No.
Since many people are familiar with the syntax of other dynamic languages, and lots of code has already been written in those languages, it is natural to wonder why we didn't just plug a Matlab or Python front-end into a Julia back-end (or “transpile” code to Julia) in order to get all the performance benefits of Julia without requiring programmers to learn a new language. Simple, right?
The basic issue is that there is nothing special about Julia's compiler: we use a commonplace compiler (LLVM) with no “secret sauce” that other language developers don't know about. Indeed, Julia's compiler is in many ways much simpler than those of other dynamic languages (e.g. PyPy or LuaJIT). Julia's performance advantage derives almost entirely from its front-end: its language semantics allow a [well-written Julia program](@ref man-performance-tips) to give more opportunities to the compiler to generate efficient code and memory layouts. If you tried to compile Matlab or Python code to Julia, our compiler would be limited by the semantics of Matlab or Python to producing code no better than that of existing compilers for those languages (and probably worse). The key role of semantics is also why several existing Python compilers (like Numba and Pythran) only attempt to optimize a small subset of the language (e.g. operations on Numpy arrays and scalars), and for this subset they are already doing at least as well as we could for the same semantics. The people working on those projects are incredibly smart and have accomplished amazing things, but retrofitting a compiler onto a language that was designed to be interpreted is a very difficult problem.
Julia's advantage is that good performance is not limited to a small subset of “built-in” types and operations, and one can write high-level type-generic code that works on arbitrary user-defined types while remaining fast and memory-efficient. Types in languages like Python simply don't provide enough information to the compiler for similar capabilities, so as soon as you used those languages as a Julia front-end you would be stuck.
For similar reasons, automated translation to Julia would also typically generate unreadable, slow, non-idiomatic code that would not be a good starting point for a native Julia port from another language.
On the other hand, language interoperability is extremely useful: we want to exploit existing high-quality code in other languages from Julia (and vice versa)! The best way to enable this is not a transpiler, but rather via easy inter-language calling facilities. We have worked hard on this, from the built-in ccall intrinsic (to call C and Fortran libraries) to JuliaInterop packages that connect Julia to Python, Matlab, C++, and more.
Julia's public API is the behavior described in
documentation of public symbols from Base and the standard libraries. Functions,
types, and constants are not part of the public API if they are not public, even if
they have docstrings or are described in the documentation. Further, only the documented
behavior of public symbols is part of the public API. Undocumented behavior of public
symbols is internal.
Public symbols are those marked with either public foo or export foo.
In other words:
- Documented behavior of public symbols is part of the public API.
- Undocumented behavior of public symbols is not part of the public API.
- Documented behavior of private symbols is not part of the public API.
- Undocumented behavior of private symbols is not part of the public API.
You can get a complete list of the public symbols from a module with names(MyModule).
Package authors are encouraged to define their public API similarly.
Anything in Julia's Public API is covered by SemVer and therefore will not be removed or receive meaningful breaking changes before Julia 2.0.
Updating Julia may break your code if you use non-public API. If the code is self-contained, it may be a good idea to copy it into your project. If you want to rely on a complex non-public API, especially when using it from a stable package, it is a good idea to open an issue or pull request to start a discussion for turning it into a public API. However, we do not discourage the attempt to create packages that expose stable public interfaces while relying on non-public implementation details of Julia and buffering the differences across different Julia versions.
Please open an issue or pull request to start a discussion for turning the existing behavior into a public API.
Julia does not have an analog of MATLAB's clear function; once a name is defined in a Julia
session (technically, in module Main), it is always present.
If memory usage is your concern, you can always replace objects with ones that consume less memory.
For example, if A is a gigabyte-sized array that you no longer need, you can free the memory
with A = nothing. The memory will be released the next time the garbage collector runs; you can force
this to happen with [GC.gc()](@ref Base.GC.gc). Moreover, an attempt to use A will likely result in an error, because most methods are not defined on type Nothing.
Perhaps you've defined a type and then realize you need to add a new field. If you try this at the REPL, you get the error:
ERROR: invalid redefinition of constant MyType
Types in module Main cannot be redefined.
While this can be inconvenient when you are developing new code, there's an excellent workaround.
Modules can be replaced by redefining them, and so if you wrap all your new code inside a module
you can redefine types and constants. You can't import the type names into Main and then expect
to be able to redefine them there, but you can use the module name to resolve the scope. In other
words, while developing you might use a workflow something like this:
include("mynewcode.jl") # this defines a module MyModule
obj1 = MyModule.ObjConstructor(a, b)
obj2 = MyModule.somefunction(obj1)
# Got an error. Change something in "mynewcode.jl"
include("mynewcode.jl") # reload the module
obj1 = MyModule.ObjConstructor(a, b) # old objects are no longer valid, must reconstruct
obj2 = MyModule.somefunction(obj1) # this time it worked!
obj3 = MyModule.someotherfunction(obj2, c)
...When a file is run as the main script using julia file.jl one might want to activate extra
functionality like command line argument handling. A way to determine that a file is run in
this fashion is to check if abspath(PROGRAM_FILE) == @__FILE__ is true.
