This project presents a version of Counting Sort Algorithm that has been parallelized by use of the CUDA environment on NVIDIA GPUs.
Counting Sort is an algorithm for sorting a collection of objects according to
their respective keys (small, positive integers).
The assumption made on the input array is that it must be filled either with
integers in a range [min, max] or any other type of elements which can be
represented each with a unique key in that range.
This algorithm does not compare the elements but rather counts their frequency
in the array to determine their new position.
Below, the pseudocode for the algorithm:
1 initialize array[N]
// find min and max in the array
2 min <- array[0]
3 max <- array[0]
4 for i <- 1 to N
5 if array[i] < min
6 min <- array[i]
7 if array[i] > max
8 max <- array[i]
9 end for
// initialize count array with a size equal to the [min, max] range
10 range <- max - min + 1
11 initialize count[range]
// count the number of occurrences of each element
12 for i <- 0 to N
13 count[array[i] - min] <- count[array[i] - min] + 1
14 end for
// reposition the elements based on their number of occurrences
15 initialize z <- 0
16 for i <- min to max
17 for j <- 0 to count[i - min]
18 array[z] <- i
19 z <- z + 1
20 end for
21 end forWorst-case Time Complexity: O(n + k)
Where n is the number of elements in the array and k is the size of the
[min, max] range.
Worst-case Space Complexity: O(n + k).
If the range [min, max] is far greater than the number of elements, the
auxiliary array introduces a considerable amount of wasted space.
Another trait of this algorithm is its Stability: elements sharing the same key will retain their relative positions.
To verify and estimate the benefits of parallelization, a shell cript can be executed to run the C program multiple times, with different combinations of these parameters:
- Array size
- Block and Grid size
The shell script calls (if not told otherwise) a Python script to create tables in order to easily compare the execution times of the different versions.
In order to be able to execute the following script, cd into the project's root directory and give it executable permissions:
chmod +x ./scripts/*.shTo generate the data regarding the execution times evaluation and comparison,
launch the following shell script.
Take a look at the dependencies required to run the script.
./scripts/measures.sh [OPTION]...The script supports the definition of various options and/or parameters via command line arguments.
| Argument | Description |
|---|---|
| -h, --help | Show guide and quit. |
| -n N, --num-measures N | Run every measure N times. (default is 20)* |
| -d DIR, --dir DIR | Specify output directory. (default is ./output) |
| --no-table | Do not run the Python script to create the tables. |
* The higher the number, the more precise the mean value is.
Other parameters, like the block sizes to use or the array size(s), can be modified inside the script itself.
To compile source files without the help of the shell script, use the makefile.
Compile sources to generate parallel version that runs on NVIDIA GPUs:
make parallelOr compile serial version that runs on any CPU:
make serialIn both cases the executable file produced is bin/main.out.
Check and modify the makefile based on the GPU's Compute Capability and to toggle the OpenMP linking.
To delete object files and executables:
make cleanA list of HW & SW requirements to run the program and the scripts:
- NVIDIA GPU
- Bash Shell 4.2+
- nvcc 9+
- make
- OpenMP 4.5+
- Python 3.7+
The following modules are needed to handle CSV files and tables and perform some calculations:
- scipy
- numpy
- pandas
- prettytable
Use pip to install these with the following command:
pip install *module_name*Marco Plaitano
Distributed under the GPLv3 license.
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