Issues and Tricks

Notes taken during the learning process

Issues and Tricks

Here is a nice blog: 7 Julia Gotcha for some basic and easily ignored tips.

  • The dot operator . stands in the heart of array operations. As the language evolves after Julia 1.0, there are more restrictions on the usage of dots. . in Julia is just a shorthand for the broadcast function. Consider the following example:


The preceding code is fused into an operation similar to the following:

broadcast(v -> exp(sin(cos(log(v)))), 1:100)

The consequence is that there is only one allocation of memory to produce the final result of the operation. In standard scripting languages, each step of the computation would typically use up new memory. This is both computationally inefficient and memory expensive. Such seemingly minor details make Julia shine in numerical computing applications.

I am so used to the MATLAB style of vectorization, so I usually write the code like

julia> lat.f[1,1,:] = (w[1]+w[3])*T[1] .- lat.f[3,1,:]

Actually in MATLAB you don't even need the dot operation for plus and minus. However, in Julia any fusion operation requires the explicit dot operation, even the equal sign! So the above example may not be optimal as you would expect: it is a copy instead of in-place operation!

The correct one should be:

julia> lat.f[1,1,:] .= (w[1]+w[3])*T[1] .- lat.f[3,1,:]

See an elegant and detailed description to the dot operations by Steven Johnson, a professor at MIT.

  • By default, a plot will not show up automatically inside a function. It is only displayed when it's returned. Otherwise, you can use, e.g., display(plot(f, -3, 3)) to show the plot.

  • The differential equation solver packages are extremely powerful. Read existing availability before you jump into writing your own version!

  • If the string also includes quotes, we can escape these by prefixing them with a backslash:

julia> "Beta is Latin for \"still doesn't work\"."

However, escaping can get messy, so there's a much better way of dealing with this –- by using triple quotes """...""".

julia> """Beta is Latin for "still doesn't work"."""

Within triple quotes, it is no longer necessary to escape the single quotes. However, make sure that the single quotes and the triple quotes are separated –- or else the compiler will get confused:

julia> """Beta is Latin for "still doesn't work"""" 

syntax: cannot juxtapose string literal

The triple quotes come with some extra special power when used with multiline text. First, if the opening """ is followed by a newline, this newline is stripped from the string. Also, whitespace is preserved but the string is dedented to the level of the least-indented line:

julia> """ 


julia> print(ans) 


The previous snippet illustrates how the first line is stripped and the whitespace is preserved—but the indentation starts with the least indented line (the space in front of 'Here' was removed).

  • Concatanating strings

Strings can be concatenated with an asterisk operator *, but this ONLY works for strings. To deal with other types, use string function:

julia> string(greeting, ", ", username)

"Good morning, 9543794"
julia> string(2, " and ", 3) 

"2 and 3"

There is also a String method (with capital S). Remember that in Julia names are case-sensitive, so string and String are two different things. For most purposes we'll need the lowercase function.

  • Interpolating strings

When creating longer, more complex strings, concatenation can be noisy and error-prone. For such cases, we're better off using the $ symbol to perform variable interpolation into strings:

julia> username = "Adrian" 

julia> greeting = "Good morning" 

julia> "$greeting, $username" 

"Good morning, Adrian"

More complex expressions can be interpolated by wrapping them into $(...):

julia> "$(uppercase(greeting)), $(reverse(username))" 


Just like the string function, interpolation takes care of converting the values to strings:

julia> "The sum of 1 and 2 is $(1 + 2)" 

"The sum of 1 and 2 is 3"
  • varinfo() is approximately equal to whos in MATLAB. SOME IDEs now have support for showing the variables in the current scope, which is extremely helpful.

  • Julia has alias. For instance, the Int type will reflect that, as it's just an alias to either Int32 or Int64:

julia> @show Int 

Int = Int64
  • @which can be used to show which method is actually being called with Julia's multi-dispatch system.

  • To find out what methods are defined for a function, use methods():

  • An easy way to load CSV/TSV file into Julia is by using the readdlm function, which is available in the DelimitedFiles module. If you use skipstart only, then an array is returned; if instead you use header=true, this would change the return type of the function invocation to a tuple of (data_cells, header_cells). This is an old module, with fast but limited functionality support. For more advanced reading, you can use CSV.jl package. This will return a DataFrame object instead of array.

  • We can check if a value is missing by using the ismissing function.

  • Unfortunately, transposing doesn't work smoothly for all kinds of matrices in Julia 1.1 yet, and the recommended way is to do this via permutedims (especially for a mixture of types).