However, it is recommended to not write files that double as a script and as an importable library. If one needs functionality both available as a library and a script, it is better to write is as a library, then import the functionality into a distinct script.
Running a Julia script using julia file.jl does not throw
InterruptException when you try to terminate it with CTRL-C
(SIGINT). To run a certain code before terminating a Julia script,
which may or may not be caused by CTRL-C, use atexit.
Alternatively, you can use julia -e 'include(popfirst!(ARGS))' file.jl to execute a script while being able to catch
InterruptException in the try block.
Note that with this strategy PROGRAM_FILE will not be set.
Passing options to julia in a so-called shebang line, as in
#!/usr/bin/env julia --startup-file=no, will not work on many
platforms (BSD, macOS, Linux) where the kernel, unlike the shell, does
not split arguments at space characters. The option env -S, which
splits a single argument string into multiple arguments at spaces,
similar to a shell, offers a simple workaround:
#!/usr/bin/env -S julia --color=yes --startup-file=no
@show ARGS # put any Julia code here!!! note
Option env -S appeared in FreeBSD 6.0 (2005), macOS Sierra (2016)
and GNU/Linux coreutils 8.30 (2018).
Julia's run function launches external programs directly, without
invoking an operating-system shell
(unlike the system("...") function in other languages like Python, R, or C).
That means that run does not perform wildcard expansion of * ("globbing"),
nor does it interpret shell pipelines like | or >.
You can still do globbing and pipelines using Julia features, however. For example, the built-in
pipeline function allows you to chain external programs and files, similar to shell pipes, and
the Glob.jl package implements POSIX-compatible globbing.
You can, of course, run programs through the shell by explicitly passing a shell and a command string to run,
e.g. run(`sh -c "ls > files.txt"`) to use the Unix Bourne shell,
but you should generally prefer pure-Julia scripting like run(pipeline(`ls`, "files.txt")).
The reason why we avoid the shell by default is that shelling out sucks:
launching processes via the shell is slow, fragile to quoting of special characters, has poor error handling, and is
problematic for portability. (The Python developers came to a similar conclusion.)
You might have something like:
x = 0
while x < 10
x += 1
end
and notice that it works fine in an interactive environment (like the Julia REPL),
but gives UndefVarError: `x` not defined when you try to run it in script or other
file. What is going on is that Julia generally requires you to be explicit about assigning to global variables in a local scope.
Here, x is a global variable, while defines a [local scope](@ref scope-of-variables), and x += 1 is
an assignment to a global in that local scope.
As mentioned above, Julia (version 1.5 or later) allows you to omit the global
keyword for code in the REPL (and many other interactive environments), to simplify
exploration (e.g. copy-pasting code from a function to run interactively).
However, once you move to code in files, Julia requires a more disciplined approach
to global variables. You have least three options:
- Put the code into a function (so that
xis a local variable in a function). In general, it is good software engineering to use functions rather than global scripts (search online for "why global variables bad" to see many explanations). In Julia, global variables are also [slow](@ref man-performance-tips). - Wrap the code in a
letblock. (This makesxa local variable within thelet ... endstatement, again eliminating the need forglobal). - Explicitly mark
xasglobalinside the local scope before assigning to it, e.g. writeglobal x += 1.
More explanation can be found in the manual section [on soft scope](@ref on-soft-scope).
I passed an argument x to a function, modified it inside that function, but on the outside, the variable x is still unchanged. Why?
Suppose you call a function like this:
julia> x = 10
10
julia> function change_value!(y)
y = 17
end
change_value! (generic function with 1 method)
julia> change_value!(x)
17
julia> x # x is unchanged!
10
In Julia, the binding of a variable x cannot be changed by passing x as an argument to a function.
When calling change_value!(x) in the above example, y is a newly created variable, bound initially
to the value of x, i.e. 10; then y is rebound to the constant 17, while the variable
x of the outer scope is left untouched.
However, if x is bound to an object of type Array
(or any other mutable type). From within the function, you cannot "unbind" x from this Array,
but you can change its content. For example:
julia> x = [1,2,3]
3-element Vector{Int64}:
1
2
3
julia> function change_array!(A)
A[1] = 5
end
change_array! (generic function with 1 method)
julia> change_array!(x)
5
julia> x
3-element Vector{Int64}:
5
2
3
Here we created a function change_array!, that assigns 5 to the first element of the passed
array (bound to x at the call site, and bound to A within the function). Notice that, after
the function call, x is still bound to the same array, but the content of that array changed:
the variables A and x were distinct bindings referring to the same mutable Array object.
