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
<mailto: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 <mailto: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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