High-order Virtual Machine (HVM)
=================================
**High-order Virtual Machine (HVM)** is a pure functional runtime that is **lazy**, **non-garbage-collected** and
**massively parallel**. It is also **beta-optimal**, meaning that, for higher-order computations, it can be
exponentially faster than alternatives, including Haskell's GHC.
That is possible due to a new model of computation, the **Interaction Net**, which supersedes the **Turing Machine** and
the **Lambda Calculus**. Previous implementations of this model have been inefficient in practice, however, a recent
breakthrough has drastically improved its efficiency, resulting in the HVM. Despite being relatively new, it already
beats mature compilers in many cases, and is set to scale towards uncharted levels of performance.
**Welcome to the massively parallel future of computers!**
Examples
========
Essentially, HVM is a minimalist functional language that is compiled to a novel runtime based on [Interaction
Nets](https://pdf.sciencedirectassets.com/272575/1-s2.0-S0890540100X00600/1-s2.0-S0890540197926432/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEEkaCXVzLWVhc3QtMSJGMEQCIDQQ1rA8jV9UQEKlaFzLfqINP%2B%2FrcTj4NCbI40n%2FrGbyAiBLG%2FpnaXmp6BGaG1Yplr3YYsHzxet6aQXc1qnkbb3W0irMBAhSEAUaDDA1OTAwMzU0Njg2NSIM1FOMCDHcoyvFFAU6KqkEgJPcH6B8%2BRYsdLtUERtVlwtcXZBW38xnvb%2FPRSkmxcNaK%2BmQTa7L3ZFuZt9rpNjrB3sJHg%2Bxc%2FqdAF%2FsthEb1NreHNze7LmbStuRufZCGEcxax%2FsyjnSb9bnrHuDEpnck1Dhk2YPqR8%2Bim%2BdQisDUp%2F4torZsCK1%2BPAEQkQAmGqinioexAr8dEE0BOlHgxBz5YRIkV9pjLoq%2FjWFqiUSO2bPdVi2AfpDbXI48ek6gQs%2F6VTIFRShfezfAr1HoDlQEoyyVYnVy6wI%2Fu1WVB%2FA0JJHK1B7rZFEYilPSAdUpVSOvjhNHN9elxIxlFX6hOZz3YJ4QDeLCPztfMClYYxAex6hoBBVzTkRzszs18hK1K%2FMUMwF4o%2FDy1i3WLeUmC36CL7WXDik%2BTZ7WjJNYGVRILH6cDsHrg17A0MVI5njvw7iM%2FrYKoOgBD2ESct4nO3mpRkKVq%2F9UyKScwVT5VrNpuLWLnrg29BDvE%2BDoFI6c71cisENjhIhGPNrBCQvZLNe1k%2BD54NyfqOe4a1DguuzxBnsNj6BBD2lM6TyDvCz9w36u194aN8oks9hLuTuKp7Rk05dTt6rj4pThkHA%2FQQymmx74MlQtTXTnD5v%2F%2BmGSUz6vHzqaV2Ft5xjWf9w9NJHfTkFkpxNEv8fTUUSMBEhL4nF8wj0wiNbSwp9NvPOj3YMIG2icNxdAZyNsJYJUowOCXi4JTwCkqb2WdNOi88pOSaAautZrBg7nzCKyuCbBjqqATOzXItndBn%2Be6oyH2l8sD%2B5v%2FjIqCz8%2Bx%2Bz%2FZA3dntddFac64iWFGPbJeRGw05BiPX5TKBnrR%2BmaqfO%2F7SxoYfTV4hl5Z2lmJcoiEd%2BWUmNK2wntMlGtFn%2FmFeeljKBeMxnfh8DN0qRz10NZAfxhvqxAEBu67G0ZXpECGxr8fAiBrdvnEac6rWfv8%2FT0VA%2Fu6xjIMIrrwU65xAuVuIG%2BXpsdC073VLm1%2BEW&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20221119T011901Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYYRK5XVMW%2F20221119%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=74892553e56ba432350974a6f4dbebfd97418e2187a5c4e183da61dd0e951609&hash=bc1de316d0b6ee58191106c1cdbc34d1eaeab536a9bbc02dfae09818a8cc2510&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S0890540197926432&tid=spdf-00500b38-a41c-4d5b-98bb-4a2754da3953&sid=17532fa99b4522476f2b00d636dc838e7e36gxrqa&type=client&ua=515904515402570a0401&rr=76c51d7eea7b4d36).