No, you are not allowed to have a using or import statement inside a function. If you want
to import a module but only use its symbols inside a specific function or set of functions, you
have two options:
-
Use
import:import Foo function bar(...) # ... refer to Foo symbols via Foo.baz ... end
This loads the module
Fooand defines a variableFoothat refers to the module, but does not import any of the other symbols from the module into the current namespace. You refer to theFoosymbols by their qualified namesFoo.baretc. -
Wrap your function in a module:
module Bar export bar using Foo function bar(...) # ... refer to Foo.baz as simply baz .... end end using Bar
This imports all the symbols from
Foo, but only inside the moduleBar.
Many newcomers to Julia find the use of ... operator confusing. Part of what makes the ...
operator confusing is that it means two different things depending on context.
In the context of function definitions, the ... operator is used to combine many different arguments
into a single argument. This use of ... for combining many different arguments into a single
argument is called slurping:
julia> function printargs(args...)
println(typeof(args))
for (i, arg) in enumerate(args)
println("Arg #$i = $arg")
end
end
printargs (generic function with 1 method)
julia> printargs(1, 2, 3)
Tuple{Int64, Int64, Int64}
Arg #1 = 1
Arg #2 = 2
Arg #3 = 3
If Julia were a language that made more liberal use of ASCII characters, the slurping operator
might have been written as <-... instead of ....
In contrast to the use of the ... operator to denote slurping many different arguments into
one argument when defining a function, the ... operator is also used to cause a single function
argument to be split apart into many different arguments when used in the context of a function
call. This use of ... is called splatting:
julia> function threeargs(a, b, c)
println("a = $a::$(typeof(a))")
println("b = $b::$(typeof(b))")
println("c = $c::$(typeof(c))")
end
threeargs (generic function with 1 method)
julia> x = [1, 2, 3]
3-element Vector{Int64}:
1
2
3
julia> threeargs(x...)
a = 1::Int64
b = 2::Int64
c = 3::Int64
If Julia were a language that made more liberal use of ASCII characters, the splatting operator
might have been written as ...-> instead of ....
The operator = always returns the right-hand side, therefore:
julia> function threeint()
x::Int = 3.0
x # returns variable x
end
threeint (generic function with 1 method)
julia> function threefloat()
x::Int = 3.0 # returns 3.0
end
threefloat (generic function with 1 method)
julia> threeint()
3
julia> threefloat()
3.0
and similarly:
julia> function twothreetup()
x, y = [2, 3] # assigns 2 to x and 3 to y
x, y # returns a tuple
end
twothreetup (generic function with 1 method)
julia> function twothreearr()
x, y = [2, 3] # returns an array
end
twothreearr (generic function with 1 method)
julia> twothreetup()
(2, 3)
julia> twothreearr()
2-element Vector{Int64}:
2
3
It means that the type of the output is predictable from the types of the inputs. In particular, it means that the type of the output cannot vary depending on the values of the inputs. The following code is not type-stable:
julia> function unstable(flag::Bool)
if flag
return 1
else
return 1.0
end
end
unstable (generic function with 1 method)
It returns either an Int or a Float64 depending on the value of its argument.
Since Julia can't predict the return type of this function at compile-time, any computation
that uses it must be able to cope with values of both types, which makes it hard to produce
fast machine code.
[Why does Julia give a DomainError for certain seemingly-sensible operations?](@id faq-domain-errors)
Certain operations make mathematical sense but result in errors:
julia> sqrt(-2.0)
ERROR: DomainError with -2.0:
sqrt was called with a negative real argument but will only return a complex result if called with a complex argument. Try sqrt(Complex(x)).
Stacktrace:
[...]
This behavior is an inconvenient consequence of the requirement for type-stability. In the case
of sqrt, most users want sqrt(2.0) to give a real number, and would be unhappy if
it produced the complex number 1.4142135623730951 + 0.0im. One could write the sqrt
function to switch to a complex-valued output only when passed a negative number (which is what
sqrt does in some other languages), but then the result would not be [type-stable](@ref man-type-stability)
and the sqrt function would have poor performance.
In these and other cases, you can get the result you want by choosing an input type that conveys your willingness to accept an output type in which the result can be represented:
julia> sqrt(-2.0+0im)
0.0 + 1.4142135623730951im
The parameters of a [parametric type](@ref Parametric-Types) can hold either
types or bits values, and the type itself chooses how it makes use of these parameters.
