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Multidimensional Digital Signal Processing (MDSP) refers to the extension of [[Digital signal processing]] (DSP) techniques to signals that vary in more than one dimension. While conventional DSP typically deals with one-dimensional data, such as time-varying [[Audio signal|audio signals]], MDSP involves processing signals in two or more dimensions. Many of the principles from one-dimensional DSP, such as [[Fourier transform|Fourier transforms]] and [[filter design]], have analogous counterparts in multidimensional signal processing.
Modern [[
==Motivation==
▲Modern [[General-purpose computing on graphics processing units|general purpose graphics processing units (GPGPUs)]] are considered as having an excellent throughput on vector operations and numeric manipulations through a high degree of parallel computations. While processing digital signals, particularly multidimensional signals, often involves a series of vector operations on massive amount of independent data samples, GPGPUs are now widely employed to accelerate multidimensional DSP, such as [[image processing]], [[Video processing|video codecs]], [[Radar signal characteristics|radar signal analysis]], [[sonar signal processing]], and [[ultrasound scan]]ning. Conceptually, using GPGPU devices to perform multidimensional DSP is able to dramatically reduce the computation complexity compared with [[Cpu|central processing units (CPUs)]], [[Digital signal processor|digital signal processors (DSPs)]], or other [[Field-programmable gate array|FPGA]] accelerators.
Processing multidimensional signals is a common problem in scientific research and/or engineering computations. Typically, a DSP problem's computation complexity grows exponentially with the number of dimensions. Notwithstanding, with a high degree of time and storage complexity, it is extremely difficult to process multidimensional signals in real-time. Although many fast algorithms (e.g. [[Fast Fourier transform|FFT]]) have been proposed for 1-D DSP problems, they are still not efficient enough to be adapted in high dimensional DSP problems. Therefore, it is still hard to obtain the desired computation results with
==Existing
▲Processing multidimensional signals is a common problem in scientific research and/or engineering computations. Typically, a DSP problem's computation complexity grows exponentially with the number of dimensions. Notwithstanding, with a high degree of time and storage complexity, it is extremely difficult to process multidimensional signals in real-time. Although many fast algorithms (e.g. [[Fast Fourier transform|FFT]]) have been proposed for 1-D DSP problems, they are still not efficient enough to be adapted in high dimensional DSP problems. Therefore, it is still hard to obtain the desired computation results with [[Digital signal processor|digital signal processors (DSPs)]]. Hence, better algorithms and hardware architecture are needed to accelerate multidimensional DSP computations.
Practically, to accelerate multidimensional DSP, some common approaches have been proposed and developed in the past decades.
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A makeshift to achieve a real-time requirement in multidimensional DSP applications is to use a lower sampling rate, which can efficiently reduce the number of samples to be processed at one time and thereby decrease the total processing time. However, this can lead to the aliasing problem due to the [[Nyquist–Shannon sampling theorem|sampling theorem]] and poor-quality outputs. In some applications, such as military radars and medical images, we are eager to have highly precise and accurate results. In such cases, using a lower sampling rate to reduce the amount of computation in the multidimensional DSP ___domain is not always allowable.
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Digital signal processors are designed specifically to process vector operations. They have been widely used in DSP computations for decades. However, most digital signal processors are only capable of manipulating a few operations in parallel. This kind of
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In order to accelerate multidimensional DSP computations, using dedicated [[supercomputer]]s or [[Computer cluster|cluster computers]] is required in some circumstances, e.g., [[weather forecasting]] and military radars. Nevertheless, using supercomputers designated to simply perform DSP operations takes considerable money cost and energy consumption. Also, it is not practical and suitable for all multidimensional DSP applications.
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[[Graphics processing unit|GPUs]] are originally devised to accelerate image processing and video stream rendering. Moreover, since modern GPUs have good ability to perform numeric computations in parallel with a relatively low cost and better energy efficiency, GPUs are becoming a popular alternative to replace supercomputers performing multidimensional DSP.<ref>{{cite
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[[File:SIMD GPGPU.jpg|alt= Figure illustrating a SIMD/vector computation unit in GPGPUs..|thumb|GPGPU/SIMD computation model
Modern GPU designs are mainly based on the [[Single instruction, multiple data|SIMD]] (Single Instruction Multiple Data) computation paradigm.<ref>{{cite journal|title
GPGPUs are able to perform an operation on multiple independent data concurrently with their vector or SIMD functional units. A modern GPGPU can spawn thousands of concurrent threads and process all threads in a batch manner. With this nature, GPGPUs can be employed as DSP accelerators easily while many DSP problems can be solved by [[Divide and conquer algorithms|divide-and-conquer]] algorithms. A large scale and complex DSP problem can be divided into a bunch of small numeric problems and be processed altogether at one time so that the overall time complexity can be reduced significantly. For example, multiplying two {{math|''M'' × ''M''}} matrices can be processed by {{math|''M'' × ''M''}} concurrent threads on a GPGPU device without any output data dependency. Therefore, theoretically, by means of GPGPU acceleration, we can gain up to {{math|''M'' × ''M''}} speedup compared with a traditional CPU or digital signal processor.