This approach is not only memory-efficient (no GC needed), but also has two significant advantages: **automatic
parallelism** and **beta-optimality**. The idea is that you write a simple functional program, and HVM will turn it into
a massively parallel, beta-optimal executable. The examples below highlight these advantages in action.
Bubble Sort
-----------
<table>
<tr>
<td>From: <a href="./examples/sort/bubble/main.hvm">HVM/examples/sort/bubble/main.hvm</a></td>
<td>From: <a href="./examples/sort/bubble/main.hs" >HVM/examples/sort/bubble/main.hs</a></td>
</tr>
<tr>
<td>
```javascript
// sort : List -> List
(Sort Nil) = Nil
(Sort (Cons x xs)) = (Insert x (Sort xs))
// Insert : U60 -> List -> List
(Insert v Nil) = (Cons v Nil)
(Insert v (Cons x xs)) = (SwapGT (> v x) v x xs)
// SwapGT : U60 -> U60 -> U60 -> List -> List
(SwapGT 0 v x xs) = (Cons v (Cons x xs))
(SwapGT 1 v x xs) = (Cons x (Insert v xs))
```
</td>
<td>
```haskell
sort' :: List -> List
sort' Nil = Nil
sort' (Cons x xs) = insert x (sort' xs)
insert :: Word64 -> List -> List
insert v Nil = Cons v Nil
insert v (Cons x xs) = swapGT (if v > x then 1 else 0) v x xs
swapGT :: Word64 -> Word64 -> Word64 -> List -> List
swapGT 0 v x xs = Cons v (Cons x xs)
swapGT 1 v x xs = Cons x (insert v xs)
```
</td>
</tr>
</table>
On this example, we run a simple, recursive [Bubble Sort](https://en.wikipedia.org/wiki/Bubble_sort) on both HVM and GHC
(Haskell's compiler). Notice the algorithms are identical. The chart shows how much time each runtime took to sort a
list of given size (the lower, the better). The purple line shows GHC (single-thread), the green lines show HVM (1, 2, 4
and 8 threads). As you can see, both perform similarly, with HVM having a small edge. Sadly, here, its performance
doesn't improve with added cores. That's because Bubble Sort is an *inherently sequential* algorithm, so HVM can't
improve it.
Radix Sort
----------
<table>
<tr>
<td>From: <a href="./examples/sort/radix/main.hvm">HVM/examples/sort/radix/main.hvm</a></td>
<td>From: <a href="./examples/sort/radix/main.hs" >HVM/examples/sort/radix/main.hs</a></td>
</tr>
<tr>
<td>
```javascript
// Sort : Arr -> Arr
(Sort t) = (ToArr 0 (ToMap t))
// ToMap : Arr -> Map
(ToMap Null) = Free
(ToMap (Leaf a)) = (Radix a)
(ToMap (Node a b)) =
(Merge (ToMap a) (ToMap b))
// ToArr : Map -> Arr
(ToArr x Free) = Null
(ToArr x Used) = (Leaf x)
(ToArr x (Both a b)) =
let a = (ToArr (+ (* x 2) 0) a)
let b = (ToArr (+ (* x 2) 1) b)
(Node a b)
// Merge : Map -> Map -> Map
(Merge Free Free) = Free
(Merge Free Used) = Used
(Merge Used Free) = Used
(Merge Used Used) = Used
(Merge Free (Both c d)) = (Both c d)
(Merge (Both a b) Free) = (Both a b)
(Merge (Both a b) (Both c d)) =
(Both (Merge a c) (Merge b d))
```
</td>
<td>
```haskell
sort :: Arr -> Arr
sort t = toArr 0 (toMap t)
toMap :: Arr -> Map
toMap Null = Free
toMap (Leaf a) = radix a
toMap (Node a b) =
merge (toMap a) (toMap b)
toArr :: Word64 -> Map -> Arr
toArr x Free = Null
toArr x Used = Leaf x
toArr x (Both a b) =
let a' = toArr (x * 2 + 0) a
b' = toArr (x * 2 + 1) b
in Node a' b'
merge :: Map -> Map -> Map
merge Free Free = Free
merge Free Used = Used
merge Used Free = Used
merge Used Used = Used
merge Free (Both c d) = (Both c d)
merge (Both a b) Free = (Both a b)
merge (Both a b) (Both c d) =
(Both (merge a c) (merge b d))
```
</td>
</tr>
</table>
On this example, we try a [Radix Sort](https://en.wikipedia.org/wiki/Radix_sort), based on merging immutable trees. In
this test, for now, single-thread performance was superior on GHC - and this is often the case, since GHC is much older
and has astronomically more micro-optimizations - yet, since this algorithm is *inherently parallel*, HVM was able to
outperform GHC given enough cores. With **8 threads**, HVM sorted a large list **2.5x faster** than GHC.