For example, Array{Float64, 2} is parameterized by the type Float64 to express its
element type and the integer value 2 to express its number of dimensions. When
defining your own parametric type, you can use subtype constraints to declare that a
certain parameter must be a subtype (<:) of some abstract type or a previous
type parameter. There is not, however, a dedicated syntax to declare that a parameter
must be a value of a given type — that is, you cannot directly declare that a
dimensionality-like parameter isa Int within the struct definition, for
example. Similarly, you cannot do computations (including simple things like addition
or subtraction) on type parameters. Instead, these sorts of constraints and
relationships may be expressed through additional type parameters that are computed
and enforced within the type's [constructors](@ref man-constructors).
As an example, consider
struct ConstrainedType{T,N,N+1} # NOTE: INVALID SYNTAX
A::Array{T,N}
B::Array{T,N+1}
endwhere the user would like to enforce that the third type parameter is always the second plus one. This can be implemented with an explicit type parameter that is checked by an [inner constructor method](@ref man-inner-constructor-methods) (where it can be combined with other checks):
struct ConstrainedType{T,N,M}
A::Array{T,N}
B::Array{T,M}
function ConstrainedType(A::Array{T,N}, B::Array{T,M}) where {T,N,M}
N + 1 == M || throw(ArgumentError("second argument should have one more axis" ))
new{T,N,M}(A, B)
end
endThis check is usually costless, as the compiler can elide the check for valid concrete types. If the second argument is also computed, it may be advantageous to provide an [outer constructor method](@ref man-outer-constructor-methods) that performs this calculation:
ConstrainedType(A) = ConstrainedType(A, compute_B(A))Julia uses machine arithmetic for integer computations. This means that the range of Int values
is bounded and wraps around at either end so that adding, subtracting and multiplying integers
can overflow or underflow, leading to some results that can be unsettling at first:
julia> x = typemax(Int)
9223372036854775807
julia> y = x+1
-9223372036854775808
julia> z = -y
-9223372036854775808
julia> 2*z
0
Clearly, this is far from the way mathematical integers behave, and you might think it less than ideal for a high-level programming language to expose this to the user. For numerical work where efficiency and transparency are at a premium, however, the alternatives are worse.
One alternative to consider would be to check each integer operation for overflow and promote
results to bigger integer types such as Int128 or BigInt in the case of overflow.
Unfortunately, this introduces major overhead on every integer operation (think incrementing a
loop counter) – it requires emitting code to perform run-time overflow checks after arithmetic
instructions and branches to handle potential overflows. Worse still, this would cause every computation
involving integers to be type-unstable. As we mentioned above, [type-stability is crucial](@ref man-type-stability)
for effective generation of efficient code. If you can't count on the results of integer operations
being integers, it's impossible to generate fast, simple code the way C and Fortran compilers
do.
A variation on this approach, which avoids the appearance of type instability is to merge the
Int and BigInt types into a single hybrid integer type, that internally changes representation
when a result no longer fits into the size of a machine integer. While this superficially avoids
type-instability at the level of Julia code, it just sweeps the problem under the rug by foisting
all of the same difficulties onto the C code implementing this hybrid integer type. This approach
can be made to work and can even be made quite fast in many cases, but has several drawbacks.
One problem is that the in-memory representation of integers and arrays of integers no longer
match the natural representation used by C, Fortran and other languages with native machine integers.
Thus, to interoperate with those languages, we would ultimately need to introduce native integer
types anyway. Any unbounded representation of integers cannot have a fixed number of bits, and
thus cannot be stored inline in an array with fixed-size slots – large integer values will always
require separate heap-allocated storage. And of course, no matter how clever a hybrid integer
implementation one uses, there are always performance traps – situations where performance degrades
unexpectedly. Complex representation, lack of interoperability with C and Fortran, the inability
to represent integer arrays without additional heap storage, and unpredictable performance characteristics
make even the cleverest hybrid integer implementations a poor choice for high-performance numerical
work.
An alternative to using hybrid integers or promoting to BigInts is to use saturating integer arithmetic, where adding to the largest integer value leaves it unchanged and likewise for subtracting from the smallest integer value. This is precisely what Matlab™ does:
>> int64(9223372036854775807)
ans =
9223372036854775807
>> int64(9223372036854775807) + 1
ans =
9223372036854775807
>> int64(-9223372036854775808)
ans =
-9223372036854775808
>> int64(-9223372036854775808) - 1
ans =
-9223372036854775808
At first blush, this seems reasonable enough since 9223372036854775807 is much closer to 9223372036854775808
than -9223372036854775808 is and integers are still represented with a fixed size in a natural
way that is compatible with C and Fortran. Saturated integer arithmetic, however, is deeply problematic.
