Theano GPU计算慢于numpy

Question

I'm learning to use theano. I want to populate a term-document matrix (a numpy sparse matrix) by calculating binary TF-IDF for each element inside it:

import theano
import theano.tensor as T
import numpy as np
from time import perf_counter

def tfidf_gpu(appearance_in_documents,num_documents,document_words):
    start = perf_counter()
    APP = T.scalar('APP',dtype='int32')
    N = T.scalar('N',dtype='int32')
    SF = T.scalar('S',dtype='int32')
    F = (T.log(N)-T.log(APP)) / SF
    TFIDF = theano.function([N,APP,SF],F)
    ret = TFIDF(num_documents,appearance_in_documents,document_words)
    end = perf_counter()
    print("\nTFIDF_GPU ",end-start," secs.")
    return ret

def tfidf_cpu(appearance_in_documents,num_documents,document_words):
    start = perf_counter()
    tfidf = (np.log(num_documents)-np.log(appearance_in_documents))/document_words
    end = perf_counter()
    print("TFIDF_CPU ",end-start," secs.\n")
    return tfidf

But the numpy version is much faster than the theano implementation:

Progress 1/43
TFIDF_GPU  0.05702276699594222  secs.
TFIDF_CPU  1.454801531508565e-05  secs.

Progress 2/43
TFIDF_GPU  0.023830442980397493  secs.
TFIDF_CPU  1.1073017958551645e-05  secs.

Progress 3/43
TFIDF_GPU  0.021920352999586612  secs.
TFIDF_CPU  1.0738993296399713e-05  secs.

Progress 4/43
TFIDF_GPU  0.02303648801171221  secs.
TFIDF_CPU  1.1675001587718725e-05  secs.

Progress 5/43
TFIDF_GPU  0.02359767400776036  secs.
TFIDF_CPU  1.4385004760697484e-05  secs.

....

I've read that this can be due to overhead, that for small operations might kill the performance.

Is my code bad or should I avoid using GPU because of the overhead?

Answer 1

The thing is that you are compiling your Theano function every time. The compilation takes time. Try passing the compiled function like this:

def tfidf_gpu(appearance_in_documents,num_documents,document_words,TFIDF):
    start = perf_counter()
    ret = TFIDF(num_documents,appearance_in_documents,document_words)
    end = perf_counter()
    print("\nTFIDF_GPU ",end-start," secs.")
    return ret

APP = T.scalar('APP',dtype='int32')
N = T.scalar('N',dtype='int32')
SF = T.scalar('S',dtype='int32')
F = (T.log(N)-T.log(APP)) / SF
TFIDF = theano.function([N,APP,SF],F)

tfidf_gpu(appearance_in_documents,num_documents,document_words,TFIDF)

Also your TFIDF task is a bandwidth intensive task. Theano, and GPU in general, is best for computation intensive tasks.

The current task will considerable overhead taking the data to the GPU and back because in the end you will need to read each element O(1) times. But if you want to do more computation it makes sense to use the GPU.