use actix::prelude::*;
/// Bridge between Actix actors and Ring Kernels
pub struct GpuActorBridge {
runtime: Arc<RingKernelRuntime>,
kernel_id: String,
}
impl Actor for GpuActorBridge {
type Context = Context<Self>;
}
/// Forward messages to GPU kernel
impl<T: RingMessage + Message<Result = R>, R: RingMessage> Handler<T> for GpuActorBridge {
type Result = ResponseFuture<R>;
fn handle(&mut self, msg: T, _ctx: &mut Self::Context) -> Self::Result {
let runtime = self.runtime.clone();
let kernel_id = self.kernel_id.clone();
Box::pin(async move {
runtime.send(&kernel_id, msg).await?;
runtime.receive(&kernel_id, Duration::from_secs(30)).await
})
}
}
// Usage
let gpu_actor = GpuActorBridge {
runtime: runtime.clone(),
kernel_id: "vector_add".to_string(),
}.start();
let result: VectorAddResponse = gpu_actor.send(VectorAddRequest {
a: vec![1.0, 2.0],
b: vec![3.0, 4.0],
..Default::default()
}).await??;
use tower::{Service, ServiceBuilder};
/// Ring Kernel as a Tower service
pub struct RingKernelService<Req, Resp> {
runtime: Arc<RingKernelRuntime>,
kernel_id: String,
_marker: PhantomData<(Req, Resp)>,
}
impl<Req, Resp> Service<Req> for RingKernelService<Req, Resp>
where
Req: RingMessage,
Resp: RingMessage,
{
type Response = Resp;
type Error = Error;
type Future = Pin<Box<dyn Future<Output = Result<Resp>> + Send>>;
fn poll_ready(&mut self, _cx: &mut Context<'_>) -> Poll<Result<()>> {
Poll::Ready(Ok(()))
}
fn call(&mut self, req: Req) -> Self::Future {
let runtime = self.runtime.clone();
let kernel_id = self.kernel_id.clone();
Box::pin(async move {
runtime.send(&kernel_id, req).await?;
runtime.receive(&kernel_id, Duration::from_secs(30)).await
})
}
}
// Usage with Tower middleware
let service = ServiceBuilder::new()
.timeout(Duration::from_secs(5))
.rate_limit(1000, Duration::from_secs(1))
.service(RingKernelService::<VectorAddRequest, VectorAddResponse>::new(
runtime.clone(),
"vector_add".to_string(),
));
use axum::{extract::State, routing::post, Json, Router};
struct AppState {
runtime: Arc<RingKernelRuntime>,
}
async fn vector_add(
State(state): State<Arc<AppState>>,
Json(request): Json<VectorAddApiRequest>,
) -> Result<Json<VectorAddApiResponse>, AppError> {
let kernel_request = VectorAddRequest {
id: Uuid::new_v4(),
priority: priority::NORMAL,
correlation_id: None,
a: request.a,
b: request.b,
};
state.runtime.send("vector_add", kernel_request).await?;
let response: VectorAddResponse = state.runtime
.receive("vector_add", Duration::from_secs(30))
.await?;
Ok(Json(VectorAddApiResponse {
result: response.result,
latency_ns: response.timestamp.physical(),
}))
}
#[tokio::main]
async fn main() {
let runtime = RingKernelRuntime::builder()
.backend(BackendType::Cuda)
.build()
.await
.unwrap();
// Launch and activate kernel
let kernel = runtime.launch("vector_add", Dim3::linear(1), Dim3::linear(256), Default::default())
.await.unwrap();
kernel.activate().await.unwrap();
let state = Arc::new(AppState { runtime });
let app = Router::new()
.route("/api/vector-add", post(vector_add))
.with_state(state);
axum::Server::bind(&"0.0.0.0:3000".parse().unwrap())
.serve(app.into_make_service())
.await
.unwrap();
}
use arrow::array::{Float32Array, ArrayRef};
use arrow::record_batch::RecordBatch;
/// Process Arrow arrays on GPU
pub async fn process_arrow_batch(
runtime: &RingKernelRuntime,
batch: RecordBatch,
) -> Result<RecordBatch> {
let a = batch.column(0).as_any().downcast_ref::<Float32Array>().unwrap();
let b = batch.column(1).as_any().downcast_ref::<Float32Array>().unwrap();
let request = VectorAddRequest {
id: Uuid::new_v4(),
priority: priority::NORMAL,
correlation_id: None,
a: a.values().to_vec(),
b: b.values().to_vec(),
};
runtime.send("vector_add", request).await?;
let response: VectorAddResponse = runtime.receive("vector_add", Duration::from_secs(30)).await?;
let result_array: ArrayRef = Arc::new(Float32Array::from(response.result));
