I fell in love with MATLAB (or GNU Octave for free since you really pay for toolboxes/packages) back around 2004, despite it warts. So I second Julia, which is similar, but is a more modern functional language instead of imperative.
I asked Google's Gemini if Julia can run on GPU unmodified without annotations, pragmas, intrinsics or similar manually-managed friction, and it said yes, but that data types must be swapped out for GPU-backed types:
If your code is written using vector/matrix operations, broadcasting, or standard linear algebra functions, it can run on the GPU entirely unmodified. You only need to change the input data type to a GPU-backed array (e.g., swapping a CPU Array for a CuArray from CUDA.jl).
# A standard Julia function — completely agnostic to hardware
function custom_math!(C, A, B)
@. C = sin(A) + 2 * B # Normal broadcasted operation
end
# Running on the CPU:
A_cpu = rand(1000)
B_cpu = rand(1000)
C_cpu = similar(A_cpu)
custom_math!(C_cpu, A_cpu, B_cpu)
# Running on the GPU (Unmodified function!):
using CUDA
A_gpu = CuArray(A_cpu)
B_gpu = CuArray(B_cpu)
C_gpu = similar(A_gpu)
custom_math!(C_gpu, A_gpu, B_gpu) # Automatically compiles to native PTX!
https://cuda.juliagpu.org/stable/This is the direction we should be going. So while Nvidia's Rust port is an important first step, it's an evolutionary rather than revolutionary achievement. But that's all Nvidia can really do now, since it's locked into its own paradigm like Intel/Microsoft and has gotten too big to think outside the box.
Edit: PTX in its example stands for Parallel Thread Execution, the Virtual Machine (VM) Instruction Set Architecture (ISA) created by NVIDIA for its GPUs, which works similarly to Java byte code.
Edit 2: Broadcasting is a feature in Julia that allows you to apply a function or mathematical operation element-by-element across arrays of different shapes and sizes, without writing manual loops. In Julia, broadcasting is syntactically indicated by a dot (.) placed before an operator or function name (e.g., sin.(x) or .+). <- I was today years old when I learned the term for this