  • If the REPL output is too wide, it will omit some of the DataFrame columns. To get Julia to display all the columns, you can use the showall function.

  • In some parts of the code, you can avoid temporary memory allocation by @view. For example, if we want to pick a submatrix from A, we can do

julia> block = A[i-1:i+1,j-1:j+1] # allocate temporary var

julia> block = @view A[i-1:i+1,j-1:j+1] # no allocation
  • In operations like setting array slice values, e.g. a[1:3,1] = [1,2,3], the right hand side creates a temporary vector. One way to avoid it is by setting each element separately. I don't know if there are better ways.

  • The macro @code_native shows the assembly code.

  • Do-Block: the do x syntax creates an anonymous function with argument x and passes it as the first argument to the preceding function. The implementation of do-block syntax is mind-refreshing and elegant.

  • rand and rand! has the ability to pick a random value from a given data. The latter one with exclamation mark can fill random value into a given data array.

  • Julia uses im for indicating imaginary numbers.

  • The equivalent of linspace in Julia is range(a, stop = b, length = c) |> collect.

  • Adding ; in square bracket can change the return type to an array as you would expect. For example,

julia> x = [1:10]

1-element Array{UnitRange{Int64},1}:
julia> x = [1:10;]

10-element Array{Int64,1}:
  • $ can be used to protect the functions that we do not want to broadcast, when used together with @.

  • Often, especially in performance-critical code, we want to squeeze the maximum speed out of Julia. If you are working with arrays, the @inbounds macro can be used to significantly reduce access time to the elements. The drawback is that you have to be sure that you are not trying to access an out-of-bounds location. The index boundary check can be turned off also by adding the --check-bounds=no flag to Julia. Also note the scope of @inbounds. In short, this macro effects all the index checking inside for loops, but not functions that are not inlined. The propagation of turning off inbound checking requires extra commands.

If you are developing a function in which you want to allow its user to disable bounds checking, you can use the @boundscheck macro. Here is an example function definition from base/bitarray.jl:

@inline function getindex(B::BitArray, i::Int)
    @boundscheck checkbounds(B, i)
    unsafe_bitgetindex(B.chunks, i)

This annotation will only have an effect if the function, getindex in this case, is inlined into a caller. Therefore, the @inline macro is used at the beginning of its definition.

  • A symbol is used to represent a variable in metaprogramming. Once you have symbols as a data type, of course, it becomes tempting to use them for other things, like as hash keys. But that's an incidental, opportunistic usage of a data type that has another primary purpose. Potentially using symbols over strings can speed up your Dict operations!

  • There are some general rules in the Plot.jl package that are useful to remember. For any matrix input, each column represents a data series and each row represents a data point. No matter it is x, y, labels, etc..

  • There is a macro called @debug, which only evaluates the statements after when debug logging is enabled. The level of logging can be selected by an environment variable JULIA_DEBUG.

  • Take advantage of one-line functions for your work. For example, to search for all the files with keywords in the directory, you can do

julia> searchdir(path,key) = filter(x->occursin(key,x), readdir(path))
  • In some cases, ifelse can improve performance from ? due to the avoid of branches.

  • There is a pipeline operator in Julia, similar to Bash:

julia> trunc(-1.5) |> typeof

In this way, we can change some operations into a more readable form.

  • If you really care about performance, try the @fastmath macro.

  • For the common loops, using for i in 1:length(A) is fine and equivalent to for i in eachindex(A). However, if A is an abstractArray that may be a subArray (view of array) that includes some non-continuous indexing, using eachindex is better.

  • MATLAB squeeze is equivalent to dropdims in Julia. However, improper use of dropdims may lead to 0-dimension arrays. There is a quite extensive discussion online about why exactly the same implementation of squeeze in Julia is not a good idea.

  • Julia has macros defined in package like Cascadia and LaTeXStrings:

using Cascadia
sm = sel''''
  • I once had a task of finding all the missing numbers in a sequence 0:2000. There are many ideas, but in Julia the simplest one is using Set, combining with the setdiff function.

  • Since 2018, Statistics package is moved out of stdlib into StatsBase package, but it still maintains the acronym.

  • The using statement is not allowed inside functions. If you really want to do it, add a @eval in the front of using.

  • Julia has this concept of partial application, which allows you to write functions like filter(>(0), a) instead of filter(x->x>0, a).

