# Difference between revisions of "GPU610/TeamEh"

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As you can see the std::vector<double, std::allocator<double> >::operator[](unsigned long) function is where | As you can see the std::vector<double, std::allocator<double> >::operator[](unsigned long) function is where | ||

8.7 % of the time is spent and thus this is where the program would most benefit from parallelisation. | 8.7 % of the time is spent and thus this is where the program would most benefit from parallelisation. | ||

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=== Assignment 2 === | === Assignment 2 === | ||

=== Assignment 3 === | === Assignment 3 === |

## Revision as of 17:52, 3 October 2014

GPU610/DPS915 | Student List | Group and Project Index | Student Resources | Glossary

## Contents

# Team Eh

## Team Members

- Benjamin Snively, Some responsibility
- Brad Hoover, Some other responsibility
- Balint Czunyi, Some other responsibility

- ...

## Progress

### Assignment 1

#### Benjamin Snively's Results

##### Introduction

This image processing program was found on github. It processes and manipulates images using convolutions matrices (kernels). It has several different functions including aligning and sharpening images.

To convolve an image the kernel is applied to each pixel. Using the kernel, the pixel's value is combined with that of its neighbors to create a new pixel value. This program implements the filter using two loops to loop over each pixel in sequence. For a given an image convolution is an O(rows x columns) function. As blurring operation on each pixel is independent of the others, therefore it is a perfect candidate for parallelization.

To profile the application, I created a large bitmap file (about 800 x 800, 2MB) and ran it through three different operations. To conserve space, I have not included a profile of all of the available operations.

##### Gassian Blur

Command: `--gassian 5`

Each sample counts as 0.01 seconds.

% cumulative self self total time seconds seconds calls s/call s/call name 44.81 88.57 88.57 _mcount_private 31.92 151.66 63.09 __fentry__ 4.85 161.25 9.59 1 9.59 45.84 Gauss_filter::smooth_ord(Matrix<std::tuple<unsigned int, unsigned int, unsigned int> >&) 1.92 165.05 3.80 633231640 0.00 0.00 Matrix<std::tuple<unsigned int, unsigned int, unsigned int> >::operator()(unsigned int, unsigned int) 1.43 167.88 2.83 633887736 0.00 0.00 std::__shared_ptr<std::tuple<unsigned int, unsigned int, unsigned int>, (__gnu_cxx::_Lock_policy)2>::get() const 1.37 170.58 2.70 630508256 0.00 0.00 std::_Tuple_impl<0ul, int&, int&, int&>& std::_Tuple_impl<0ul, int&, int&, int&>::operator=<unsigned int, unsigned int, unsigned int>(std::_Tuple_impl<0ul, unsigned int, unsigned int, unsigned int> const&) 0.92 172.40 1.82 630508256 0.00 0.00 std::_Head_base<0ul, int&, false>::_Head_base(int&) 0.87 174.12 1.72 630508256 0.00 0.00 std::_Tuple_impl<2ul, int&>& std::_Tuple_impl<2ul, int&>::operator=<unsigned int>(std::_Tuple_impl<2ul, unsigned int> const&) 0.86 175.81 1.69 630508256 0.00 0.00 std::_Head_base<1ul, int&, false>::_Head_base(int&) 0.84 177.47 1.66 630508256 0.00 0.00 std::_Tuple_impl<1ul, int&, int&>& std::_Tuple_impl<1ul, int&, int&>::operator=<unsigned int, unsigned int>(std::_Tuple_impl<1ul, unsigned int, unsigned int> const&) 0.78 179.02 1.55 630508256 0.00 0.00 std::_Head_base<2ul, int&, false>::_Head_base(int&) 0.77 180.54 1.52 630508256 0.00 0.00 std::tuple<int&, int&, int&> std::tie<int, int, int>(int&, int&, int&) 0.74 182.00 1.46 630508256 0.00 0.00 std::_Tuple_impl<2ul, int&>::_Tuple_impl(int&) 0.65 183.28 1.28 630508256 0.00 0.00 std::tuple<int&, int&, int&>& std::tuple<int&, int&, int&>::operator=<unsigned int, unsigned int, unsigned int, void>(std::tuple<unsigned int, unsigned int, unsigned int> const&) 0.57 184.41 1.13 630508256 0.00 0.00 std::tuple<int&, int&, int&>::tuple(int&, int&, int&) 0.55 185.50 1.09 630508256 0.00 0.00 std::_Head_base<0ul, int&, false>::_M_head(std::_Head_base<0ul, int&, false>&) 0.52 186.53 1.03 630508256 0.00 0.00 std::_Tuple_impl<0ul, int&, int&, int&>::_Tuple_impl(int&, int&, int&)