The first and most obvious issue is that this is not the way machine integer arithmetic works,
so implementing saturated operations requires emitting instructions after each machine integer
operation to check for underflow or overflow and replace the result with typemin(Int)
or typemax(Int) as appropriate. This alone expands each integer operation from a single,
fast instruction into half a dozen instructions, probably including branches. Ouch. But it gets
worse – saturating integer arithmetic isn't associative. Consider this Matlab computation:
>> n = int64(2)^62
4611686018427387904
>> n + (n - 1)
9223372036854775807
>> (n + n) - 1
9223372036854775806
This makes it hard to write many basic integer algorithms since a lot of common techniques depend
on the fact that machine addition with overflow is associative. Consider finding the midpoint
between integer values lo and hi in Julia using the expression (lo + hi) >>> 1:
julia> n = 2^62
4611686018427387904
julia> (n + 2n) >>> 1
6917529027641081856
See? No problem. That's the correct midpoint between 2^62 and 2^63, despite the fact that n + 2n
is -4611686018427387904. Now try it in Matlab:
>> (n + 2*n)/2
ans =
4611686018427387904
Oops. Adding a >>> operator to Matlab wouldn't help, because saturation that occurs when adding
n and 2n has already destroyed the information necessary to compute the correct midpoint.
Not only is lack of associativity unfortunate for programmers who cannot rely it for techniques
like this, but it also defeats almost anything compilers might want to do to optimize integer
arithmetic. For example, since Julia integers use normal machine integer arithmetic, LLVM is free
to aggressively optimize simple little functions like f(k) = 5k-1. The machine code for this
function is just this:
julia> code_native(f, Tuple{Int})
.text
Filename: none
pushq %rbp
movq %rsp, %rbp
Source line: 1
leaq -1(%rdi,%rdi,4), %rax
popq %rbp
retq
nopl (%rax,%rax)The actual body of the function is a single leaq instruction, which computes the integer multiply
and add at once. This is even more beneficial when f gets inlined into another function:
julia> function g(k, n)
for i = 1:n
k = f(k)
end
return k
end
g (generic function with 1 methods)
julia> code_native(g, Tuple{Int,Int})
.text
Filename: none
pushq %rbp
movq %rsp, %rbp
Source line: 2
testq %rsi, %rsi
jle L26
nopl (%rax)
Source line: 3
L16:
leaq -1(%rdi,%rdi,4), %rdi
Source line: 2
decq %rsi
jne L16
Source line: 5
L26:
movq %rdi, %rax
popq %rbp
retq
nopSince the call to f gets inlined, the loop body ends up being just a single leaq instruction.
Next, consider what happens if we make the number of loop iterations fixed:
julia> function g(k)
for i = 1:10
k = f(k)
end
return k
end
g (generic function with 2 methods)
julia> code_native(g,(Int,))
.text
Filename: none
pushq %rbp
movq %rsp, %rbp
Source line: 3
imulq $9765625, %rdi, %rax # imm = 0x9502F9
addq $-2441406, %rax # imm = 0xFFDABF42
Source line: 5
popq %rbp
retq
nopw %cs:(%rax,%rax)Because the compiler knows that integer addition and multiplication are associative and that multiplication distributes over addition – neither of which is true of saturating arithmetic – it can optimize the entire loop down to just a multiply and an add. Saturated arithmetic completely defeats this kind of optimization since associativity and distributivity can fail at each loop iteration, causing different outcomes depending on which iteration the failure occurs in. The compiler can unroll the loop, but it cannot algebraically reduce multiple operations into fewer equivalent operations.
The most reasonable alternative to having integer arithmetic silently overflow is to do checked arithmetic everywhere, raising errors when adds, subtracts, and multiplies overflow, producing values that are not value-correct. In this blog post, Dan Luu analyzes this and finds that rather than the trivial cost that this approach should in theory have, it ends up having a substantial cost due to compilers (LLVM and GCC) not gracefully optimizing around the added overflow checks. If this improves in the future, we could consider defaulting to checked integer arithmetic in Julia, but for now, we have to live with the possibility of overflow.
In the meantime, overflow-safe integer operations can be achieved through the use of external libraries such as SaferIntegers.jl. Note that, as stated previously, the use of these libraries significantly increases the execution time of code using the checked integer types. However, for limited usage, this is far less of an issue than if it were used for all integer operations. You can follow the status of the discussion here.
As the error states, an immediate cause of an UndefVarError on a remote node is that a binding
by that name does not exist. Let us explore some of the possible causes.
julia> module Foo
foo() = remotecall_fetch(x->x, 2, "Hello")
end
julia> Foo.foo()
ERROR: On worker 2:
UndefVarError: `Foo` not defined in `Main`
Stacktrace:
[...]The closure x->x carries a reference to Foo, and since Foo is unavailable on node 2,
an UndefVarError is thrown.
Globals under modules other than Main are not serialized by value to the remote node. Only a reference is sent.
