Tricks and Tips – Using omp_set_num_threads and omp_get_num_threads

Tricks and Tips – Using omp_set_num_threads and omp_get_num_threads
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When programming with OpenMP, it should be noticed that omp_get_num_threads() returns 1 in sequential sections of the code. Accordingly, even if setting, by omp_set_num_threads(), an overall number of threads larger than 1, any call to omp_get_num_threads() will return 1, unless we are in a parallel section. The example on our GitHub website tries to clarify this point.
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Compiling mex files with Visual Studio 2013

Compiling mex files with Visual Studio 2013
Configuration: Matlab 2015b, Visual Studio 2013, Intel 64bit machine. In Visual Studio do the following: 1) File -> New Project; Select location and name; in the project type, select Templates -> Visual C++ -> Win32 -> Win32 Console Application -> OK; 2) In the Win32 Application Wizard, click Next, in the Application Type choose DLL, then click Finish. 3) Project -> P...
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A thing to care about when passing a struct to a CUDA kernel

A thing to care about when passing a struct to a CUDA kernel
Structures can be passed by values to CUDA kernels. However, some care should be devoted to set up a proper destructor since the destructor is called at exit from the kernel. Consider this example with the uncommented destructor and do not pay too much attention on what the code actually does. If you run that code, you will receive the following output: Calling destructor Counting in the lo...
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Count the occurrences of numbers in a CUDA array

Count the occurrences of numbers in a CUDA array
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We comparing two approaches to count the occurrences of numbers in a CUDA array. The two approaches use CUDA Thrust: Using thrust::counting_iterator and thrust::upper_bound, following the histogram Thrust example; Using thrust::unique_copy and thrust::upper_bound. A fully worked example is available on our GitHub page. The first approach has shown to be the fastest. On an NVIDIA GTX...
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Radix-4 Decimation-In-Frequency Iterative FFT

Radix-4 Decimation-In-Frequency Iterative FFT
On our GitHub web page, we have made available a fully worked Matlab implementation of a radix-4 Decimation in Frequency FFT algorithm. In the code, we have also provided an overall operations count in terms of complex matrix multiplications and additions. It can be indeed shown that each radix-4 butterfly involves 3 complex multiplications and 8 complex additions. Since there are log4N = l...
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Understanding the radix-2 FFT recursive algorithm

Understanding the radix-2 FFT recursive algorithm
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The recursive implementation of the radix-2 Decimation-In-Frequency algorithm can be understood using the following two figures. The first one refers to pushing the stack phase, while the second one illustrates the popping the stack phase.   In particular, the two figures illustrate the Matlab implementation that you may find on our GitHub website: Implementation I Im...
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Radix-2 Decimation-In-Frequency Iterative FFT

Radix-2 Decimation-In-Frequency Iterative FFT
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At the github page, we prove an implementation of the radix-2 Decimation-In-Frequency FFT in Matlab. The code is an iterative one and considers the scheme in the following figure: A recursive approach is also possible. The implementation calculates also the number of performed multiplications and additions and compares it with the theoretical calculations reported in “Number of operation...
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Radix-2 Decimation-In-Time Iterative FFT

Radix-2 Decimation-In-Time Iterative FFT
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At the github page, we prove an implementation of the radix-2 Decimation-In-Time FFT in Matlab. The code is an iterative one and considers the scheme in the following figure:   A recursive approach is also possible. The implementation calculates also the number of performed multiplications and additions and compares it with the theoretical calculations reported in “Number of oper...
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Number of operation counts for radix-2 FFTs

Number of operation counts for radix-2 FFTs
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The actual Floating Point Operations per Second (FLOPS) depend on the particular hardware and implementation and algorithms with higher number of Floating Point Operations (FLOPs) may correspond to lower FLOPS implementations, just because with such implementations you can more effectively exploit the hardware. To compute the number of floating point operations for a Decimation In Time (DIT) ra...
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Complex numbers product using only three real multiplications

Complex numbers product using only three real multiplications
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The product between two complex numbers can be performed with only three real multiplications. This is an application of Karatsuba's algorithm. Indeed, x = a + i * b; y = c + i * d; real(x * y) = a * c - b * d; imag(x * y) = (a + b) * (c + d) - a * c - b * d; Of course, the question is: can we perform the product between two complex numbers with less than three real multiplications? Th...
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