##### Sharpen

Command: `--unsharp`

Each sample counts as 0.01 seconds.

% cumulative self self total time seconds seconds calls ms/call ms/call name 44.44 0.96 0.96 _mcount_private 27.31 1.55 0.59 __fentry__ 7.41 1.71 0.16 1 160.00 458.44 unsharp(Matrix<std::tuple<unsigned int, unsigned int, unsigned int> >) 5.56 1.83 0.12 20345464 0.00 0.00 Matrix<std::tuple<unsigned int, unsigned int, unsigned int> >::operator()(unsigned int, unsigned int) 1.39 1.86 0.03 21001560 0.00 0.00 std::__shared_ptr<std::tuple<unsigned int, unsigned int, unsigned int>, (__gnu_cxx::_Lock_policy)2>::get() const 1.39 1.89 0.03 7876396 0.00 0.00 std::_Tuple_impl<0ul, unsigned int, unsigned int, unsigned int>::_M_head(std::_Tuple_impl<0ul, unsigned int, unsigned int, unsigned int>&) 1.39 1.92 0.03 656096 0.00 0.00 std::_Tuple_impl<2ul, unsigned int>& std::_Tuple_impl<2ul, unsigned int>::operator=<unsigned char>(std::_Tuple_impl<2ul, unsigned char>&&) 0.93 1.94 0.02 7876396 0.00 0.00 std::_Tuple_impl<1ul, unsigned int, unsigned int>::_M_head(std::_Tuple_impl<1ul, unsigned int, unsigned int>&) 0.93 1.96 0.02 1968288 0.00 0.00 unsigned char&& std::forward<unsigned char>(std::remove_reference<unsigned char>::type&) 0.93 1.98 0.02 656096 0.00 0.00 std::_Head_base<0ul, unsigned char, false>::_M_head(std::_Head_base<0ul, unsigned char, false>&)

##### Identity

command: `--custom '0,0,0,0,1,0,0,0,0'`

Each sample counts as 0.01 seconds.

% cumulative self self total time seconds seconds calls ms/call ms/call name 53.61 1.71 1.71 _mcount_private 28.21 2.61 0.90 __fentry__ 4.39 2.75 0.14 2 70.00 218.45 Use_kernel::new_im() 1.88 2.81 0.06 8542240 0.00 0.00 Matrix<std::tuple<unsigned int, unsigned int, unsigned int> >::operator()(unsigned int, unsigned int) 0.94 2.84 0.03 5904864 0.00 0.00 std::_Tuple_impl<0ul, int&, int&, int&>::_Tuple_impl(int&, int&, int&) 0.63 2.86 0.02 13126802 0.00 0.00 __gnu_cxx::__enable_if<std::__is_integer<int>::__value, double>::__type std::floor<int>(int) 0.63 2.88 0.02 9841440 0.00 0.00 double& std::forward<double&>(std::remove_reference<double&>::type&) 0.63 2.90 0.02 7223552 0.00 0.00 std::_Head_base<0ul, unsigned int, false>::_M_head(std::_Head_base<0ul, unsigned int, false> const&) 0.63 2.92 0.02 5904864 0.00 0.00 std::_Tuple_impl<1ul, int&, int&>& std::_Tuple_impl<1ul, int&, int&>::operator=<unsigned int, unsigned int>(std::_Tuple_impl<1ul, unsigned int, unsigned int> const&) 0.63 2.94 0.02 5904864 0.00 0.00 std::_Tuple_impl<2ul, int&>::_Tuple_impl(int&)

##### Summary

The functions that perform the filtering are `Gauss_filter::smooth_ord`

, `unsharp`

and `Use_kernel::new_im()`

. These functions are all O(r x c) with respect to image dimensions and thus where the biggest gains from parallelization will be found.

