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README.md

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+# Parallel matrix multiplication
+
+Performance tests for parallel matrix multiplication algorithms.
+
+## Usage
+
+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.
+
+```shell
+pip install -r requirements.txt
+```
+
+2. Build the matrix multiplication executables using `make`.
+
+```shell
+cd MatMult
+make
+```
+
+3. Run the experiments using the launcher.
+
+```shell
+cd ..
+python launcher.py experiments.csv
+```
+
+4. Generate the graphics, using the results of the experiments from every machine.
+
+```shell
+python graphics.py experiments1.csv experiments2.csv experiments3.csv -o img/
+```
+
+## Results
+
+The experimentation results are summarized in the following figures.
+
+![Matrix size vs Time line plot](img/size-time.png)
+![Threads vs Time line plot](img/threads-time.png)
+![Matrix size vs Threads heatmap](img/size-threads-time.png)
+![Matrix size vs Threads heatmap](img/distribution.png)
+
+In this case, the experiments were run in 3 different machines:
+
+- A ml.c5.9xlarge AWS instance, with 36 vCPUs and 72 GiB RAM.
+- A ml.m5.8xlarge AWS instance, with 32 vCPUs and 128 GiB RAM.
+- A ml.r5.8xlarge AWS instance, with 32 vCPUs and 128 GiB RAM.
+
+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.
+
+### Metrics
+
+The following metrics summarize the experimentation results, regarding the algorithms performance for different number of threads:
+
+- Speed up: $$Sp = \frac{t_{serial}}{t_{parallel}}$$
+
+![SpeedUp](img/speedup.png)
+
+- Efficiency: $$Ep = \frac{Sp}{p}$$
+
+![Efficiency](img/efficiency.png)