Aug 31

If you are interested in Hadoop or Mapreduce, I would like to recommend participating or submitting your paper to the First International Workshop on Theory and Practice of Mapreduce (MAPRED’2010) (held in correspondance with the 2nd IEEE International Conference on Cloud Computing Technology and Science).

(I just joined the workshop as a program committee member)

Best regards,

Amund Tveit (co-founder of Atbrox)

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Aug 20

Atbrox is startup company providing technology and services for Search and Mapreduce/Hadoop. Our background is from Google, IBM and research.

GPU – Graphical Processing Unit like the NVIDIA Tesla – is fascinating hardware, in particular regarding extreme parallelism (hundreds of cores) and memory bandwidth (tens of Gigabytes/second). The main programming languages for programming GPUs are C-based OpenCL and Nvidia’s Cuda, in addition there are wrappers to those in many languages, for the following example we use Andreas Kl√∂ckner’s PyCuda for Python.

Word Count with PyCuda and MapReduce

One of the classic mapreduce examples is word frequency count (i.e. individual word frequencies), but let us start with an even simpler example – word count, i.e. how many words are there in a (potentially big) string?

In python the default approach would perhaps be to do:

wordcount = len(bigstring.split())

But assuming that you didn’t have split() or that split() was too slow, what would you do?

How to calculate word count?
If you have the string mystring = "this is a string" you could iterate through it and count the number of spaces, e.g. with

sum([1 for c in mystring if c == ' '])

(notice the one-off error), and perhaps split it up and parallelize it somehow. However, if there are several spaces in a row in the string this algorithm will fail, and it doesn’t use the GPU horsepower.

The MapReduce approach
Assuming you still have mystring = "this is a string", try to align the string almost with itself, i.e. have one string being all characters in mystring except the last – "this is a strin" == mystring[:-1] (called prefix from here), and another string with all characters in mystring except the first – "his is a string" == mystring[1:] (called suffix from here), and align those two like this:

this is a strin # prefix
his is a string # suffix

you can see that counting all occurences of when the character in the upper string (prefix) is whitespace and the corresponding character in the lower string (suffix) is non-white will give the correct count of words (with the same one-off as above that can be fixed by checking that first character is non-whitespace). This way of counting also deals with multiple spaces in a row (as the above one doesn’t). This can be expressed in Python with Map() and Reduce() as:

mystring = "this is a string"
prefix = mystring[:-1]
suffix = mystring[1:]
mapoutput = map(lambda x,y: (x == ' ')*(y != ' '), prefix, suffix)
reduceoutput = reduce(lambda x,y: x+y, mapoutput)
sum = reduceoutput + (mystring[0] != ' ') # fix one off-error

Mapreduce with PyCuda

PyCuda supports using python and numpy library with Cuda, and it also has library to support mapreduce type calls on data structures loaded to the GPU (typically arrays), under is my complete code for calculating word count with PyCuda, I used the complete works by Shakespeare as test dataset (downloaded as Plain text) and replicated it hundred times so in total 493820800 bytes (~1/2 Gigabyte) that I uploaded to our Nvidia Tesla C1060 GPU and run word count on (the results were compared with unix command line wc and len(dataset.split()) for smaller datasets).

import pycuda.autoinit
import numpy
from pycuda import gpuarray, reduction
import time

def createCudaWordCountKernel():
    initvalue = "0"
    mapper = "(a[i] == 32)*(b[i] != 32)" # 32 is ascii code for whitespace
    reducer = "a+b"
    cudafunctionarguments = "char* a, char* b"
    wordcountkernel = reduction.ReductionKernel(numpy.float32, neutral = initvalue, 
                                            reduce_expr=reducer, map_expr = mapper,
                                            arguments = cudafunctionarguments)
    return wordcountkernel

def createBigDataset(filename):
    print "reading data"
    dataset = file(filename).read()
    print "creating a big dataset"
    words = " ".join(dataset.split()) # in order to get rid of \t and \n
    chars = [ord(x) for x in words]
    bigdataset = []
    for k in range(100):
        bigdataset += chars
    print "dataset size = ", len(bigdataset)
    print "creating numpy array of dataset"
    bignumpyarray = numpy.array( bigdataset, dtype=numpy.uint8)
    return bignumpyarray

def wordCount(wordcountkernel, bignumpyarray):
    print "uploading array to gpu"
    gpudataset = gpuarray.to_gpu(bignumpyarray)
    datasetsize = len(bignumpyarray)
    start = time.time()
    wordcount = wordcountkernel(gpudataset[:-1],gpudataset[1:]).get()
    stop = time.time()
    seconds = (stop-start)
    estimatepersecond = (datasetsize/seconds)/(1024*1024*1024)
    print "word count took ", seconds*1000, " milliseconds"
    print "estimated throughput ", estimatepersecond, " Gigabytes/s"
    return wordcount

if __name__ == "__main__":
    bignumpyarray = createBigDataset("dataset.txt")
    wordcountkernel = createCudaWordCountKernel()
    wordcount = wordCount(wordcountkernel, bignumpyarray)

Results

python wordcount_pycuda.py 
reading data
creating a big dataset, about 1/2 GB of Shakespeare text
dataset size =  493820800
creating numpy array of dataset
uploading array to gpu
word count took  38.4578704834  milliseconds
estimated throughput  11.9587084015  Gigabytes/s (95.67 Gigabit/s)
word count =  89988104.0

Improvement Opportunities?
There are plenty of improvement opportunities, in particular fixing the creation of numpy array – bignumpyarray = numpy.array( bigdataset, dtype=numpy.uint8) – which took almost all of the total time.

It is also interesting to notice that this approach doesn’t gain from using combiners like in Hadoop/Mapreduce (a combiner is basically a reducer that sits on the tail of the mapper and creates partial results in the case of associative and commutative reducer methods, it can for all practical purposes be compared to an afterburner on a jet motor).

Atbrox on LinkedIn

Best regards,

Amund Tveit (Atbrox co-founder)

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