Hi Johann,
Thanks for bring this up. I believe that I have determined that this is
not a PyTables / pthreads issue. Doing some profiling npoints=1000000, I
found that most of the time (97%) was being spent in the sum() call (see
below). This ratio doesn't change much with different values of npoints.
Since there is no implicit parallelism here, I would recommend using
numpy.sum() instead of Python's.
I hope this helps. If you need other tips on speeding up the
sum operation, please let us know.
Be Well
Anthony
Timer unit: 1e-06 s
File: pytables_expr_test.py
Function: fn at line 66
Total time: 1.63254 s
Line # Hits Time Per Hit % Time Line Contents
==============================================================
66 def fn(p, h5table):
67 '''
68 actual function we
are going to minimize. It consists of
69 the pytables Table
object and a list of parameters.
70 '''
71 1 14 14.0 0.0 uv =
h5table.colinstances
72
73 # store parameters in
a dict object with names
74 # like p0, p1, p2,
etc. so they can be used in
75 # the Expr object.
76 4 21 5.2 0.0 for i in
xrange(len(p)):
77 3 19 6.3 0.0 k = 'p'+str(i)
78 3 14 4.7 0.0 uv[k] = p[i]
79
80 # systematic shift on
b is a polynomial in a
81 1 4 4.0 0.0 db = 'p0 * a*a + p1
* a + p2'
82
83 # the element-wise
function
84 1 6 6.0 0.0 fn_str = '(a - (b +
%s))**2' % db
85
86 1 16427 16427.0 1.0 expr =
Expr(fn_str,uservars=uv)
87 1 21438 21438.0 1.3 expr.eval()
88
89 # returning the "sum
of squares"
90 1 1594600 1594600.0 97.7 return sum(expr)
On Mon, May 14, 2012 at 1:59 PM, Johann Goetz <jgo...@ucla.edu> wrote:
> SHORT VERSION:
>
> Please take a look at the fn() function in the attached file (pasted
> below). When I run this with 10M events or more I notice that the total CPU
> usage never goes above the percentage I get using single-threaded eval().
> Am I at some other limit or can I improve performance by doing something
> else?
>
> LONG VERSION:
>
> I have been trying to use the tables.Expr object to speed up a
> sophisticated calculation over an entire dataset (a pytables Table object).
> The calculation took so long that I had to create a simple example to make
> sure I knew what I was doing. I apologize in advance for the lengthy code
> below, but I wanted the example to mimic exactly what I'm trying to do and
> to be totally self-contained.
>
> I have attached a file (and pasted it below) in which I create a hdf5 file
> with a single large Table of two columns. As you can see, I'm not worried
> about writing speed at all - I'm concerned about read speed.
>
> I would like to draw your attention to the fn() function. This is where I
> evaluate a "chi-squared" value on the dataset. My strategy is to populate
> the "h5table.colinstances" dict object with several parameters which I call
> p0, p1, etc and then create the Expr object using these and the column
> names from the Table.
>
> If I create 10M rows (77 MB file) in the Table (with the command below),
> the evaluation seems to be CPU bound (one of my cores is at 100% - the
> others are idle) and it takes about 7 seconds (about 10 MB/s). Similarly, I
> get about 70 seconds for 100M events.
>
> python pytables_expr_test.py 10000000
> python pytables_expr_test.py 100000000
>
> So my question: It seems to me that I am not fully using the CPU power
> available on my computer (see next paragraph). Am I missing something or
> doing something wrong in the fn() function below?
>
> A few side-notes: My hard-disk is capable of over 200 MB/s in sequential
> reading (sustained and tested with large files using the iozone program), I
> have two 4-core CPU's on this machine but the total CPU usage during eval()
> never goes above the percentage I get using single-threaded mode with
> "numexpr.set_num_threads(1)".
>
> I am using pytables 2.3.1 and numexpr 2.0.1
>
> --
> Johann T. Goetz, PhD. <http://sites.google.com/site/theodoregoetz/>
> jgo...@ucla.edu
> Nefkens Group, UCLA Dept. of Physics & Astronomy
> Hall-B, Jefferson Lab, Newport News, VA
>
>
> ### BEGIN file: pytables_expr_test.py
>
> from tables import openFile, Expr
>
> ### Control of the number of threads used when issuing the
> ### Expr::eval() command
> #import numexpr
> #numexpr.set_num_threads(2)
>
> def create_ntuple_file(filename, npoints, pmodel):
> '''
> create an hdf5 file with a single table which contains
> npoints number of rows of type row_t (defined below)
> '''
> from numpy import random, poly1d
> from tables import IsDescription, Float32Col
>
> class row_t(IsDescription):
> '''
> the rows of the table to be created
> '''
> a = Float32Col()
> b = Float32Col()
>
> def append_row(h5row, pmodel):
> '''
> consider this a single "event" being appended
> to the dataset (table)
> '''
> h5row['a'] = random.uniform(0,10)
>
> h5row['b'] = h5row['a'] # reality (or model)
> h5row['b'] = h5row['b'] - poly1d(pmodel)(h5row['a']) # systematics
> h5row['b'] = h5row['b'] + random.normal(0,0.1) # noise
>
> h5row.append()
>
> h5file = openFile(filename, 'w')
> h5table = h5file.createTable('/', 'table', row_t, "Data")
> h5row = h5table.row
>
> # recording data to file...
> for n in xrange(npoints):
> append_row(h5row, pmodel)
>
> h5file.close()
>
> def create_ntuple_file_if_needed(filename, npoints, pmodel):
> '''
> looks to see if the file is already there and if so,
> it makes sure its the right size. Otherwise, it
> removes the existing file and creates a new one.
> '''
> from os import path, remove
>
> print 'model parameters:', pmodel
>
> if path.exists(filename):
> h5file = openFile(filename, 'r')
> h5table = h5file.root.table
> if len(h5table) != npoints:
> h5file.close()
> remove(filename)
>
> if not path.exists(filename):
> create_ntuple_file(filename, npoints, pmodel)
>
> def fn(p, h5table):
> '''
> actual function we are going to minimize. It consists of
> the pytables Table object and a list of parameters.
> '''
> uv = h5table.colinstances
>
> # store parameters in a dict object with names
> # like p0, p1, p2, etc. so they can be used in
> # the Expr object.
> for i in xrange(len(p)):
> k = 'p'+str(i)
> uv[k] = p[i]
>
> # systematic shift on b is a polynomial in a
> db = 'p0 * a*a + p1 * a + p2'
>
> # the element-wise function
> fn_str = '(a - (b + %s))**2' % db
>
> expr = Expr(fn_str,uservars=uv)
> expr.eval()
>
> # returning the "sum of squares"
> return sum(expr)
>
> if __name__ == '__main__':
> '''
> usage:
> python pytables_expr_test.py [npoints]
>
> Hint: try this with 10M points
> '''
> from sys import argv
> from time import time
>
> npoints = 1000000
> if len(argv) > 1:
> npoints = int(argv[1])
>
> filename = 'tmp.'+str(npoints)+'.hdf5'
>
> pmodel = [-0.04,0.002,0.001]
>
> print 'creating file (if it doesn\'t exist)...'
> create_ntuple_file_if_needed(filename, npoints, pmodel)
>
> h5file = openFile(filename, 'r')
> h5table = h5file.root.table
>
> print 'evaluating function'
> starttime = time()
> print fn([0.,0.,0.], h5table)
> print 'evaluated file in',time()-starttime,'seconds.'
>
> #EOF
>
>
>
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