Functions which create global bindings (except under Main) may cause an UndefVarError to be thrown later.
julia> @everywhere module Foo
function foo()
global gvar = "Hello"
remotecall_fetch(()->gvar, 2)
end
end
julia> Foo.foo()
ERROR: On worker 2:
UndefVarError: `gvar` not defined in `Main.Foo`
Stacktrace:
[...]In the above example, @everywhere module Foo defined Foo on all nodes. However the call to Foo.foo() created
a new global binding gvar on the local node, but this was not found on node 2 resulting in an UndefVarError error.
Note that this does not apply to globals created under module Main. Globals under module Main are serialized
and new bindings created under Main on the remote node.
julia> gvar_self = "Node1"
"Node1"
julia> remotecall_fetch(()->gvar_self, 2)
"Node1"
julia> remotecall_fetch(varinfo, 2)
name size summary
––––––––– –––––––– –––––––
Base Module
Core Module
Main Module
gvar_self 13 bytes StringThis does not apply to function or struct declarations. However, anonymous functions bound to global
variables are serialized as can be seen below.
julia> bar() = 1
bar (generic function with 1 method)
julia> remotecall_fetch(bar, 2)
ERROR: On worker 2:
UndefVarError: `#bar` not defined in `Main`
[...]
julia> anon_bar = ()->1
(::#21) (generic function with 1 method)
julia> remotecall_fetch(anon_bar, 2)
1As you'll see if you try this, the result is a MethodError:
julia> foo(x::Vector{Real}) = 42
foo (generic function with 1 method)
julia> foo([1])
ERROR: MethodError: no method matching foo(::Vector{Int64})
The function `foo` exists, but no method is defined for this combination of argument types.
Closest candidates are:
foo(!Matched::Vector{Real})
@ Main none:1
Stacktrace:
[...]
This is because Vector{Real} is not a supertype of Vector{Int}! You can solve this problem with something
like foo(bar::Vector{T}) where {T<:Real} (or the short form foo(bar::Vector{<:Real}) if the static parameter T
is not needed in the body of the function). The T is a wild card: you first specify that it must be a
subtype of Real, then specify the function takes a Vector of with elements of that type.
This same issue goes for any composite type Comp, not just Vector. If Comp has a parameter declared of
type Y, then another type Comp2 with a parameter of type X<:Y is not a subtype of Comp. This is
type-invariance (by contrast, Tuple is type-covariant in its parameters). See [Parametric Composite
Types](@ref man-parametric-composite-types) for more explanation of these.
The [main argument](@ref man-concatenation) against + is that string concatenation is not
commutative, while + is generally used as a commutative operator. While the Julia community
recognizes that other languages use different operators and * may be unfamiliar for some
users, it communicates certain algebraic properties.
Note that you can also use string(...) to concatenate strings (and other values converted
to strings); similarly, repeat can be used instead of ^ to repeat strings. The
[interpolation syntax](@ref string-interpolation) is also useful for constructing strings.
There are several differences between using and import
(see the Modules section),
but there is an important difference that may not seem intuitive at first glance,
and on the surface (i.e. syntax-wise) it may seem very minor. When loading modules with using,
you need to say function Foo.bar(... to extend module Foo's function bar with a new method,
but with import Foo.bar, you only need to say function bar(... and it automatically extends
module Foo's function bar.
The reason this is important enough to have been given separate syntax is that you don't want
to accidentally extend a function that you didn't know existed, because that could easily cause
a bug. This is most likely to happen with a method that takes a common type like a string or integer,
because both you and the other module could define a method to handle such a common type. If you
use import, then you'll replace the other module's implementation of bar(s::AbstractString)
with your new implementation, which could easily do something completely different (and break
all/many future usages of the other functions in module Foo that depend on calling bar).
Unlike many languages (for example, C and Java), Julia objects cannot be "null" by default.
When a reference (variable, object field, or array element) is uninitialized, accessing it
will immediately throw an error. This situation can be detected using the
isdefined or [isassigned](@ref Base.isassigned) functions.
Some functions are used only for their side effects, and do not need to return a value. In these
cases, the convention is to return the value nothing, which is just a singleton object of type
Nothing. This is an ordinary type with no fields; there is nothing special about it except for
this convention, and that the REPL does not print anything for it. Some language constructs that
would not otherwise have a value also yield nothing, for example if false; end.
For situations where a value x of type T exists only sometimes, the Union{T, Nothing}
type can be used for function arguments, object fields and array element types
as the equivalent of Nullable, Option or Maybe
in other languages. If the value itself can be nothing (notably, when T is Any),
the Union{Some{T}, Nothing} type is more appropriate since x == nothing then indicates
the absence of a value, and x == Some(nothing) indicates the presence of a value equal
to nothing. The something function allows unwrapping Some objects and
using a default value instead of nothing arguments. Note that the compiler is able to
generate efficient code when working with Union{T, Nothing} arguments or fields.
