phi processors august 21, 2017 intel colfax cluster – optimize a numerical integration implementation with parallel programming and distributed computing august 17, 2017 tags accelerated c++ ai c++ colfax colfax research computer science descriptive statistics diamonds distributed computing eda exercises exploratory data analysis ggplot2 histogram hpc hypothesis testing inferential statistics intel javascript linux machine learning mathematics modern code numpy oop optimization parallel programming programming python r recursion ruby-on-rails sampling distr

URL analysis for mathalope.co.uk

http://mathalope.co.uk/2015/03/08/swirl-r-programming-lesson-4-vectors/
http://mathalope.co.uk/2018/01/
http://mathalope.co.uk/category/programming/r/
http://mathalope.co.uk/tag/c/
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http://mathalope.co.uk/2018/01/09/3-5-years-later-140000-visitors-from-180-countries-2017-year-end-reflection/#respond
http://mathalope.co.uk/2015/08/
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http://mathalope.co.uk/category/cheatsheet/
http://mathalope.co.uk/category/programming/python/
http://mathalope.co.uk/2015/12/
http://mathalope.co.uk/tag/pytorch/
http://mathalope.co.uk/2015/03/08/swirl-r-programming-lesson-9-functions/
http://mathalope.co.uk/2017/09/13/initialize-numpy-arrays-with-tuple-unpacking-technique-np-random-rand-and-np-zeros-examples/

Whois Information

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Domain name:
mathalope.co.uk

Data validation:
Nominet was able to match the registrant's name and address against a 3rd party data source on 25-May-2014

Registrar:
GoDaddy.com, LLP. [Tag = GODADDY]
URL: http://uk.godaddy.com

Relevant dates:
Registered on: 25-May-2014
Expiry date: 25-May-2021
Last updated: 01-Mar-2016

Registration status:
Registered until expiry date.

Name servers:
ns6189.hostgator.com
ns6190.hostgator.com

WHOIS lookup made at 01:58:13 27-Jan-2019

--
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  REFERRER http://www.nominet.org.uk

  REGISTRAR Nominet UK

SERVERS

  SERVER co.uk.whois-servers.net

  ARGS mathalope.co.uk

  PORT 43

  TYPE domain

DOMAIN

SPONSOR
GoDaddy.com, LLP. [Tag = GODADDY]
URL: http://uk.godaddy.com
Relevant dates:

  CREATED 25-May-2014

  CHANGED 01-Mar-2016

STATUS
Registered until expiry date.

NSERVER

  NS6189.HOSTGATOR.COM 50.87.144.130

  NS6190.HOSTGATOR.COM 192.232.223.69

  NAME mathalope.co.uk

DISCLAIMER
This WHOIS information is provided for free by Nominet UK the central registry
for .uk domain names. This information and the .uk WHOIS are:
Copyright Nominet UK 1996 - 2019.
You may not access the .uk WHOIS or use any data from it except as permitted
by the terms of use available in full at https://www.nominet.uk/whoisterms,
which includes restrictions on: (A) use of the data for advertising, or its
repackaging, recompilation, redistribution or reuse (B) obscuring, removing
or hiding any or all of this notice and (C) exceeding query rate or volume
limits. The data is provided on an 'as-is' basis and may lag behind the
register. Access may be withdrawn or restricted at any time.

  REGISTERED no

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