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
pytables_expr_test.py
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