To represent missing data in the statistical sense (NA in R or NULL in SQL), use the
missing object. See the [Missing Values](@ref missing) section for more details.
In some languages, the empty tuple (()) is considered the canonical
form of nothingness. However, in julia it is best thought of as just
a regular tuple that happens to contain zero values.
The empty (or "bottom") type, written as Union{} (an empty union type), is a type with
no values and no subtypes (except itself). You will generally not need to use this type.
In Julia, x += y gets replaced during lowering by x = x + y. For arrays, this has the consequence
that, rather than storing the result in the same location in memory as x, it allocates a new
array to store the result. If you prefer to mutate x, use x .+= y to update each element
individually.
While this behavior might surprise some, the choice is deliberate. The main reason is the presence
of immutable objects within Julia, which cannot change their value once created. Indeed, a
number is an immutable object; the statements x = 5; x += 1 do not modify the meaning of 5,
they modify the value bound to x. For an immutable, the only way to change the value is to reassign
it.
To amplify a bit further, consider the following function:
function power_by_squaring(x, n::Int)
ispow2(n) || error("This implementation only works for powers of 2")
while n >= 2
x *= x
n >>= 1
end
x
endAfter a call like x = 5; y = power_by_squaring(x, 4), you would get the expected result: x == 5 && y == 625.
However, now suppose that *=, when used with matrices, instead mutated the left hand side.
There would be two problems:
- For general square matrices,
A = A*Bcannot be implemented without temporary storage:A[1,1]gets computed and stored on the left hand side before you're done using it on the right hand side. - Suppose you were willing to allocate a temporary for the computation (which would eliminate most
of the point of making
*=work in-place); if you took advantage of the mutability ofx, then this function would behave differently for mutable vs. immutable inputs. In particular, for immutablex, after the call you'd have (in general)y != x, but for mutablexyou'd havey == x.
Because supporting generic programming is deemed more important than potential performance optimizations
that can be achieved by other means (e.g., using broadcasting or explicit loops), operators like += and
*= work by rebinding new values.
While the streaming I/O API is synchronous, the underlying implementation is fully asynchronous.
Consider the printed output from the following:
julia> @sync for i in 1:3
@async write(stdout, string(i), " Foo ", " Bar ")
end
123 Foo Foo Foo Bar Bar Bar
This is happening because, while the write call is synchronous, the writing of each argument
yields to other tasks while waiting for that part of the I/O to complete.
print and println "lock" the stream during a call. Consequently changing write to println
in the above example results in:
julia> @sync for i in 1:3
@async println(stdout, string(i), " Foo ", " Bar ")
end
1 Foo Bar
2 Foo Bar
3 Foo Bar
You can lock your writes with a ReentrantLock like this:
julia> l = ReentrantLock();
julia> @sync for i in 1:3
@async begin
lock(l)
try
write(stdout, string(i), " Foo ", " Bar ")
finally
unlock(l)
end
end
end
1 Foo Bar 2 Foo Bar 3 Foo Bar
Zero-dimensional arrays are arrays of the form Array{T,0}. They behave similar
to scalars, but there are important differences. They deserve a special mention
because they are a special case which makes logical sense given the generic
definition of arrays, but might be a bit unintuitive at first. The following
line defines a zero-dimensional array:
julia> A = zeros()
0-dimensional Array{Float64,0}:
0.0
In this example, A is a mutable container that contains one element, which can
be set by A[] = 1.0 and retrieved with A[]. All zero-dimensional arrays have
the same size (size(A) == ()), and length (length(A) == 1). In particular,
zero-dimensional arrays are not empty. If you find this unintuitive, here are
some ideas that might help to understand Julia's definition.
- Zero-dimensional arrays are the "point" to vector's "line" and matrix's "plane". Just as a line has no area (but still represents a set of things), a point has no length or any dimensions at all (but still represents a thing).
- We define
prod(())to be 1, and the total number of elements in an array is the product of the size. The size of a zero-dimensional array is(), and therefore its length is1. - Zero-dimensional arrays don't natively have any dimensions into which you
index -- they’re just
A[]. We can apply the same "trailing one" rule for them as for all other array dimensionalities, so you can indeed index them asA[1],A[1,1], etc; see Omitted and extra indices.
It is also important to understand the differences to ordinary scalars. Scalars
are not mutable containers (even though they are iterable and define things
like length, getindex, e.g. 1[] == 1). In particular, if x = 0.0 is
defined as a scalar, it is an error to attempt to change its value via
x[] = 1.0. A scalar x can be converted into a zero-dimensional array
containing it via fill(x), and conversely, a zero-dimensional array a can
be converted to the contained scalar via a[]. Another difference is that
a scalar can participate in linear algebra operations such as 2 * rand(2,2),
but the analogous operation with a zero-dimensional array
fill(2) * rand(2,2) is an error.
