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A survey of graphics processing unit (GPU) utilization for radar signal and data processing system
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2017 IEEE.Graphics Processing Units (GPU) in the last decade has been progressing very rapidly. The hardware originally used for image processing displayed on the screen has shifted into a device for computing in parallel (general purpose GPU). GPU can also be used to perform radar data processing either in the stage of signal processing or in the data processing stage. This is done because the radar data is processed in large size and the computation process allows to be parallelized. Previously, radar data processing was performed using a specialized digital signal processor (DSP) device and/or field-programmable gate array (FPGA). But the cost required for both types of devices is more expensive than the GPU. In addition, both have low scalability. GPU use is a compromise solution compared to using DSP or FPGA because GPU can cover the above weaknesses although on the other hand GPU power consumption is not as good as DSP and FPGA. This paper examines the extent to which the GPU has been used in radar signal processing and radar data processing. Several studies have used GPU for radar signal and data processing algorithm implementations on the GPU compared to using the usual Central Processing Unit (CPU). The comparison results show the GPU performance is much better than the CPU. Speedup relative to the CPU has reached the double-digit level.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Computation process,CUDA,Data processing algorithms,Data processing systems,Digital signal processor devices,General purpose gpu,Graphics Processing Unit (GPU),Radar data processing[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]CUDA,graphics processing unit (GPU),parallel computing,radar,radar data processing,radar signal processing[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEEI.2017.8312430[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]