pyccel.complexity.memory module

pyccel.complexity.memory module#

This module provides us with functions and objects that allow us to compute the memory complexity a of a program.

Example

>>> code = '''
... n = 10
... for i in range(0,n):
...     for j in range(0,n):
...         x = pow(i,2) + pow(i,3) + 3*i
...         y = x / 3 + 2* x
... '''

>>> from pyccel.complexity.memory import MemComplexity
>>> M = MemComplexity(code)
>>> d = M.cost()
>>> print "f = ", d['f']
f =  n**2*(2*ADD + DIV + 2*MUL + 2*POW)
>>> print "m = ", d['m']
m =  WRITE + 2*n**2*(READ + WRITE)
>>> q = M.intensity()
>>> print "+++ computational intensity ~", q
+++ computational intensity ~ (2*ADD + DIV + 2*MUL + 2*POW)/(2*READ + 2*WRITE)
class pyccel.complexity.memory.MemComplexity(filename_or_text)[source]#

Bases: Complexity

Class for memory complexity computation. This class implements a simple two level memory model

Example

>>> code = '''
... n = 10
... for i in range(0,n):
...     for j in range(0,n):
...         x = pow(i,2) + pow(i,3) + 3*i
...         y = x / 3 + 2* x
... '''

>>> from pyccel.complexity.memory import MemComplexity
>>> M = MemComplexity(code)
>>> d = M.cost()
>>> print "f = ", d['f']
f =  n**2*(2*ADD + DIV + 2*MUL + 2*POW)
>>> print "m = ", d['m']
m =  WRITE + 2*n**2*(READ + WRITE)
>>> q = M.intensity()
>>> print "+++ computational intensity ~", q
+++ computational intensity ~ (2*ADD + DIV + 2*MUL + 2*POW)/(2*READ + 2*WRITE)

Now let us consider a case where some variables are supposed to be in the fast memory, (r in this test)

>>> code = '''
... n = 10
... x = zeros(shape=(n,n), dtype=float)
... r = float()
... r = 0
... for i in range(0, n):
...     r = x[n,i] + 1
... '''

>>> M = MemComplexity(code)
>>> d = M.cost()
>>> print "f = ", d['f']
f =  ADD*n
>>> print "m = ", d['m']
m =  2*WRITE + n*(READ + WRITE)
>>> q = M.intensity()
>>> print "+++ computational intensity ~", q
+++ computational intensity ~ ADD/(READ + WRITE)

Notice, that this is not what we expect! the cost of writing into r is ‘zero’, and therefor, there should be no \(n*WRITE\) in our memory cost. In order to achieve this, you must tell pyccel that you have the variable r is already in the fast memory. This can be done by adding the argument local_vars=[‘r’] when calling the cost method.

>>> d = M.cost(local_vars=['r'])
>>> print "f = ", d['f']
f =  ADD*n
>>> print "m = ", d['m']
m =  READ*n + WRITE
>>> q = M.intensity(local_vars=['r'])
>>> print "+++ computational intensity ~", q
+++ computational intensity ~ ADD/READ

and this is exactly what we were expecting.

cost()[source]#

Computes the complexity of the given code.

local_vars: list

list of variables that are supposed to be in the fast memory. We will ignore their corresponding memory accesses.

intensity(d=None, args=None, local_vars=[], verbose=False)[source]#

Returns the computational intensity for the two level memory model.

d: dict

dictionary containing the floating and memory costs. if not given, we will compute them.

args: list

list of free parameters, i.e. degrees of freedom.

local_vars: list

list of variables that are supposed to be in the fast memory. We will ignore their corresponding memory accesses.

verbose: bool

talk more

pyccel.complexity.memory.count_access(expr, visual=True)[source]#

returns the number of access to memory in terms of WRITE and READ.

expr: sympy.Expr

any sympy expression or pyccel.ast.core object

visual: bool

If visual is True then the number of each type of operation is shown with the core class types (or their virtual equivalent) multiplied by the number of times they occur.

local_vars: list

list of variables that are supposed to be in the fast memory. We will ignore their corresponding memory accesses.

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