60 lines
1.7 KiB
Markdown
60 lines
1.7 KiB
Markdown
# Parallel matrix multiplication
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Performance tests for parallel matrix multiplication algorithms.
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## Usage
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1. Install the required python libraries, found in `requirements.txt`. The following command shows how to do it using `pip`, but `conda` or any package manager can also be used.
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```shell
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pip install -r requirements.txt
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```
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2. Build the matrix multiplication executables using `make`.
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```shell
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cd MatMult
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make
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```
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3. Run the experiments using the launcher.
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```shell
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cd ..
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python launcher.py experiments.csv
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```
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4. Generate the graphics, using the results of the experiments from every machine.
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```shell
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python graphics.py experiments1.csv experiments2.csv experiments3.csv -o img/
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```
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## Results
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The experimentation results are summarized in the following figures.
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In this case, the experiments were run in 3 different machines:
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- A ml.c5.9xlarge AWS instance, with 36 vCPUs and 72 GiB RAM.
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- A ml.m5.8xlarge AWS instance, with 32 vCPUs and 128 GiB RAM.
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- A ml.r5.8xlarge AWS instance, with 32 vCPUs and 128 GiB RAM.
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Every matrix size - number of threads combination was executed 30 times, in order to make the experiments statistically significant. The values in the graphics represent the mean values of those 30 repetitions.
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### Metrics
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The following metrics summarize the experimentation results, regarding the algorithms performance for different number of threads:
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- Speed up: $$Sp = \frac{t_{serial}}{t_{parallel}}$$
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- Efficiency: $$Ep = \frac{Sp}{p}$$
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