Type Stability

If there is one thing that has a direct and massive impact on the performance of Julia code, it's the type system. And the most important thing about it is to write code that is type-stable. Type stability means that the type of a variable (including the return value of a function) must not vary with time or under different inputs. Understanding how to leverage type stability is key to writing fast software. Now that we know how to measure our code's execution time, we can see the effect of type instability with a few examples.

Let's take this innocent-looking function, for example:

julia> function f1() 

           x = 0 
           for i in 1:10 
               x += sin(i) 
f1 (generic function with 1 method)

There's nothing fancy about it. We have a variable, x, which is initialized to 0 –- and then a loop from 1 to 10, where we add the sin of a number to x. And then we return x. Nothing to see, right? Well, actually, quite the contrary –- a few bad things, performance-wise, are happening here. And they all have to do with type instability.

Julia provides a great tool for inspecting and diagnosing code for type-related issues –- the @code_warntype macro. Here's what we get when we use it with our f1 function:

julia> @code_warntype f1()

Check for the output, especially the color coding parts. As you might expect, green is good and red is bad. The problems are with Body::Union{Float64, Int64} on the first line, (#4 => 0, #14 => %29)::Union{Float64, Int64} on line 12, and (#13 => %29, \#4 => 0)::Union{Float64, Int64} on the penultimate line.

On the first line, the Body::Union{Float64, Int64}, as well as on the penultimate line, ::Union{Float64, Int64}, tell us the same thing –- the function returns a Union{Float64, Int64}, meaning that the function can return either a Float or an Integer. This is textbook type instability and bad news for performance. Next, on line 12, something has a type of Union{Float64, Int64} and this value is then returned as the result of the function. In case you're wondering, that something is x.

The problem is that we unsuspectingly initialized x to 0, an Integer. However, the sin function will return a Float. Adding a Float to an Integer will result in a Float, causing the type of x to change accordingly. Thus, x has two types during the execution of the function, and since we return x, our function is also type-unstable.

Granted, understanding the output of @code_warntype is not easy, although it does get easier with time. However, we can make our job easier by using the super-useful Traceur.jl package. It provides a @trace macro, which generates human-friendly information. Let's add it and try it out; you'll appreciate it:

(IssueReporter) pkg> add Traceur 
julia> using Traceur 

julia> @trace f1() 

┌ Warning: x is assigned as Int64 
└ @ REPL[94]:2 
┌ Warning: x is assigned as Float64 
└ @ REPL[94]:4 
┌ Warning: f1 returns Union{Float64, Int64} 
└ @ REPL[94]:2 

How cool is that? Crystal clear!

With this feedback in mind, we can refactor our code into a new f2 function:

julia> function f2() 

           x = 0.0 
           for i in 1:10 
                  x += sin(i) 
f2 (generic function with 1 method) 
julia> @trace f2() 


Awesome, nothing to report! No news is good news!

Now, we can benchmark f1 and f2 to see the result of our refactoring:

julia> @btime f1() 

  129.413 ns (0 allocations: 0 bytes) 
julia> @btime f2() 

  79.241 ns (0 allocations: 0 bytes) 

One specific part that I have made many mistakes is the construction of type-stable struct. Check the official performance tips about how to write type-stable struct constructors!

Avoid Memory Allocation

Using array views to avoid memory allocation

Static arrays

In the current implementation, working with large StaticArrays puts a lot of stress on the compiler, and becomes slower than Base.Array as the size increases. A very rough rule of thumb is that you should consider using a normal Array for arrays larger than 100 elements.

Heap vs stack

Remember tha mutable objects like arrays are allocated on the heap, while immutable objects like tuple and static arrays are allocated on the stack. This will cause performance differences especially inside loop kernels!

Benchmarking tools

Given its focus on performance, it should come as no surprise that both core Julia and the ecosystem provide a variety of tools for inspecting our code, looking for bottlenecks and measuring runtime and memory usage. One of the simplest is the @time macro. It takes an expression and then prints its execution time, number of allocations, and the total number of bytes the execution caused to be allocated, before returning the result of the expression. For example, note the following:

julia> @time [x for x in 1:1_000_000]; 

  0.031727 seconds (55.85 k allocations: 10.387 MiB)

Generating an array of one million integers by iterating from one to one million takes 0.03 seconds. Not bad, but what if I told you that we can do better –- much better? We just committed one of the cardinal sins of Julia—code should not be run (nor benchmarked) in the global scope. So, rule one –- always wrap your code into functions.

