89 lines
2.7 KiB
Plaintext
89 lines
2.7 KiB
Plaintext
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# Comparison between C-implemented and R-implemented dual-loop matrix summing function performance
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## Running
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To run this project, run the following commands:
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```bash
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# matrix test
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Rscript mat_tests.R
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```
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### Building and running
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If you edit the C code, to recompile run:
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```bash
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bash make_c.sh
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```
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### View Evaluation
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To install packages necessary for this .rmd document, run:
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```bash
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Rscript install_libs.R
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```
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## Evaluation
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The experiment shows the performance comparison between the R-implemented and C-implemented matrix summing functions
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for different matrix sizes. As the matrix size increases, the C-implemented function demonstrates significantly
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better performance compared to the R-implemented function.
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Surprisingly, the speedup remains fairly constant in relative terms, stabilizing at about 4x
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> Note: Evaluation script run on an AMD Ryzen 9 7950X3D cpu with enough RAM for all matrix sizes
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| Matrix size | sum1 run duration (secs) | sum2 run duration (secs) |
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|-------------|---------------------------|---------------------------|
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| 5x5 | 7.152557e-06 | 7.867813e-06 |
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| 10x10 | 9.775162e-06 | 5.00679e-06 |
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| 50x50 | 9.346008e-05 | 8.106232e-06 |
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| 100x100 | 0.0003376007 | 1.955032e-05 |
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| 500x500 | 0.007472992 | 0.001415014 |
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| 1000x1000 | 0.03007007 | 0.005748034 |
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| 5000x5000 | 0.6559205 | 0.1854615 |
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| 10000x10000 | 2.692389 | 0.6747584 |
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| 20000x20000 | 10.67763 | 2.615553 |
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| 30000x30000 | 24.33534 | 5.987761 |
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```{r diagram}
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library(ggplot2)
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library(dplyr)
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library(tidyr)
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# prepare data
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data <- tribble(
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~Matrix.size, ~R.sum, ~C.sum,
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"5", 7.152557e-06, 7.867813e-06,
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"10", 9.775162e-06, 5.00679e-06,
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"50", 9.346008e-05, 8.106232e-06,
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"100", 0.0003376007, 1.955032e-05,
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"500", 0.007472992, 0.001415014,
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"1000", 0.03007007, 0.005748034,
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"5000", 0.6559205, 0.1854615,
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"10000", 2.692389, 0.6747584,
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"20000", 10.67763, 2.615553,
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"30000", 24.33534, 5.987761
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)
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# Convert Matrix.size to factor with desired order
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data$Matrix.size <- factor(data$Matrix.size, levels = data$Matrix.size)
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# rearrange data
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data_long <- data %>%
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pivot_longer(cols = c(R.sum, C.sum),
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names_to = "Method",
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values_to = "Duration")
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# Create the plot
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ggplot(data_long, aes(x = Matrix.size, y = Duration, color = Method)) +
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geom_point() +
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scale_y_log10() +
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labs(x = "Matrix Size", y = "Duration (seconds)", color = "Method") +
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ggtitle("Calculation Time per (square) matrix size") +
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theme_minimal()
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```
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