#### Bradly Hoovers Results

##### Introduction

The SHA-1 algorithm used in this project was implemented by Paul E. Jones[1]. It is a C++ version of the algorithm. The recursive permutation algorithm was taken from a user submission[2] on stack exchange.

[1]http://www.packetizer.com/security/sha1/

[2] http://codereview.stackexchange.com/questions/38474/brute-force-algorithm-in-c

After some tweaking to integrate the two to work in conjunction, I ran the program using the Upper and lower case alphabet for the permutation, with a length of 5, as input. The length and character set are hard coded.

##### Length of 5, upper and lowercase

Command: ./brutis

Each sample counts as 0.01 seconds.

Each sample counts as 0.01 seconds. % cumulative self self total time seconds seconds calls s/call s/call name 83.38 109.96 109.96 387659012 0.00 0.00 SHA1::ProcessMessageBlock() 8.77 121.52 11.56 387659012 0.00 0.00 SHA1::PadMessage() 4.01 126.81 5.28 1930693908 0.00 0.00 SHA1::Input(unsigned char const*, unsigned int) 1.48 128.76 1.96 387659012 0.00 0.00 SHA1::operator<<(char const*) 1.19 130.33 1.57 387659012 0.00 0.00 SHA1::Result(unsigned int*) 0.73 131.29 0.96 387659012 0.00 0.00 SHA1::Reset() 0.36 131.76 0.47 5 0.09 26.35 SHA1::check(char*, int, int, int, char const*) 0.05 131.83 0.07 SHA1::~SHA1() 0.03 131.88 0.05 SHA1::SHA1() 0.03 131.92 0.05 SHA1::operator<<(unsigned char) 0.02 131.94 0.02 SHA1::Input(char) 0.00 131.94 0.00 1 0.00 0.00 _GLOBAL__sub_I__ZN4SHA1C2Ev 0.00 131.94 0.00 1 0.00 0.00 _GLOBAL__sub_I_main

##### Summary

The total runtime for this test was approximately 132 seconds. Using Amdahl's law to calculate the speed up I would obtain on my laptop, the equation is:

S384 = 1 / ((1 - 0.8338) + (0.8338/384)) = 5.9393

The maximum expected speed is 5.9393. My laptop’s 650M GPU only has 384 cores. This is not a significant increase in speed.

Using my desktop’s GTX780 has 2304 core. Using my desktop’s gpu the resulting speed up would be:

S2304 = 1 / ((1 - 0.8338) + (0.8338/2304)) = 6.004

After observing these results, and further analysis of the algorithm, I have found that the SHA-1 algorithm is a sequential algorithm not entirely suitable for parallelisation.

Due to this, I choose Ben's image processing for parallelisation.

#### Balint Czunyi's Results

### Introduction

My Project used a Heat Equation calculator.

Source: https://github.com/MyCodes/Heat-Equation

After some changes to the Makefile to work with the profiler and testing several different parameters for the calculations I have come up with the following results:

### Explicit from -15 to +15

Each sample counts as 0.01 seconds.

% cumulative self self total time seconds seconds calls ms/call ms/call name 34.78 0.08 0.08 __fentry__ 26.09 0.14 0.06 2 30.00 31.67 Heat::writePlot() 26.09 0.20 0.06 _mcount_private 8.70 0.22 0.02 8986009 0.00 0.00 std::vector<double, std::allocator<double> >::operator[](unsigned long) 4.35 0.23 0.01 1 10.00 26.66 Heat::solveEquation(char) 0.00 0.23 0.00 8986009 0.00 0.00 std::vector<std::vector<double, std::allocator<double> >, std::allocator<std::vector<double, std::allocator<double> > > >::operator[](unsigned long) 0.00 0.23 0.00 1496502 0.00 0.00 heatFunction(double, double)

. . .

### Summary

As you can see the std::vector<double, std::allocator<double> >::operator[](unsigned long) function is where 8.7 % of the time is spent and thus this is where the program would most benefit from parallelisation.