Bend

Bend is a programming language that can run massively parallel programs on the GPU or the CPU using the power of interaction nets and the HVM. With Bend, you can write programs for the GPU as easily as you'd write a normal program in your favorite language.

It is based on the Interaction-Calculus, a variation of the untyped lambda calculus that compiles efficiently to interaction nets.

Currently Bend only supports strict/eager evaluation. If you need lazy, optimal evaluation, we recommend using HVM1 for now.

Installation

With the nightly version of rust installed, clone the repository:

git clone https://github.com/HigherOrderCO/bend.git

cd bend

Install using cargo:

cargo install --path . --locked

If you want to run programs directly from Bend, you also need to have HVM installed.

Usage

Command	Usage	Description
Check	bend check <file>	Checks if a program is valid
Compile	bend compile <file>	Compiles a program to HVM and outputs it to stdout
Run	bend run <file>	Compiles and then runs a program in HVM
Normalize	bend norm <file>	Compiles and then normalizes a program in HVM, outputting the result to stdout
Desugar	bend desugar <file>	Desugars a program to the core syntax and outputs it to stdout

If you want to compile a file to a file, just redirect the output with >:

bend compile <file.bend> > <file.hvm>

There are many compiler options that can be passed through the CLI. You can see the list of options here.

Examples

Bend offers two flavors of syntax, a user-friendly python-like syntax (the default) and the core ML/Haskell-like syntax that's used internally by the compiler. You can read the full reference for both of them here, but these examples will use the first one.

To see some more complex examples programs, check out the examples folder.

We can start with a basic program that adds the numbers 3 and 2.

def main:
  return 2 + 3

Normalizing this program will show the number 5. Be careful with run and norm, since they will not show any warnings by default. Before running a new program it's useful to first check it.

Bend programs consist of a series of function definitions, always starting with a function called main or Main.

Functions can receive arguments both directly and using a lambda abstraction.

// These two are equivalent
def add(x, y):
  return x + y

def add2:
  return lambda x, y: x + y

You can then call this function like this:

def main:
  sum = add(2, 3)
  return sum

You can bundle multiple values into a single value using a tuple or a struct.

// With a tuple
def Tuple.fst(x):
  // This destructures the tuple into the two values it holds.
  // '*' means that the value is discarded and not bound to any variable.
  (fst, *) = x
  return fst

// With a struct
struct Pair(fst, snd):
def Pair.fst(x):
  match x:
    Pair:
      return x.fst

// We can also directly access the fields of a struct.
// This requires that we tell the compiler the type of the variable where it is defined.
def Pair.fst_2(x: Pair):
  return x.fst

For more complicated data structures, we can use enum to define a algebraic data types.

enum MyTree:
  Node(val, ~left, ~right)
  Leaf

We can then pattern match on the enum to perform different actions depending on the variant of the value.

def Maybe.or_default(x, default):
  match x:
    Maybe/some:
      // We can access the fields of the variant using 'matched.field'
      return x.val
    Maybe/none:
      return default

We use ~ to indicate that a field is recursive. This allows us to easily create and consume these recursive data structures with bend and fold:

def MyTree.sum(x):
  // Sum all the values in the tree.
  fold x:
    // The fold is implicitly called for fields marked with '~' in their definition.
    Node:
      return val + x.left + x.right
    Leaf:
      return 0

def main:
  bend val = 0 while val < 0:
    // 'go' calls the bend recursively with the provided values.
    x = Node(val=val, left=go(val + 1), right=go(val + 1))
  then:
    // 'then' is the base case, when the condition fails.
    x = Leaf

  return MyTree.sum(x)

These are equivalent to inline recursive functions that create a tree and consume it.

def MyTree.sum(x):
  match x:
    Node:
      return x.val + MyTree.sum(x.left) + MyTree.sum(x.right)
    Leaf:
      return 0

def main_bend(val):
  if val < 0:
    return Node(val, main_bend(val + 1), main_bend(val + 1))
  else:
    return Leaf

def main:
  return main_bend(0)

Making your program around trees is a very good way of making it parallelizable, since each core can be dispatched to work on a different branch of the tree.