RecordBatch::try_new(
batch.schema(),
vec![result_array],
).map_err(Into::into)
}
use polars::prelude::*;
/// GPU-accelerated Polars operations
pub trait GpuOps {
fn gpu_add(self, other: &Series, runtime: &RingKernelRuntime) -> Result<Series>;
}
impl GpuOps for Series {
fn gpu_add(self, other: &Series, runtime: &RingKernelRuntime) -> Result<Series> {
let a = self.f32()?.cont_slice()?;
let b = other.f32()?.cont_slice()?;
let request = VectorAddRequest {
id: Uuid::new_v4(),
priority: priority::NORMAL,
correlation_id: None,
a: a.to_vec(),
b: b.to_vec(),
};
// Blocking for simplicity (use async in production)
let rt = tokio::runtime::Handle::current();
let response: VectorAddResponse = rt.block_on(async {
runtime.send("vector_add", request).await?;
runtime.receive("vector_add", Duration::from_secs(30)).await
})?;
Ok(Series::new("result", response.result))
}
}
use tonic::{Request, Response, Status};
pub mod ringkernel_proto {
tonic::include_proto!("ringkernel");
}
use ringkernel_proto::ring_kernel_server::{RingKernel, RingKernelServer};
pub struct RingKernelGrpc {
runtime: Arc<RingKernelRuntime>,
}
#[tonic::async_trait]
impl RingKernel for RingKernelGrpc {
async fn process(
&self,
request: Request<ProcessRequest>,
) -> Result<Response<ProcessResponse>, Status> {
let req = request.into_inner();
let kernel_req = VectorAddRequest {
id: Uuid::new_v4(),
priority: priority::NORMAL,
correlation_id: None,
a: req.a,
b: req.b,
};
self.runtime.send(&req.kernel_id, kernel_req).await
.map_err(|e| Status::internal(e.to_string()))?;
let response: VectorAddResponse = self.runtime
.receive(&req.kernel_id, Duration::from_secs(30))
.await
.map_err(|e| Status::internal(e.to_string()))?;
Ok(Response::new(ProcessResponse {
result: response.result,
}))
}
}
use candle_core::{Device, Tensor};
/// Bridge between Candle tensors and Ring Kernels
pub async fn gpu_tensor_op(
runtime: &RingKernelRuntime,
a: &Tensor,
b: &Tensor,
) -> Result<Tensor> {
let a_vec = a.to_vec1::<f32>()?;
let b_vec = b.to_vec1::<f32>()?;
let request = VectorAddRequest {
id: Uuid::new_v4(),
priority: priority::NORMAL,
correlation_id: None,
a: a_vec,
b: b_vec,
};
runtime.send("vector_add", request).await?;
let response: VectorAddResponse = runtime.receive("vector_add", Duration::from_secs(30)).await?;
Tensor::from_vec(response.result, a.shape(), &Device::Cpu)
.map_err(Into::into)
}
use config::{Config, ConfigError, File};
use serde::Deserialize;
#[derive(Debug, Deserialize)]
pub struct RingKernelConfig {
pub backend: String,
pub device_id: usize,
pub queue_capacity: usize,
pub poll_interval_us: u64,
pub telemetry_enabled: bool,
}
impl RingKernelConfig {
pub fn load() -> Result<Self, ConfigError> {
Config::builder()
.add_source(File::with_name("config/ringkernel"))
.add_source(config::Environment::with_prefix("RINGKERNEL"))
.build()?
.try_deserialize()
}
pub fn to_runtime_config(&self) -> RuntimeConfig {
RuntimeConfig {
default_queue_capacity: self.queue_capacity,
bridge_poll_interval: Duration::from_micros(self.poll_interval_us),
enable_telemetry: self.telemetry_enabled,
..Default::default()
}
}
}
use tracing::{info, instrument, span, Level};
impl RingKernelRuntime {
#[instrument(skip(self, message))]
pub async fn send<T: RingMessage>(&self, kernel_id: &str, message: T) -> Result<()> {
let span = span!(Level::DEBUG, "ringkernel.send", kernel_id, message_id = %message.message_id());
let _guard = span.enter();
info!(priority = message.priority(), "Sending message to kernel");
// ... implementation
}
}
use prometheus::{Counter, Histogram, Registry};
lazy_static! {
static ref MESSAGES_SENT: Counter = Counter::new(
"ringkernel_messages_sent_total",
"Total messages sent to kernels"
).unwrap();
static ref MESSAGE_LATENCY: Histogram = Histogram::with_opts(
HistogramOpts::new(
"ringkernel_message_latency_seconds",
"Message round-trip latency"
).buckets(vec![0.0001, 0.001, 0.01, 0.1, 1.0])
).unwrap();
}