Piccolo:使用分区表创建快速，分布式程序

Given my complete lack of knowledge of the language, it could be just saying “chicken chicken chicken”, but I’m inclined to believe it’s a bit more complete.

Thank you Xing Fang!

It was a singular experience. As in, I’m not going to do that again.

I’m not sure how meaningful it is, but thanks to ggplot, it is pretty, at least.

Fictional Futures and the Conspicuously Young

A deep and sobering take on modern writers.

Now I’m visiting my favorite time-wasting sites a lot less, but I’m still not any more productive.

I suppose that’s step 2.

Would handling the cache-line coherency issues in hardware be a big difference over a software DSM?

mycloud allows you to make use of your spare cycles, all from within Python – all you need to have available is the SSH connection that you’re already using to manage your systems.  Here’s an example:

Starting your cluster:

# list each machine and the number of cores to use
cluster = mycloud.Cluster([('machine1', 4),
                           ('machine2', 4)],
                          fs_prefix='/path/to/store/results')

Invoke a function over a list of inputs:

result = cluster.map(my_expensive_function, range(1000))

Use the MapReduce interface to easily handle processing of larger datasets:

from mycloud.resource import CSV
input_desc = [CSV('my_input_%d.csv' % i for i in range(100)]
output_desc = [CSV('my_output_file.csv']

def map_identity(k, v):
  yield (k, int(v[0]))

def reduce_sum(k, values):
  yield (k, sum(values))

mr = mycloud.mapreduce.MapReduce(cluster,
                                 map_identity,
                                 reduce_sum,
                                 input_desc,
                                 output_desc)

result = mr.run()

for k, v in result[0].reader():
  print k, v

The code is mostly not-documented, but please contribute documentation and/or complaints as you see fit!

You can access the source code here: https://bitbucket.org/rjpower/mycloud

Or you can access the code with pip via pypi:

pip install mycloud

Finding citations for a paper is a useful but onerous process.  You have to track down all of the work in the past that touched on similar ideas as your own, which amounts to spending hours searching through old journals looking for examples where your work has been done before.  (In computer science, there are no new papers, just recyclings of ideas from the 60′s and 70′s).

In any case, this problem is no longer!  With autocite, you can let the computer find the citations for you, or at least the most promising set of them.  Here’s an example list of papers found based on inputting the abstract for my paper,

Piccolo:

• http://cs.mst.edu/documents/technical_reports/91-14.pdf
• http://www-i2.informatik.rwth-aachen.de/Research/MCS/SDL/safire_compatibility_table.pdf
• http://cs.mst.edu/documents/technical_reports/92-02.pdf
• http://cs.mst.edu/documents/technical_reports/92-24.pdf
• http://staffweb.psdschools.org/wleggett/unitplan2/weblessonplan.pdf
• http://cr.yp.to/bib/1991/ecma-48.pdf
• http://www.lfcs.inf.ed.ac.uk/research/database/publications/vldb04_ranking.pdf
• http://sziami.cs.bme.hu/%7Eildi/seminar/2010_01/kapolnai_abs.pdf
• http://www.hpdc.org/2009/PDF/hursey.pdf
• http://www.cs.arizona.edu/people/dkl/students.pdf

Okay, maybe it won’t solve all of your citation problems, but it’s somewhat entertaining to play with anyway. Try it out here:

Auto-citation generator

If you’re curious, the code is available here, though it’s a bit of a mess.

autocite source