You may find that simple benchmarks of linear algebra building blocks like
using BenchmarkTools
A = randn(1000, 1000)
B = randn(1000, 1000)
@btime $A \ $B
@btime $A * $Bcan be different when compared to other languages like Matlab or R.
Since operations like this are very thin wrappers over the relevant BLAS functions, the reason for the discrepancy is very likely to be
-
the BLAS library each language is using,
-
the number of concurrent threads.
Julia compiles and uses its own copy of OpenBLAS, with threads currently capped at 8 (or the number of your cores).
Modifying OpenBLAS settings or compiling Julia with a different BLAS library, eg Intel MKL, may provide performance improvements. You can use MKL.jl, a package that makes Julia's linear algebra use Intel MKL BLAS and LAPACK instead of OpenBLAS, or search the discussion forum for suggestions on how to set this up manually. Note that Intel MKL cannot be bundled with Julia, as it is not open source.
When using Julia in high-performance computing (HPC) facilities with shared filesystems, it is recommended to use a shared
depot (via the [JULIA_DEPOT_PATH](@ref JULIA_DEPOT_PATH) environment variable). Since Julia v1.10, multiple Julia processes on functionally similar
workers and using the same depot will coordinate via pidfile locks to only spend effort precompiling on one process while the
others wait. The precompilation process will indicate when the process is precompiling or waiting for another that is
precompiling. If non-interactive the messages are via @debug.
However, due to caching of binary code, the cache rejection since v1.9 is more strict and users may need to set the
[JULIA_CPU_TARGET](@ref JULIA_CPU_TARGET) environment variable appropriately to get a single cache that is usable throughout the HPC
environment.
The Stable version of Julia is the latest released version of Julia, this is the version most people will want to run. It has the latest features, including improved performance. The Stable version of Julia is versioned according to SemVer as v1.x.y. A new minor release of Julia corresponding to a new Stable version is made approximately every 4-5 months after a few weeks of testing as a release candidate. Unlike the LTS version the Stable version will not normally receive bugfixes after another Stable version of Julia has been released. However, upgrading to the next Stable release will always be possible as each release of Julia v1.x will continue to run code written for earlier versions.
You may prefer the LTS (Long Term Support) version of Julia if you are looking for a very stable code base. The current LTS version of Julia is versioned according to SemVer as v1.6.x; this branch will continue to receive bugfixes until a new LTS branch is chosen, at which point the v1.6.x series will no longer received regular bug fixes and all but the most conservative users will be advised to upgrade to the new LTS version series. As a package developer, you may prefer to develop for the LTS version, to maximize the number of users who can use your package. As per SemVer, code written for v1.0 will continue to work for all future LTS and Stable versions. In general, even if targeting the LTS, one can develop and run code in the latest Stable version, to take advantage of the improved performance; so long as one avoids using new features (such as added library functions or new methods).
You may prefer the nightly version of Julia if you want to take advantage of the latest updates to the language, and don't mind if the version available today occasionally doesn't actually work. As the name implies, releases to the nightly version are made roughly every night (depending on build infrastructure stability). In general nightly released are fairly safe to use—your code will not catch on fire. However, they may be occasional regressions and or issues that will not be found until more thorough pre-release testing. You may wish to test against the nightly version to ensure that such regressions that affect your use case are caught before a release is made.
Finally, you may also consider building Julia from source for yourself. This option is mainly for those individuals who are comfortable at the command line, or interested in learning. If this describes you, you may also be interested in reading our guidelines for contributing.
Links to each of these download types can be found on the download page at https://julialang.org/downloads/. Note that not all versions of Julia are available for all platforms.
Each minor version of julia has its own default environment. As a result, upon installing a new minor version of Julia, the packages you added using the previous minor version will not be available by default. The environment for a given julia version is defined by the files Project.toml and Manifest.toml in a folder matching the version number in .julia/environments/, for instance, .julia/environments/v1.3.
If you install a new minor version of Julia, say 1.4, and want to use in its default environment the same packages as in a previous version (e.g. 1.3), you can copy the contents of the file Project.toml from the 1.3 folder to 1.4. Then, in a session of the new Julia version, enter the "package management mode" by typing the key ], and run the command instantiate.
This operation will resolve a set of feasible packages from the copied file that are compatible with the target Julia version, and will install or update them if suitable. If you want to reproduce not only the set of packages, but also the versions you were using in the previous Julia version, you should also copy the Manifest.toml file before running the Pkg command instantiate. However, note that packages may define compatibility constraints that may be affected by changing the version of Julia, so the exact set of versions you had in 1.3 may not work for 1.4.