Keep in mind one could parallelize the Haskell version with `par` annotations, but that would demand time-consuming,
expensive refactoring - and, in some cases, it isn't even *possible* to use all the available parallelism with `par`
alone. HVM, on the other hands, will automatically distribute parallel workloads through all available cores, achieving
horizontal scalability. As HVM matures, the single-thread gap will decrease significantly.
Lambda Multiplication
---------------------
<table>
<tr>
<td>From: <a href="./examples/lambda/multiplication/main.hvm">HVM/examples/lambda/multiplication/main.hvm </a></td>
<td>From: <a href="./examples/lambda/multiplication/main.hs" >HVM/examples/lambda/multiplication/main.hs </a></td>
</tr>
<tr>
<td>
```javascript
// Increments a Bits by 1
// Inc : Bits -> Bits
(Inc xs) = λex λox λix
let e = ex
let o = ix
let i = λp (ox (Inc p))
(xs e o i)
// Adds two Bits
// Add : Bits -> Bits -> Bits
(Add xs ys) = (App xs λx(Inc x) ys)
// Multiplies two Bits
// Mul : Bits -> Bits -> Bits
(Mul xs ys) =
let e = End
let o = λp (B0 (Mul p ys))
let i = λp (Add ys (B0 (Mul p ys)))
(xs e o i)
```
</td>
<td>
```haskell
-- Increments a Bits by 1
inc :: Bits -> Bits
inc xs = Bits $ \ex -> \ox -> \ix ->
let e = ex
o = ix
i = \p -> ox (inc p)
in get xs e o i
-- Adds two Bits
add :: Bits -> Bits -> Bits
add xs ys = app xs (\x -> inc x) ys
-- Muls two Bits
mul :: Bits -> Bits -> Bits
mul xs ys =
let e = end
o = \p -> b0 (mul p ys)
i = \p -> add ys (b0 (mul p ys))
in get xs e o i
```
</td>
</tr>
</table>
This example implements bitwise multiplication using [λ-encodings](https://en.wikipedia.org/wiki/Church_encoding). Its
purpose is to show yet another important advantage of HVM: beta-optimality. This chart isn't wrong: HVM multiplies
λ-encoded numbers **exponentially faster** than GHC, since it can deal with very higher-order programs with optimal
asymptotics, while GHC can not. As esoteric as this technique may look, it can actually be very useful to design
efficient functional algorithms. One application, for example, is to implement [runtime
deforestation](https://github.com/Kindelia/HVM/issues/167#issuecomment-1314665474) for immutable datatypes. In general,
HVM is capable of applying any fusible function `2^n` times in linear time, which sounds impossible, but is indeed true.
*Charts made on [plotly.com](https://chart-studio.plotly.com/).*
Getting Started
===============
1. Install Rust nightly:
```
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
rustup default nightly
```
2. Install HVM:
```
cargo install hvm
```
3. Run an HVM expression:
```
hvm run "(@x(+ x 1) 41)"
```
That's it! For more advanced usage, check the [complete guide](guide/README.md).
More Information
================
- To learn more about the **underlying tech**, check [guide/HOW.md](guide/HOW.md).
- To ask questions and **join our community**, check our [Discord Server](https://discord.gg/kindelia).
- To **contact the author** directly, send an email to <taelin@kindelia.org>.