The previous snippet can easily be refactored as follows:

julia> function onetomil()  

            [x for x in 1:1_000_000]
onetomil (generic function with 1 method)

Now, the benchmark is as follows:

julia> @time onetomil();

  0.027002 seconds (65.04 k allocations: 10.914 MiB)

All right, that's clearly faster –- but not much faster. However, what if we run the benchmark one more time?

julia> @time onetomil();

  0.002413 seconds (6 allocations: 7.630 MiB)

Wow, that's an order of magnitude faster! So, what gives?

Julia uses a just-in-time (JIT) compiler; that is, a function is compiled in real time when it is invoked for the first time. So, our initial benchmark also included the compilation time. This brings us to the second rule –- don't benchmark the first run.

The best way to accurately measure the performance of a piece of code, thus, would be to execute it multiple times and then compute the mean. There is a great tool, specially designed for this use case, called BenchmarkTools. Let's add it and give it a try:

julia> using BenchmarkTools 

julia> @benchmark onetomil() 

  memory estimate:  7.63 MiB 
  allocs estimate:  2 
  minimum time:     1.373 ms (0.00% GC) 
  median time:      1.972 ms (0.00% GC) 
  mean time:        2.788 ms (34.06% GC) 
  maximum time:     55.129 ms (96.23% GC) 
  samples:          1788 
  evals/sample:     1

We can also use the more compact @btime macro, which has an output similar to @time, but executes an equally comprehensive benchmark:

julia> @btime onetomil(); 

  1.363 ms (2 allocations: 7.63 MiB

BenchmarkTools exposes a very rich API and it's worth getting to know it well.

For packages, there is a helper library PkgBenchmark which let you define a suite of tests for benchmark. However, this timing really depends not only on the code itself, but also the testing environment, machine and setup.

One more thing to keep in mind here: timing by itself is very tricky. If doing inappropriately, you may only end up in timing the part you don't want (e.g. garbbage collection). Check the advices by experts!

Common Misunderstandings

Now you may have the impression that writing Julia code is like writing a static-typed language: the performance is gained by specifying every single argument of the functions. This is not true! Type assertions in function arguments are mainly used to control multiple dispatch, which has nothing to do with performance. To get performance in Julia the important hint is not annotating with types, but achieving type stability. This simply means that upon executing a piece of code, the variable types don't change.

There is an excellent explanation to this on StackOverFlow.


  • Differences between assignment, copy and deepcopy for mutable and immutable objects.

a = ones(3)
b = a
b[1] = 2.0

then a will also change. However, if you assign b to another type

a = ones(3)
b = a
b = 2

then a will not change.

One common misunderstanding from C users is pass-by-reference/value. Julia behaves the same as in Python. For example,

julia> a = 1

julia> b = a

julia> b = 2

julia> a

what do you expect for the value of a? Because a is immutable, it will not change from 1 to 2! However, the following

julia> a = [1,2]

julia> b = a

julia> b[1] = 2

julia> a

is different, because array is mutable object. Therefore the value of a should be [2,2].

  • Arrays and vectors are tricky. Be careful about singleton dimensions!

julia> a = ["1","2"]

julia> a = ["1" "2"]

julia> a = ["1";"2"]

julia> reshape(a,:,1)

julia> reshape(a,1,:)
  • reshape function does not allocate new memory! The indexes can only be Int64 on a 64bit machine and Int32 on a 32bit machine. See the reason behind this decision by Stefan and Jeff.

  • Object and reference needs special attention. Strings are immutable, therefore you cannot do operations like

julia> a = "hello"; a[2] = "a"

On the other hand, arrays are mutable, which makes it important to distinguish between aliasing and copying. For example,

julia> a = [1,2,3]; b = a; b[1] = 42; println(a)

will also change the values of a. The slicing operation [:] means copying, and the heavy memory usage compared to in-place manipulations is probably one of the reasons Julia encourages de-vectorized code. However, I am not entirely sure about what will be happening if the dot syntax is used together with slicing. As a side note, MATLAB uses "lazy copy" strategy.

  • If I define a macro and execute the script again after the first one, it always says invalid redefinition of constant ....

  • syntax: invisible character \u2060.

This happens once when I was using unicode. \u2060 is called word joiner.

  • I once encountered an error when using the ODE solvers. It turned out that the problem is I do not set the initial conditions with the correct types...

  • For a container-like thing, Julia used to have type keyword, but it is removed after version 0.7. Now only struct is used.

  • Strings can be treated as a list of characters, so we can index into them–- that is, access the character at a certain position in the word. It is important to notice that indexing via a singular value returns a Char, while indexing via a range returns a String (remember, for Julia these are two completely different things).

