I'm trying to pass a nested Vector to a GPU kernel, however I cannot get it to work. Here's a minimal example:
using CUDA
function kernel(data::CuDeviceVector{CuDeviceVector{Float64, 1}, 1})
# Do nothing for now
end
# Build a vector of 10 floats
floats = Vector{Float64}()
for i in 1:10
push!(floats, Float64(1))
end
# Wrap the vector in another vector
outer = Vector{CuArray{Float64}}()
push!(outer, floats |> CuArray)
# Upload to the GPU
final = outer |> CuArray
# Invoke the kernel
#cuda threads=1 kernel(final)
I first build a Vector{Float64}, then a Vector{CuArray{Float64}} and finally a CuArray{CuArray{Float64}} which I then try to call the kernel with. The error occurs at final = outer |> CuArray:
ERROR: LoadError: CuArray only supports element types that are stored inline
How can I use nested Vectors with the GPU?
Related
I am new to Julia and I am trying to create a plot with the following:
xi2 = range(0,sqrt(6),step=1e-3)
collect(xi2)
plot(xi2, 1-xi2^2/6, label="n = 0")
When I try this though, I have the error:
MethodError: no method matching ^(::StepRangeLen{Float64, Base.TwicePrecision{Float64}, Base.TwicePrecision{Float64}, Int64}, ::Int64)
Closest candidates are:
^(::Union{AbstractChar, AbstractString}, ::Integer) at C:\Users\Acer\AppData\Local\Programs\Julia-1.7.0\share\julia\base\strings\basic.jl:721
^(::Rational, ::Integer) at C:\Users\Acer\AppData\Local\Programs\Julia-1.7.0\share\julia\base\rational.jl:475
^(::Complex{<:AbstractFloat}, ::Integer) at C:\Users\Acer\AppData\Local\Programs\Julia-1.7.0\share\julia\base\complex.jl:839
...
What am I missing here?
You want the elements of xi2 raised to the power of two, so you want element-wise operations using the dot operator:
julia> xi2 = range(0,sqrt(6),step=1e-3);
julia> plot(xi2, 1 .- xi2.^2/6, label="n = 0")
(The collect step was unnecessary, since most array operations can be performed on a range directly. And in case you did want to collect - i.e. allocate memory and make it a full array - you have to assign the result of collect to some variable. In your original code, the elements were being collected into an array, but then thrown away since the result wasn't assigned to anything.)
I have two 1-D arrays in which I would like to calculate the approximate cumulative integral of 1 array with respect to the scalar spacing specified by the 2nd array. MATLAB has a function called cumtrapz that handles this scenario. Is there something similar that I can try within Julia to accomplish the same thing?
The expected result is another 1-D array with the integral calculated for each element.
There is a numerical integration package for Julia (see the link) that defines cumul_integrate(X, Y) and uses the trapezoidal rule by default.
If this package didn't exist, though, you could easily write the function yourself and have a very efficient implementation out of the box because the loop does not come with a performance penalty.
Edit: Added an #assert to check matching vector dimensions and fixed a typo.
function cumtrapz(X::T, Y::T) where {T <: AbstractVector}
# Check matching vector length
#assert length(X) == length(Y)
# Initialize Output
out = similar(X)
out[1] = 0
# Iterate over arrays
for i in 2:length(X)
out[i] = out[i-1] + 0.5*(X[i] - X[i-1])*(Y[i] + Y[i-1])
end
# Return output
out
end
I have the following code:
# install.packages("jsonlite")
require("jsonlite")
x = list(
test = "my_test",
data = c(1, 2, 3)
)
toJSON(x)
This prints:
{"test":["my_test"],"data":[1,2,3]}
I was expecting:
{"test":"my_test","data":[1,2,3]}
I've tried using some of the parameters from the documentation, but can't seem to get it right.
The argument auto_unbox=TRUE did the trick:
automatically unbox all atomic vectors of length 1. It is usually safer to avoid this and instead use the unbox function to unbox individual elements. An exception
is that objects of class AsIs (i.e. wrapped in I()) are not automatically unboxed. This is a way to mark single values as length-1 arrays.
I.e., the solution was toJSON(x, auto_unbox=TRUE), which returns what I expected:
{"test":"my_test","data":[1,2,3]}
I need to build a matrix with extremely small entries.
So far I realized that the fastest way to define the kind of matrix that I need is:
Define a vectorized function of coordinates:
func = function(m,n){...}
Combine every possible coordinate using outer:
matrix = outer(1:100,1:100,FUN=func)
Having to deal with extremely small numbers I work in func's environment using brob numbers, its output will therefore be of the same type of a brob:
typeof(func(0:100,0:100) )
[1] "S4"
If I directly plug two vectors 0:100 in my function func it returns a vector of brobs but if I try to use it with outer I get the error:
Error in outer(1:100, 1:100, FUN = func) : invalid first argument
I suppose this is because package Brobdingnag can somehow deal with vectors but not with matrices. Is it right? Is there any way to make it work?
I would like to create an empty vector and append to it an array in Julia. How do I do that?
x = Vector{Float64}
append!(x, rand(10))
results in
`append!` has no method matching append!(::Type{Array{Float64,1}}, ::Array{Float64,1})
Thanks.
Your variable x does not contain an array but a type.
x = Vector{Float64}
typeof(x) # DataType
You can create an array as Array(Float64, n)
(but beware, it is uninitialized: it contains arbitrary values) or zeros(Float64, n),
where n is the desired size.
Since Float64 is the default, we can leave it out.
Your example becomes:
x = zeros(0)
append!( x, rand(10) )
I am somewhat new to Julia and came across this question after getting a similar error. To answer the original question for Julia version 1.2.0, all that is missing are ():
x = Vector{Float64}()
append!(x, rand(10))
This solution (unlike x=zeros(0)) works for other data types, too. For example, to create an empty vector to store dictionaries use:
d = Vector{Dict}()
push!(d, Dict("a"=>1, "b"=>2))
A note regarding use of push! and append!:
According to the Julia help, push! is used to add individual items to a collection, while append! adds an collection of items to a collection. So, the following pieces of code create the same array:
Push individual items:
a = Vector{Float64}()
push!(a, 1.0)
push!(a, 2.0)
Append items contained in an array:
a = Vector{Float64}()
append!(a, [1.0, 2.0])
You can initialize an empty Vector of any type by typing the type in front of []. Like:
Float64[] # Returns what you want
Array{Float64, 2}[] # Vector of Array{Float64,2}
Any[] # Can contain anything
New answer, for Julia 1. append! is deprecated, you now need to use push!(array, element) to add elements to an array
my_stuff = zeros()
push!(my_stuff, "new element")