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The use of Graphics Processing Units for rendering is well known, but their power for general parallel computation has only recently been explored. Parallel algorithms running on GPUs can often achieve up to x speedup over similar CPU algorithms, with many existing applications for physics simulations, signal processing, financial modeling, neural networks, and countless other fields. This course will cover programming techniques for the GPU. Problem sets will cover performance optimization and specific GPU applications in numerical mathematics, medical imaging, finance, and other fields. Labwork will require significant programming. A working knowledge of the C programming language will be necessary. Although CS 24 is not a prerequisite, it or equivalent systems programming experience is recommended.
The received echo signals have to be delayed to make their wave fronts and phase coherent before summing the signals.
In digital beamforming, the delays are not always located at the sampled points. Generally, the values of the delayed signals are estimated by the values of the nearest samples.
This method is fast and easy, however inaccurate. The comparisons of the visual qualities of the reconstructed images and the qualities of the beamformed signals are reported.
Moreover, the computational speeds of these methods are also optimized by reorganizing the data processing flow and by applying the graphics processing unit GPU. The use of single and double precision floating-point formats of the intermediate data is also considered. The speeds with and without these optimizations are also compared. Keywords: array transducer, CUDA, dynamic receive beamforming, graphics processing unit, image reconstruction, ultrasound imaging 1.
Introduction Ultrasound imaging using arrays of transducer elements has been widely applied in medicine [ 1 , 2 ] and industry [ 3 , 4 ]. One of its challenges is to deal with real-time applications. To improve the image qualities, many imaging techniques require more complex algorithms [ 5 , 6 ], possibly resulting in longer computational time and preventing them from being real-time. An ultrasound image is composed of many scanlines.
Each scanline is created from beamforming of many echo signals, i.
The transducer sends pulse signals and receives echo signals reflected from scatterers or interfaces inside an object before beamforming these echo signals to form a scanline on an image. These processes are repeated for different scanline locations to form the entire image.
Generally, the beamforming adds delays to the signals received from the array elements.
These delay values are calculated from the distances between the elements and a focal point on the scanline in order to equalize the wave fronts and the phases of the echo signals at that focus. This receive beamforming can be performed at many focal points on a scanline, e. However, the delays need to be adjusted for each focus. Using inaccurate delays results in noisy beamformed signals [ 7 ].
At present, many ultrasound devices digitize the echo signals that come from the transducer array elements before beamforming. The required beamforming delays are hardly ever matched with the existing sampling points. Therefore, the subsample estimation of the delayed signal values among these sampling points is needed. The values of the nearest samples are usually used because of the fast processing.
Using the values from the nearest samples is equivalent to using inaccurate delay times.
This makes the delayed signals dephased, and the summation of these signals provides smaller magnitudes. Moreover, the delayed signals could be dephased further if the sampling rate is insufficient.
This makes the summation of the magnitudes go to zero instead of the maximum. Another subsample estimation uses the in-phase I and quadrature Q components of the echo signals. However, if the sampling rate is not equal to four or a multiple of four times the pulse center frequency, the generalized sub-sample delay method [ 9 ] can be applied.
It calculates the Q components using trigonometric functions of the angular deviation between the existing samples and the exact quarter wavelength shift. Researchers have applied field programmable gate arrays FPGA for beamforming in order to increase the processing speeds.
A single FPGA has been used in an ultrasound imaging system in order to meet the high processing requirements of the beamforming [ 10 , 11 ]. For this reason, the delays are generally precomputed and kept in the FPGA memory to save computational time and logic gates. Advanced optimization techniques such as inter-procedural analysis IPA and profile-directed feedback PDF are available only at high levels of optimization but can result in increased performance improvements.
IPA analyzes and optimizes your application as a whole, rather than on a file-by-file basis. PDF generates information that instructs the optimizer to focus on trade-offs that favor code that executes more frequently.
Also there is binary compatibility with GNU-built objects, archives, and other shared objects. You now have the versatility to use the IBM compilers to build parts of your application that will benefit from the higher performance that can be offered and still bind the IBM and GNU compiled parts together in a single application.
The community edition is available for download from IBM. Infrastructure matters The XL family of compilers in conjunction with Power Systems continues to serve the high performance computing HPC and commercial sectors.
One of the most discussed topics in the IT world is the migration of applications to a cloud environment. The IBM Service and Support organization is made up of teams of individuals who work together to provide you with the responsive platform and cross-platform software support that you require. For complex or code-related problems, IBM has specialized, skilled service teams with access to the experts in our development laboratories, as required.
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