Initial documentation release

-Added explicit documentation that covers many questions about VkFFT and aims to provide help in using VkFFT. It is an initial version of it, so it will be expanded and improved in the future.
-Added checks for passing of 0s as buffer sizes (#41)
-Fixed issue #44

benchmark_scripts/vkFFT_scripts/src/sample_50_convolution_VkFFT_single_1d_matrix.cpp

@@ -73,6 +73,7 @@ VkFFTResult sample_50_convolution_VkFFT_single_1d_matrix(VkGPU* vkGPU, uint64_t
 	configuration.kernelConvolution = true; //specify if this plan is used to create kernel for convolution
 	configuration.coordinateFeatures = 9; //Specify dimensionality of the input feature vector (default 1). Each component is stored not as a vector, but as a separate system and padded on it's own according to other options (i.e. for x*y system of 3-vector, first x*y elements correspond to the first dimension, then goes x*y for the second, etc).
 	//coordinateFeatures number is an important constant for convolution. If we perform 1x1 convolution, it is equal to number of features, but matrixConvolution should be equal to 1. For matrix convolution, it must be equal to matrixConvolution parameter. If we perform 2x2 convolution, it is equal to 3 for symmetric kernel (stored as xx, xy, yy) and 4 for nonsymmetric (stored as xx, xy, yx, yy). Similarly, 6 (stored as xx, xy, xz, yy, yz, zz) and 9 (stored as xx, xy, xz, yx, yy, yz, zx, zy, zz) for 3x3 convolutions. 
+	configuration.normalize = 1;//normalize iFFT
 
 	//After this, configuration file contains pointers to Vulkan objects needed to work with the GPU: VkDevice* device - created device, [uint64_t *bufferSize, VkBuffer *buffer, VkDeviceMemory* bufferDeviceMemory] - allocated GPU memory FFT is performed on. [uint64_t *kernelSize, VkBuffer *kernel, VkDeviceMemory* kernelDeviceMemory] - allocated GPU memory, where kernel for convolution is stored.
 	configuration.device = &vkGPU->device;

benchmark_scripts/vkFFT_scripts/src/sample_51_convolution_VkFFT_single_3d_matrix_zeropadding_r2c.cpp

@@ -69,6 +69,7 @@ VkFFTResult sample_51_convolution_VkFFT_single_3d_matrix_zeropadding_r2c(VkGPU*
 	configuration.size[1] = 32;
 	configuration.size[2] = 32;
 
+	configuration.normalize = 1;//normalize iFFT
 	configuration.performZeropadding[0] = true; //Perform padding with zeros on GPU. Still need to properly align input data (no need to fill padding area with meaningful data) but this will increase performance due to the lower amount of the memory reads/writes and omitting sequences only consisting of zeros.
 	configuration.performZeropadding[1] = true;
 	configuration.performZeropadding[2] = true;

benchmark_scripts/vkFFT_scripts/src/sample_52_convolution_VkFFT_single_2d_batched_r2c.cpp

@@ -73,6 +73,7 @@ VkFFTResult sample_52_convolution_VkFFT_single_2d_batched_r2c(VkGPU* vkGPU, uint
 	configuration.performR2C = true; //Perform R2C/C2R transform. Can be combined with all other options. Reduces memory requirements by a factor of 2. Requires special input data alignment: for x*y*z system pad x*y plane to (x+2)*y with last 2*y elements reserved, total array dimensions are (x*y+2y)*z. Memory layout after R2C and before C2R can be found on github.
 	configuration.coordinateFeatures = 2; //Specify dimensionality of the input feature vector (default 1). Each component is stored not as a vector, but as a separate system and padded on it's own according to other options (i.e. for x*y system of 3-vector, first x*y elements correspond to the first dimension, then goes x*y for the second, etc).
 	//coordinateFeatures number is an important constant for convolution. If we perform 1x1 convolution, it is equal to number of features, but matrixConvolution should be equal to 1. For matrix convolution, it must be equal to matrixConvolution parameter. If we perform 2x2 convolution, it is equal to 3 for symmetric kernel (stored as xx, xy, yy) and 4 for nonsymmetric (stored as xx, xy, yx, yy). Similarly, 6 (stored as xx, xy, xz, yy, yz, zz) and 9 (stored as xx, xy, xz, yx, yy, yz, zx, zy, zz) for 3x3 convolutions. 
+	configuration.normalize = 1;//normalize iFFT
 
 	configuration.numberBatches = 2;
 	//After this, configuration file contains pointers to Vulkan objects needed to work with the GPU: VkDevice* device - created device, [uint64_t *bufferSize, VkBuffer *buffer, VkDeviceMemory* bufferDeviceMemory] - allocated GPU memory FFT is performed on. [uint64_t *kernelSize, VkBuffer *kernel, VkDeviceMemory* kernelDeviceMemory] - allocated GPU memory, where kernel for convolution is stored.