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COM4521/COM6521: Parallel Computing with Graphical
创建日期：2024-05-04 17:06:05
标签： COM4521
Parallel Computing with Graphical

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COM4521/COM6521: Parallel Computing with Graphical

Processing Units (GPUs)
Assignment (80% of module mark)
Document Changes:
Any corrections or changes to this document will be noted here and an update will be sent
out to the course google group mailing list.
Introduction
The assessment has been designed against the module's learning objectives. The aim of the
assignment is to assess your ability and understanding, of implementing and optimising
parallel algorithms using both OpenMP and CUDA.
An existing project containing a single threaded implementation of an algorithm has been
provided. This provided starting code also contains functions for validating the correctness
and timing the performance of your implemented algorithms.
You are expected to implement both an OpenMP and a CUDA version of the provided
algorithm, and to complete a report which documents the justification for the techniques you
have used and how profiling and/or benchmarking supports your choices.
The Algorithm and Starting Code
The task is to implement a simple pixelate filter on a photographic image input to produce a
photo mosaic. The mosaic should decompose the image into a grid of regular tiles, and
average the pixels within each tile. Figure 1 shows the mosaic effect with varying tile sizes
(c), for the purposes of the assignment the tile size is fixed to the value found in the starting
code.
Figure 1: Varying mosaic cell size. Top Left shows the original image, Top Right shows c = 4, Bottom
Left shows c = 16, Bottom Right shows c =32.
The Mosaic algorithm has been broken up into 3 stages
1. Tile sum (stage 1) - the pixels of each tile are summed.
2. Compact Mosaic (stage 2) - each tile sum is converted into the average, to produce a
compact mosaic image with 1 pixel per tile, the average of the whole image is also
returned by this stage.
3. Broadcast (stage 3) - each pixel of the compact mosaic is broadcast to all pixels in
the tile at the full image scale, producing the output image.
The reference implementation and starting code are available to download from:
https://github.com/RSE-Sheffield/COMCUDA_2022/archive/master.zip
The provided code only handles 3 channel images (e.g. RGB), other images will not be
loaded. Furthermore the tile size is fixed (see TILE_SIZE in config.h) and images will be
cropped to a multiple of the tile size in each dimension. Your algorithms only need to support
these provided configurations. The only variable input is that of image size, which may range
from a single tile, to hundreds of tiles when your code is tested on submission.
The handout code includes test images (in the samples folder) of different input sizes. You
can also use your own test images, most RGB jpeg and png images should be supported.
Each of the stages are described in more detail below.
Tile Sum (Stage 1)
Image data is stored in left to right, top to bottom pixel order. Each pixel consisted of 3
unsigned char, representing the 3 colour channels red/green/blue. Throughout, these 3
channels must be treated independently.
In this stage, each tile’s pixels are loaded and their pixels summed to produce a total for
each of the 3 colour channels.
The method cpu_stage1() provides the single threaded implementation of this stage of
the algorithm. The methods validate_tile_sum(...) and skip_tile_sum(...)
can be used to validate or skip the output of this stage. However, both of these functions
require pointers to host memory matching the layout and order of the CPU reference
implementation.
Compact Mosaic (Stage 2)
In this stage, the tile sums from the previous stage are divided by the square of TILE_SIZE
(the number of pixels in a tile) to produce the average pixel value for each tile.
The whole image pixel average must also be returned by this stage, which can be calculated
using the individual tile sums or averages.
The method cpu_stage2() provides the single threaded implementation of this stage of
the algorithm. The methods validate_compact_mosaic(...) and
skip_compact_mosaic(...) can be used to validate or skip the output of this stage.
However, both of these functions require pointers to host memory matching the layout and
order of the CPU reference implementation.
Broadcast (Stage 3)
The final output image is calculated in stage 3 by broadcasting the pixel averages from the
compact mosaic, to a full scale image.
The methods validate_broadcast(...) and skip_broadcast(...) can be used
to validate or skip the output of this stage. However, both of these functions require pointers
to host memory matching the layout and order of the CPU reference implementation.
The Task
Code
For this assignment you must complete the code found in openmp.c and cuda.cu, so that
they perform the same algorithm described above and found inthe reference implementation
(cpu.c), using OpenMP and CUDA respectively. You should not modify or create any other
files within the project. The two algorithms to be implemented are separated into 5 methods
named openmp_begin(), openmp_stage1(), openmp_stage2(),
