程序代写 Please note that all examples and laboratory exercises conducted during the

Please note that all examples and laboratory exercises conducted during the laboratory sessions are
not assessed.
A disparity algorithm is given in C language (see blackboard: Assignment Disparity.zip). You are
required to study the code and increase the performance in terms of speed.

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Tasks to perform:
You are required to study the C code (stereo_vision_c.c), optimise the algorithm and use
intrinsics and compiler switches such as #pragma to increase the performance. Always
compile your code with -03 optimization flag. Your code should be faster than the base line
version (0.58s) with zero errors. You get full marks for achieving faster than 0.01s with zero
Total mark 20%
2. Write the linear assembly for the stereo vision code (stereo_vision_sa.sa), in order to
increase the speed performance using the SIMD instructions. You get full marks for achieving
faster than 0.01s with zero errors.
Total mark 60%
Note: You get 30% if the code in linear assembly operates properly regardless of the
performance (with zero errors). If your modified algorithm achieved a higher performance
but with slight errors, please submit it along with a zero-error version code and explain clearly
in the report, we will mark them on a case-by-case basis.
3. Write a formal report explaining the implementation and showing/discussing the results.
Presentation will be taken into account.
Total mark 20%
Make sure to check the correctness of your code by looking at the printed error rate and the generated
image as explained in the lecture. When you start working on linear assembly, remember to reset the
output buffer from C code to zeros (line 76 in main.c). A report template is given to you, your report
should not exceed 6 pages; extra pages can be used for the appendix.
Please submit the report in one PDF document, and one zip file with ALL the project. (Please DO
NOT send only the source files). Marks will be deduced if a project will not compile. Only ONE report
per group is required, with names, email addresses and candidate number. Submission should be
through Blackboard.

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