A hand-coded kernel with lines of code speeds up interpolation from 4. With limited changes to address performance bottlenecks, performance was further enhanced. Rewriting this loop so that each row is processed one at a time that is, taking the FFT and then performing the vector multiplication results in a 1. However, programming such complex architectures can increase development costs by reducing developer productivity and code reuse. The change is trivial to implement, requiring only a net increase of eight lines of code, yet it yields a 20 percent improvement in the execution time of the entire front-end stage. And the application remained portable across vastly different architectures. For background, Synthetic Aperture Radar SAR is used for a variety of imaging and remote sensing applications, including reconnaissance, surveillance, and terrain mapping.
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With limited changes to address performance bottlenecks, performance was further enhanced. The entire front-end processing chain was run looping over the data 10 times to average out the measurements. And the application remained portable across vastly different architectures.
[PDF] VSIPL++: A Signal Processing Library Scaling with Moore's Law - Semantic Scholar
However, programming such complex architectures can increase development costs by reducing developer productivity and code reuse. A contribution from several inputs for each side-lobe of the sinc function used in the interpolation is added to calculate the intensity and phase of the corresponding output pixel.
Click graphic to zoom. Getting good performance from the SPEs requires carefully balancing the input and output of data with the computations being performed. The need for high-performance Signal- and Image-Processing SIP applications is driving interest in parallel vzipl++ multicore hardware for military embedded systems. Its goal is to simultaneously deliver productivity, portability, and performance.
In addition, because the C code is iterative, it is more difficult to divide among multiple processors or to optimize for different architectures. The benchmark demonstrates the intense computational requirements found in actual systems using synthetic and scalable data.
Getting from prototype to high-performance production code efficiently. The raw radar returns are processed by means of a 2D Fourier matched filtering step, a spatial frequency interpolation step, and a transformation back to the spatial domain. Because development costs vary roughly linearly with the number of lines of code, it is important to look at performance and Source Lines Of Code SLOC count together to understand the relationship between developer effort and performance benefit.
Optimizing a C implementation to achieve comparable performance would greatly increase program complexity and thus would drive up development time and cost.
Case study: Developing high-performance radar applications using the VSIPL++ API
A large amount of time is spent in a loop over data in the "range" direction perpendicular to the flight pathperforming a polar-to-rectangular coordinate conversion. Even modest, easy-to-implement changes can significantly improve performance. The key is to find a library at the right level of abstraction — high enough to provide the necessary primitives for the application domain but low enough to vsipl+ for efficient implementations and thus high performance.
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By contrast, the C reference implementation is more verbose and thus more error prone. Out of the box, this code outperformed vwipl++ C reference implementation.
Given large enough data sets, memory accesses in the second step cause cache misses on a Xeon processor, leading to expensive reads from main memory.
Thus, a vsil++ development technique that offers benefits for this SSAR's benchmark points to an approach that is likely to work for larger SIP applications as well. The change is trivial to implement, requiring only a net increase of eight lines of code, yet it yields a 20 percent improvement in the execution time of the entire front-end stage.
DTIC ADA433656: Pulse Compression Made Easy With VSIPL++
He holds a B. With a portable library, though, the same application code will run on more than one system.
Consider the following challenge. Click graphic to zoom by 1. C implementation source code. Getting from prototype to high-performance production code efficiently Consider the following challenge. For background, Synthetic Aperture Radar SAR is used for a variety of imaging and remote sensing applications, including reconnaissance, surveillance, vsipl+ terrain mapping. Rewriting this loop so that each row is processed one at a time that is, taking the FFT and then performing the vector multiplication results in a 1.
A hand-coded kernel with lines of code speeds up interpolation from 4.
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