openmp_stage3(), openmp_end() respectively (and likewise for CUDA). The begin
method takes the input image and performs any memory allocations of copies required by
the algorithm. The end method, similarly returns the output image and frees allocated
memory resources.
You should implement the OpenMP and CUDA algorithms with the intention of achieving the
fastest performance for each algorithm on the hardware you use to develop and test your
assignment. As the second stage is the most advanced, it is recommended that you address
the other stages first. The starting code has helper methods, which allow you to skip and
validate the three stages individually.
It is important to free all used memory, as memory leaks could cause the benchmark mode,
which repeats the algorithm to run out of memory.
The header helper.h contains methods which can be used to skip and validate individual
stages of the assignment, to assist you with development and finding problems with the
correctness of your code. The VALIDATION pre-processor definition is defined only for Debug
builds. Each of the stage functions (for both OpenMP and CUDA) provides a place for you to
call the appropriate validation functions to check that your code is correct. As VALIDATION is
not defined for Release builds this will not affect your benchmarking performance. Note, if
you choose to use an alternative memory layout it is not necessary to use the validation
methods, however if your code does not pass the final tests, you won’t be able to achieve
per stage marks from testing.
The Report
You are expected to provide a report alongside your code submission. For each of the 6
stages you should complete the template provided in Appendix A. The report is your chance
to demonstrate to the marker that you understand what has been taught in the module.
Benchmarks should always be carried out in release mode, with timing averaged over
several runs. The provided project code has a runtime argument --bench which will repeat
the algorithm for a given input 100 times. It is important to benchmark over a range of inputs,
to allow consideration of how the performance scales with the size of inputs.
It is recommended that you provide evidence from the profiler, to support your justification of
the performance and limitations of your CUDA implementations.
Deliverables
You must submit your openmp.c, cuda.cu and your report document (e.g. .pdf/.docx)
within a single zip file via Mole, before the deadline. Your code should build in the Release
mode configuration provided to you (or via the provided Makefile) without errors or warnings
(other than those caused by IntelliSense). You do not need to hand in the project files or any
other files other than openmp.c, cuda.cu. As such, it is important that you do not modify
any of the other files provided in the starting code so that your submitted code remains
compatible with the projects that will be used to mark your submission. You should not
create or include any additional header or source files other than those provided.
Your code should not rely on any third party tools/libraries not introduced within the
lectures/lab classes. The use of Thrust and CUB is permitted.
Even if you do not complete all aspects of the assignment, partial progress should be
submitted as this can still receive marks. I.e. You can use the fallback CPU implementation
to move onto the next stage.
Marking
When marking, both the correctness of the output, and the quality/appropriateness of the
technique used will be assessed. The report should be used to justify the approach used and
demonstrate its impact on the performance.
The marks for each stage of the assignment will be distributed as follows:
OpenMP (30%) CUDA (70%)
Stage 1 (30%) 10% 30%
Stage 2 (40%) 10% 20%
Stage 3 (30%) 10% 20%
The CUDA stage is more heavily weighted as it is more difficult. The first stage in CUDA also
has a greater weight than stages 2 and 3 as there is more opportunity for experimentation in
implementing an optimal solution.
For each of the 6 stages in total, the distribution of the marks will be determined by the
following criteria;
1) Quality of implementation [50%]
a) Have all parts of the stage been implemented?
b) Is the implementation free from race conditions or other errors regardless of
the output?
c) Is code structured clearly and logically?
d) How optimal is the solution that has been implemented? Has good hardware
utilisation been achieved?
2) Automated tests to check for correctness in a range of conditions [25%]
a) Is the implementation for the specific stage complete and correct (i.e. when
compared to a number of test cases which will vary the input)?
3) Choice, justification and performance reporting of the approach towards
implementation as evidenced in the report. [25%]. A breakdown of how marks are
awarded is provided in the report structure template in Appendix A.
If you submit work after the deadline you will incur a deduction of 5% of the mark for each
working day that the work is late after the deadline. Work submitted more than 5 working
days late will be graded as 0. This is the same lateness policy applied university wide to all
undergraduate and postgraduate programmes.

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