  • In Julia, string literals are encoded using UTF-8. UTF-8 is a variable-width encoding, meaning that not all characters are represented using the same number of bytes. For example, ASCII characters are encoded using a single byte–- but other characters can use up to four bytes. This means that not every byte index into a UTF-8 string is necessarily a valid index for a corresponding character. If you index into a string at such an invalid byte index, an error will be thrown. Here is what I mean:

julia> str = "Søren Kierkegaard was a Danish Philosopher" 

julia> str[1] 

'S': ASCII/Unicode U+0053 (category Lu: Letter, uppercase)
julia> str[2] 

'ø': Unicode U+00f8 (category Ll: Letter, lowercase) 
julia> str[3] 

StringIndexError("Søren Kierkegaard was a Danish Philosopher", 3)
julia> str[4] 

'r': ASCII/Unicode U+0072 (category Ll: Letter, lowercase)
  • MySQL is too prone to error on my Mac. The API is not good enough for a stable development for a rookie like me.

  • It is worth remembering that transpose creates a thin wrapper around the original array. This means that if we modify the transposed matrix, the original will also be modified!

  • Declare a global variable const does not mean you cannot change the values. It just means that you can no longer reassign the variable but if it refers to a mutable value, you can modify the value. In other words, the type and size of the variable is set, but not the values it stores. (Actually what I found is that you can assign it to other variables, but Julia will give you a warning. According to the manuals, this is mainly for performance reasons.)

  • Recursion with dynamic programming: a key idea is called memoization. Compare the following two versions of calculating Fibonacci sequence:

# Classical version                                                             
function fib(n)
   if n == 0
      return 0
   elseif n == 1
      return 1
      return fib(n-1) + fib(n-2)

# Memoized version                                                              
const known = Dict(0=>0, 1=>1)

function fibonacci(n)
   if n ∈ keys(known)
      return known[n]
      res = fibonacci(n-1) + fibonacci(n-2)
      known[n] = res
      return res

The second version is much faster than the first classical version because of the reuse of already known values. There is a library for implementing a macro for memoization. Similarly in Python, there is a decorator from functools for the same purpose.

  • Julia 1.0 does not support copy!, but it does in Julia 1.1+. As a workaround, you can use a .= b instead of copy!(a,b). Note that a = b won't work here if a is immutable.

  • Once I wanted to create an array of arrays and append items to each later on. This was what I did:

julia> a = Vector{Vector{Int}}(undef,2)

julia> push!(a[1],1)

ERROR: UndefRefError: access to undefined reference

An even more strange thing happened for fill:

julia> a = fill(Int[],2)

julia> push!(a[1],1)

Guess what I got? All the arrays are identical, which means that they are actually referred to the same memory allocation! This can only be avoided if I set each to a different value:

julia> a[1] = [1]

Then a[1] is detached, but a[2] and a[3] are still pointing to the same memory allocation! What I ended up doing is:

julia> for i=1:length(a) a[i] = [] end

This behavior is so weird! Be careful about all the related functions like zeros,ones.

  • for loops not inside functions (e.g. in REPL) does not inherit the variables from global scope.

  • There are many issues in 3D visualzation in Plots.jl. For example, zlabel is not working at all, as of Julia 1.2.

  • pyplot backend issue. In Juno on Windows, by default matplotlib uses a ploting backend with no gui, making it impossible to show figures directly. The solution is to add gcf() to the end of your code.

  • The Plot.jl library of Julia is not mature yet as of Julia 1.0. For example, the equal x,y,z range has no effect; the z label has some issue; the name for label and legend is confusing; the resolution of the figure changes depending on the way you execute the code; the size of the figure adjustment does not work. I had a much better experience with PyPlot package directly. It is identical to the Python version (except the enforced double quote) and very similar to a MATLAB user.

  • I falled into the issue of installing PyPlot.jl again on Raspberry Pi, ARM 64 bit version Ubuntu. Tried all the possible solutions, but nothing helped.

  • Be careful with filename test.jl, because it may conflict with the built-in test functions!

  • Do you know how to pass C function as arguments in Julia?

  • When I was developing the IDL.jl package, I encountered an issue with REPL during testing:

ERROR: LoadError: InitError: UndefVarError: active_repl not defined

This active_repl is actually a global variable for the current active REPL you are using. If you are in the testing environment, there's no active repl, so the above error raises up. The simple solution is to return the function before touching this line, or just create an instance with empty fields.

