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You could also use Mojo, one language for all targets.

by melodyogonna 10d ago

carefree-bob 10d ago
I began to lose interest after the acquisition. Have you been following along, are they still going to open source it?
- YuechenLi 10d ago
  I thought they already did and released the compiler source code under Apache 2.0.
- ecl3ctic 10d ago
  The Mojo compiler has been open source for over a month now. And the Mojo standard library has been open source for over a year. It’s all open source. Go check it out!
  - carefree-bob 10d ago
    Nice, thank you. There is an old python project I've been thinking about converting to Mojo.
adgjlsfhk1 10d ago
Or julia if you want a much more mature ecosystem.
- patagurbon 10d ago
  I highly recommend Julia for (scientific) GPU programming but it would be nice if there was a larger community and/or funding behind the GPU side of things. It has very few core devs for what it is.
- eggy 9d ago
  Julia has had a great CUDA story for a few years now, and this about 9 days old. Rust rejects buffer aliasing at compile time using Rust's borrow checker, but shared memory in cuda-oxide currently requires unsafe, but then there's HuggingFace's Grout and mistral.rs, so yeah, Rust is picking up ground here on Julia. How is OpenCL's performance these days?
- zackmorris 9d ago
  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/ 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

You could also use Mojo, one language for all targets.

carefree-bob 10d ago

I began to lose interest after the acquisition. Have you been following along, are they still going to open source it?

YuechenLi 10d ago
I thought they already did and released the compiler source code under Apache 2.0.
ecl3ctic 10d ago
The Mojo compiler has been open source for over a month now. And the Mojo standard library has been open source for over a year. It’s all open source. Go check it out!
- carefree-bob 10d ago
  Nice, thank you. There is an old python project I've been thinking about converting to Mojo.

adgjlsfhk1 10d ago

Or julia if you want a much more mature ecosystem.

patagurbon 10d ago
I highly recommend Julia for (scientific) GPU programming but it would be nice if there was a larger community and/or funding behind the GPU side of things. It has very few core devs for what it is.
eggy 9d ago
Julia has had a great CUDA story for a few years now, and this about 9 days old. Rust rejects buffer aliasing at compile time using Rust's borrow checker, but shared memory in cuda-oxide currently requires unsafe, but then there's HuggingFace's Grout and mistral.rs, so yeah, Rust is picking up ground here on Julia. How is OpenCL's performance these days?
zackmorris 9d ago
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/ 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