ExCubecl

ExCubecl is a GPU compute runtime for Elixir, powered by CubeCL via Rust NIFs.

It provides GPU buffer management, kernel execution, async command submission, and pipeline orchestration — designed for AI inference, media processing, and realtime GPU effects on mobile and desktop.

Architecture

┌─────────────────────────────────────────────┐
│              Elixir / BEAM                   │
│  ExCubecl.buffer(...)                       │
│  ExCubecl.run_kernel(:blur, ...)            │
│  ExCubecl.pipeline() |> pipeline_run()      │
├─────────────────────────────────────────────┤
│           ExCubecl.NIF (Elixir)              │
│  - NIF function stubs                        │
├─────────────────────────────────────────────┤
│           Rust NIF (lib.rs)                  │
│  - GPU device management                     │
│  - Buffer pool / Texture pool                │
│  - Kernel cache                              │
│  - Async command queue                       │
│  - Stream scheduler                          │
├─────────────────────────────────────────────┤
│           CubeCL Runtime                     │
│  - GPU kernel compilation                    │
│  - Buffer management                         │
│  - Dispatch execution                        │
│  - Synchronization                           │
├─────────────────────────────────────────────┤
│           C FFI (ex_cubecl.h)                │
│  - Mobile platform interface                 │
│  - iOS / Android interop                     │
└─────────────────────────────────────────────┘

Installation

Add ex_cubecl to your list of dependencies in mix.exs:

def deps do
  [
    {:ex_cubecl, "~> 0.2.0"}
  ]
end

Quick Start

# Check device
ExCubecl.device_info()
%{name: "ExCubecl CPU (Rust NIF)", gpu: false, version: "0.2.0"}

# Create GPU buffers
a = ExCubecl.buffer([1.0, 2.0, 3.0], {3}, :f32)
b = ExCubecl.buffer([4.0, 5.0, 6.0], {3}, :f32)

# Inspect
ExCubecl.shape(a)   # {3}
ExCubecl.dtype(a)   # :f32
ExCubecl.size(a)    # 12 (bytes)

# Read data back
data = ExCubecl.read(a)

# Run a kernel
output = ExCubecl.buffer([0.0, 0.0, 0.0], {3}, :f32)
ExCubecl.run_kernel(:elementwise_add, [a], output, %{})

# Async execution
cmd_id = ExCubecl.submit(%{op: :run_kernel, kernel: :relu, inputs: [a], output: output, params: %{}})
ExCubecl.poll(cmd_id)   # :pending | :completed | {:error, reason}
ExCubecl.wait(cmd_id)   # blocks until done

# Pipeline orchestration
pipeline = ExCubecl.pipeline()
pipeline
|> ExCubecl.pipeline_add(%{op: :run_kernel, kernel: :blur, inputs: [a], output: b, params: %{}})
|> ExCubecl.pipeline_add(%{op: :run_kernel, kernel: :relu, inputs: [b], output: output, params: %{}})
ExCubecl.pipeline_run(pipeline)

# Cleanup
ExCubecl.free(a)
ExCubecl.free(b)
ExCubecl.free(output)
ExCubecl.free_pipeline(pipeline)

Supported Types

Mobile Integration (iOS / Android)

ExCubecl includes a C FFI layer for mobile platform integration.

iOS (Objective-C / Swift)

#include "ex_cubecl.h"

float data[] = {1.0f, 2.0f, 3.0f};
size_t shape[] = {3};
ex_cubecl_buffer_handle_t buf = ex_cubecl_buffer_new(
    (const uint8_t*)data, shape, 1, EX_CUBECL_DTYPE_F32
);

float out[3];
ex_cubecl_buffer_read(buf, (uint8_t*)out, sizeof(out));

ex_cubecl_buffer_free(buf);

Android (JNI)

#include "ex_cubecl.h"
#include <jni.h>

JNIEXPORT jlong JNICALL
Java_com_example_excubecl_ExCubeclBuffer_create(
    JNIEnv *env, jobject thiz, jbyteArray data, jlongArray shape, jint dtype) {
    jsize data_len = (*env)->GetArrayLength(env, data);
    jbyte *data_ptr = (*env)->GetByteArrayElements(env, data, NULL);
    jlong *shape_ptr = (*env)->GetLongArrayElements(env, shape, NULL);
    jsize ndim = (*env)->GetArrayLength(env, shape);

    ex_cubecl_buffer_handle_t handle = ex_cubecl_buffer_new(
        (const uint8_t*)data_ptr, (const size_t*)shape_ptr, ndim, dtype
    );

    (*env)->ReleaseByteArrayElements(env, data, data_ptr, 0);
    (*env)->ReleaseLongArrayElements(env, shape, shape_ptr, 0);

    return (jlong)handle;
}

See native/ex_cubecl_nif/include/ex_cubecl.h for the full API reference.

Use Cases

GPU Image Processing

camera frame → GPU texture → CubeCL kernel → screen render

Blur, sharpen, denoise, beauty filters, LUT filters — all without CPU copies.

AI Inference

tensor → CubeCL kernels → prediction

Segmentation, face landmarks, pose detection, embeddings — realtime camera AI.

Video Processing

video texture → GPU kernels → encoder

Compositing, transitions, overlays, subtitles, color grading.

Livestream Effects

camera → AI segmentation → background replacement → stream encoder

Virtual background, AR effects, realtime filters — all GPU-native.

Evolution Path

License

Apache 2.0 — See LICENSE for details.