---
output: rmarkdown::html_vignette
title: Loop-invariant Code Motion
vignette: >
  %\VignetteIndexEntry{Loop-invariant Code Motion}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r echo=FALSE, message=FALSE}
library("rco")
library("microbenchmark")
library("ggplot2")
autoplot.microbenchmark <- function(obj) {
  levels(obj$expr) <- paste0("Expr_", seq_along(levels(obj$expr)))
  microbenchmark:::autoplot.microbenchmark(obj)
}
speed_up <- function(obj) {
  levels(obj$expr) <- paste0("Expr_", seq_along(levels(obj$expr)))
  obj <- as.data.frame(obj)
  summaries <- do.call(rbind, by(obj$time, obj$expr, summary))
  res <- c()
  for (i in seq_len(nrow(summaries) - 1) + 1) {
    res <- rbind(res, summaries[1, ] / summaries[i, ])
  }
  rownames(res) <- levels(obj$expr)[-1]
  return(res)
}
```

# Loop-invariant Code Motion

## Background

Loop-invariant code consists of statements or expressions which can be moved outside the body of a loop without affecting the semantics of the program. Loop-invariant code motion is a compiler optimization which performs this movement automatically. 

For example, consider the following code:

```{r eval=FALSE}
i <- 0
n <- 1000
y <- rnorm(1)
z <- rnorm(1)
a <- c()
while (i < n) {
  x <- y + z
  a[i] <- 6 * i + x * x
  i <- i + 1
}
```

Here, `x` is assigned a value `y + z` that is constant in each loop execution. In this example, the assignment would be executed `n` times. The same result, but much faster, would be obtained by doing the assign outside the loop.

## Example

Consider the previous example to be automatically optimized.

```{r}
code <- paste(
  "i <- 0",
  "n <- 1000",
  "y <- rnorm(1)",
  "z <- rnorm(1)",
  "a <- c()",
  "while (i < n) {",
  "  x <- y + z",
  "  a[i] <- 6 * i + x * x",
  "  i <- i + 1",
  "}",
  sep = "\n"
)
cat(code)
```

Then, the automatically optimized code would be:

```{r}
opt_code <- opt_loop_invariant(list(code))
cat(opt_code$codes[[1]])
```

And if we measure the execution time of each one, and the speed-up:

```{r message=FALSE}
bmark_res <- microbenchmark({
  eval(parse(text = code))
}, {
  eval(parse(text = opt_code))
})
autoplot(bmark_res)
speed_up(bmark_res)
```

## Implementation

The `opt_loop_invariant` optimizer does:

* Finds `for` and `while` loops in the code.

* Discards loops that have function calls inside, as function calls can edit the environment.

* Gets which variables are variant inside the loop.

* Detects which expressions do not use any variant variable.

* Gets these invariant variables, and moves them outside the loop, but inside an `if` statement (with the same conditional as the loop).

## To-Do

* `if/else` code motion?
  
  Actually, the `opt_loop_invariant` does not consider `if/else` expressions to move. In this sense, the code:
  
  ```{r eval=FALSE}
  while (i < n) {
    if (n > 3) {
      something_without_i
    }
  }
  ```
  
  Will not be optimized to its equivalent code:
  
  ```{r eval=FALSE}
  if (i < n) {
    if (n > 3) {
      something_without_i
    }
  }
  while (i < n) {
  }
  ```

* Invariant subexpressions motion?
  
  Actually, the `opt_loop_invariant` considers only full expressions, it is not working on subexpressions, for instance, the code:
  
  ```{r eval=FALSE}
  while (i < n) {
    i <- (x * y) + 1
  }
  ```
  
  as `x` and `y` are invariant, could be optimized to:
  
  ```{r eval=FALSE}
  is_1 <- (x * y)
  while (i < n) {
    i <- is_1 + 1
  }
  ```

* Include `repeat` optimization?
  
  Since determining the conditional that causes a `repeat` loop to stop is not that simple, it is not easy to remove invariant variables and place them in an `if`.
  
  For example, the code:
  
  ```{r eval=FALSE}
  y <- 1
  repeat{
    if (y > 4) {
      break
    }
    x <- 8 * 8
    y <- y + 1
  }
  ```
  
  Must be optimized to:
  
  ```{r eval=FALSE}
  y <- 1
  if (y <= 4) {
    x <- 8 * 8
  }
  repeat{
    if (y > 4) {
      break
    }
    y <- y + 1
  }
  ```