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Currently, there are several existing programming languages or interfaces which support GPGPU programming.
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[[CUDA]] is the standard interface to program [[Nvidia|NVIDIA]] GPUs. NVIDIA also provides many CUDA libraries to support DSP acceleration on NVIDIA GPU devices.<ref>{{cite web|title
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[[OpenCL]] is an industrial standard which was originally proposed by [[Apple Inc.]] and is maintained and developed by the [[Khronos Group]] now.<ref>{{cite web|title
[[File:OpenCL
The following figure illustrates the execution flow of launching an OpenCL program on a GPU device. The CPU first detects OpenCL devices (GPU in this case) and then invokes a just-in-time compiler to translate the OpenCL source code into target binary. CPU then sends data to GPU to perform computations. When the GPU is processing data, CPU is free to process its own tasks.
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[[C++ AMP
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[[OpenACC
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Suppose {{math|'''A'''}} and {{math|'''B'''}} are two {{math|''m'' × ''m''}} matrices and we would like to compute {{math|1 = '''C''' = '''A''' × '''B'''}}.
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\end{pmatrix},\quad C_{ij}=\sum_{k=1}^m A_{ik}B_{kj}</math>
To compute each element in {{math|'''C'''}} takes {{math|''m''}} multiplications and {{math|(''m'' – ''1'')}} additions. Therefore, with a CPU implementation, the time complexity to achieve this computation is ''Θ(n''<sup
// MxM matrix multiplication in C
void matrixMul(
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for (int col = 0; col < size; col++) {
int id = row * size + col;
for (int m = 0; m < size; m++) {
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</syntaxhighlight>
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Convolution is a frequently used operation in DSP. To compute the 2-D convolution of two ''m'' × ''m'' signals, it requires {{math|''m''<sup>''2''</sup>}} multiplications and {{math|''m'' × (''m'' – ''1'')}} additions for an output element. That is, the overall time complexity is ''Θ(n''<sup
2-D convolution equation:
<math>y(n_1, n_2)=x(n_1,n_2)**h(n_1,n_2)=\sum_{k_1=0}^{m-1}\sum_{k_2=0}^{m-1}x(k_1, k_2)h(n_1-k_1, n_2-k_2)</math><syntaxhighlight lang="c++" line="1">
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</syntaxhighlight>
Note that, although the example demonstrated above is a 2-D convolution, a similar approach can be adopted for a higher dimension system. Overall, for a s-D convolution, a GPGPU implementation has time complexity ''Θ(n''<sup
M-D convolution equation:
<math>y(n_1,n_2,...,n_s)=x(n_1,n_2,...,n_s)**h(n_1,n_2,...,n_s)=\sum_{k_1=0}^{m_1-1}\sum_{k_2=0}^{m_2-1}...\sum_{k_s=0}^{m_s-1}x(k_1, k_2,...,k_s)h(n_1-k_1,n_2-k_2,...,n_s-k_s)</math>
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In addition to convolution, the [[Fourier transform|discrete
<math>X(\Omega_1,\Omega_2,...,\Omega_s)=\sum_{n_1=0}^{m_1-1}\sum_{n_2=0}^{m_2-1}...\sum_{n_s=0}^{m_s-1}x(n_1, n_2,...,n_s)e^{-j(\Omega_1n_1+\Omega_1n_1+...+\Omega_sn_s)}</math>
Practically, to implement an M-D DTFT
// DTFT in OpenCL
__kernel void convolution(
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for (int i = 0; i < size; i++) {
X_re += (x_re[id] * cos(2 * 3.1415 * id / size) - x_im[id]
X_im += (x_re[id] * sin(2 * 3.1415 * id / size) + x_im[id]
}
}
</syntaxhighlight>
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Designing a multidimensional digital filter is a big challenge, especially [[Infinite impulse response|IIR]] filters. Typically it relies on computers to solve difference equations and obtain a set of approximated solutions. While GPGPU computation is becoming popular, several adaptive algorithms have been proposed to design multidimensional [[Finite impulse response|FIR]] and/or [[Infinite impulse response|IIR]] filters by means of GPGPUs.<ref>{{cite
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Radar systems usually
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Many [[self-driving
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In order to have accurate diagnosis, 2-D or 3-D medical signals, such as [[ultrasound]], [[X-ray]], [[Magnetic resonance imaging|MRI]], and [[CT scan|CT]], often require very high sampling rate and image resolutions to reconstruct images. By applying GPGPUs' superior computation power, it was shown that we can acquire better-quality medical images<ref>{{cite web|title
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{{Reflist}}
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