  • In function arguments definition, func(a::Bool=true) is different from func(a=true) in that if you have func(1) the former version will return error for you. This might be better for error checking.

  • During the surface flux integral, I used the quantile! function from the Statistics package to check the outlier data points. By the exclamation mark ! itself you can guess that it changes the input argument, which is not what I want. The correct one to use is quantile.

As similar mistakes happen so many time, I need to warn myself again: follow the principle coding rules is the best way to avoid mistakes. Things will accumulate, either good or bad.

  • As of Julia 1.5, there is no way to switch off asserts in the code. Hopefully this feature will be added in the future.

  • The package management system still needs to be improved. Compatibility issues happen from time to time if I have already installed many packages.

  • Requires.jl is an amazing pkg that aims at solving the conditional dependency issue in the pkgs! I have applied it to Vlasiator.jl already, and it works like magic.

  • Be careful with dictionaries, especially in performance-critical part. In my reimplementation of the classical Vlasov 1D-1V solver from C++, it is 3 times slower than C++ when dictionary is used to store variables and 2 time faster than C++ when dictionary is avoided.

  • Over-constraining argument types is considered as an antipattern in Julia. The key reason for specifying argument types in Julia is multi-dispatch. In fact, specifying argument types for many functions does not improve performance, which is a common misunderstanding of Julia's JIT compiler. See more discussions here.

  • To import a main module function into a submodule:

module SuperModule
   # Define `foo()` but don't give it any methods yet
   function foo end
   # You can put this into SubModule.jl and do 
   # include("SubModule.jl") here, but I'm including 
   # it inline for this simple example.
   module SubModule1
     # Explicitly indicate that this is the *same* foo as in SuperModule
     foo(x::Int) = println("hello Int")

   module SubModule2
     foo(x::Float64) = println("hello Float64")

Remember that the imported functions must be defined before the submodules, otherwise Julia will warn you with "not found".

  • In Julia, generally there is no issue of memory fragmentation for array of structs, just as in C. On the contrary, for Java each class contains header.

  • Low level optimization: MuladdMacro. LLVM sometimes cannot generate optimal machine code as in GCC or intel. However, there are some hack packages in Julia for these low level instructions. In this particular case, first check if muladd in the base library can help!

  • One general technique in computer science is called lazy evaluations. This essentially means that computations of actual data are postponed until they are needed. It may be useful in the case where more information is gathered at a later stage such that we are able to perform better optimizations. Example packages are LazyArrays and LazyGrids.

  • For a long while I don't understand the difference between 2D array and vector of vectors, especially vector of SVector (i.e. vector of tuples). Now suddenly I realize something: 2D arrays are actually 1D arrays which pretend to be 2D. The multi-dimensional indexes are only used for stripping and accessing the underlying 1D data. On the other hand, vector of tuples is a real 2D concept. This means that when the compiler sees a vector of tuple, it knows that each tuple is separated and is allowed to do stack level optimizations. 2D arrays are not guaranteed in this way since the end of each dimension is fake, and who knows if the user wants to do something crazy like listing the 2nd half of values in one line together with the 1st half of values in the adajacent line. The compiler has no choice but to put every elements on the heap. This also explains why we can access a 2D array with 1D indexing in Julia, while it is not possible in Fortran. These implementation details makes Fortran compilers easiler to detect stack optimization chances together with SIMD and somewhat harder in Julia.

  • Kernel function, a.k.a function barrier, is a critical technique to speed up type unstable computations. There are scenarios where the types of variables are only available at runtime like reading data from file. Applying function barriers properly will let the type instability only affect a small portion of the program, while the major time-consuming calculations can be kept stable. See the progress from v0.8.24 to v0.8.25 of Vlasiator.jl.

  • Val type: checkout Val Type Performance for a live example. Do not abuse dispatch! The following example gives no performance gain compared to regular type dispatch:

using BenchmarkTools

abstract type Average end
struct Dirac <: Average end
struct Father <: Average end
struct NoAve <: Average end

check_for_father_val(::Val{Father()}) = true

check_for_father_val(::Val{Dirac()}) = true

check_for_father_val(::Val{NoAve()}) = false

check_for_father_type(::Union{Father, Dirac}) = true

check_for_father_type(::NoAve) = false

# Tests
a = Father()
b = NoAve()

@btime check_for_father_val(Val($a))

@btime check_for_father_val(Val($b))

@btime check_for_father_type($a)

@btime check_for_father_type($b)