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→Assignment 1: - Added Benjamin's results
== Progress ==
=== Assignment 1 ===
==== Benjamin Snively's Results ====
===== Introduction =====
This image processing program was found on [https://github.com/dbrotikovskaya/ImageProcessing 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: <code>--gassian 5</code>
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: <code>--unsharp</code>
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: <code>--custom '0,0,0,0,1,0,0,0,0'</code>
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 <code>Gauss_filter::smooth_ord</code>, <code>unsharp</code> and <code>Use_kernel::new_im()</code>. These functions are all O(r x c) with respect to image dimensions and thus where the biggest gains from parallelization will be found.
=== Assignment 2 ===
=== Assignment 3 ===