Attention: Note that despite the ADT syntax sugars, Bend is an untyped language and the compiler will not stop you from using values incorrectly, which can lead to very unexpected results. For example, the following program will compile just fine even though != is only defined for native numbers:

def main:
  bend val = [0, 1, 2, 3] while val != []:
    match val:
      List.cons:
        x = val.head + go(val.tail)
      List.nil:
        x = 0
  then:
    x = 0
  return x

Normalizing this program will show λ* * and not the expected 6.

It's also important to note that Bend is linear (technically affine), meaning that every variable is only used once. When a variable is used more than once, the compiler will automatically insert a duplication. Duplications efficiently share the same value between two locations, only cloning a value when it's actually needed, but their exact behaviour is slightly more complicated than that and escapes normal lambda-calculus rules. You can read more about it in Dups and sups and learn how pattern matching avoids this problem in Pattern matching.

To use a variable twice without duplicating it, you can use a use statement. It inlines clones of some value in the statements that follow it.

def foo(x):
  use result = bar(1, x)
  return (result, result)

// Is equivalent to
def foo(x):
  return (bar(1, x), bar(1, x))

Note that any variable in the use will end up being duplicated.

Bend supports recursive functions of unrestricted depth:

def native_num_to_adt(n):
  if n == 0:
    return Nat.zero
  else:
    return Nat.succ(native_num_to_adt(n - 1))

If your recursive function is not based on pattern matching syntax (like if, match, fold, etc) you have to be careful to avoid an infinite loop. Since Bend is eagerly executed, some situations will cause function applications to always be expanded, which can lead to looping situations. You can read how to avoid this in Lazy definitions.

Bend has native numbers and operations.

def main:
  a = 1      // A 24 bit unsigned integer.
  b = +2     // A 24 bit signed integer.
  c = -3     // Another signed integer, but with negative value.
  d = 1.0    // A 24 bit floating point number.
  e = +0.001 // Also a float.
  return (a * 2, b - c, d / e)

switch pattern matches on unsigned native numbers:

switch x = 4:
  // From '0' to n, ending with the default case '_'.
  0:  "zero"
  1:  "one"
  2:  "two"
  // The default case binds the name <arg>-<n>
  // where 'arg' is the name of the argument and 'n' is the next number.
  // In this case, it's 'x-3', which will have value (4 - 3) = 1
  _:  String.concat("other: ", (String.from_num x-3))

Bend has Lists and Strings, which support Unicode characters.

def main:
  return ["You: Hello, 🌎", "🌎: Hello, user"]

A string is desugared to a String data type containing two constructors, String.cons and String.nil. List also becomes a type with two constructors, List.cons and List.nil.

// These two are equivalent
def StrEx:
  "Hello"

def ids:
  [1, 2, 3] 

// These types are builtin.
enum String:
  String.cons(head, tail)
  String.nil
enum List:
  List.cons(head, tail)
  List.nil
def StrEx:
  String.cons('H', String.cons('e', String.cons('l', String.cons('l', String.cons('o', String.nil)))))
def ids:
  List.cons(1, List.cons(2, List.cons(3, List.nil)))

Characters are delimited by ' ' and support Unicode escape sequences. They are encoded as a U24 with the unicode codepoint as their value.

// These two are equivalent
def chars:
  ['A', '\u{4242}', '🌎']

def chars2:
  [65, 0x4242, 0x1F30E]

More features

Key:

• 📗: Basic resources
• 📙: Intermediate resources
• 📕: Advanced resources

Other features are described in the following documentation files:

• 📗 Lazy definitions: Making recursive definitions lazy
• 📗 Data types: Defining data types
• 📗 Pattern matching: Pattern matching
• 📗 Native numbers and operations: Native numbers
• 📗 Builtin definitions: Builtin definitions
• 📗 CLI arguments: CLI arguments
• 📙 Duplications and superpositions: Dups and sups
• 📙 Scopeless lambdas: Using scopeless lambdas
• 📕: Fusing functions: Writing fusing functions

Further reading

• 📙 Compilation and readback
• 📙 Old HVM wiki learning material. It is outdated, but it can still teach you some of the basics.