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Multi GPU APIs

To learn more about the theory of Multi GPU programming refer to this part of documentation.

Here we will cover the core multi GPU apis and a example

A Multi GPU example

In this example we will display how you can

  1. Fetch the number of devices installed on a machine
  2. For every GPU launch a thread and set an active device per thread.
  3. Execute a MSM on each GPU


let device_count = get_device_count().unwrap();

.for_each(move |device_id| {

// you can allocate points and scalars_d here

let mut cfg = MSMConfig::default_for_device(device_id); = &stream;
cfg.is_async = true;
cfg.are_scalars_montgomery_form = true;
msm(&scalars_d, &HostOrDeviceSlice::on_host(points), &cfg, &mut msm_results).unwrap();

// collect and process results


We use get_device_count to fetch the number of connected devices, device IDs will be 0, 1, 2, ..., device_count - 1

into_par_iter is a parallel iterator, you should expect it to launch a thread for every iteration.

We then call set_device(device_id).unwrap(); it should set the context of that thread to the selected device_id.

Any data you now allocate from the context of this thread will be linked to the device_id. We create our MSMConfig with the selected device ID let mut cfg = MSMConfig::default_for_device(device_id);, behind the scene this will create for us a DeviceContext configured for that specific GPU.

We finally call our msm method.

Device management API

To streamline device management we offer as part of icicle-cuda-runtime package methods for dealing with devices.


Sets the current CUDA device by its ID, when calling set_device it will set the current thread to a CUDA device.


  • device_id: usize: The ID of the device to set as the current device. Device IDs start from 0.


  • CudaResult<()>: An empty result indicating success if the device is set successfully. In case of failure, returns a CudaError.


  • Returns a CudaError if the specified device ID is invalid or if a CUDA-related error occurs during the operation.


let device_id = 0; // Device ID to set
match set_device(device_id) {
Ok(()) => println!("Device set successfully."),
Err(e) => eprintln!("Failed to set device: {:?}", e),


Retrieves the number of CUDA devices available on the machine.


  • CudaResult<usize>: The number of available CUDA devices. On success, contains the count of CUDA devices. On failure, returns a CudaError.


  • Returns a CudaError if a CUDA-related error occurs during the retrieval of the device count.


match get_device_count() {
Ok(count) => println!("Number of devices available: {}", count),
Err(e) => eprintln!("Failed to get device count: {:?}", e),


Retrieves the ID of the current CUDA device.


  • CudaResult<usize>: The ID of the current CUDA device. On success, contains the device ID. On failure, returns a CudaError.


  • Returns a CudaError if a CUDA-related error occurs during the retrieval of the current device ID.


match get_device() {
Ok(device_id) => println!("Current device ID: {}", device_id),
Err(e) => eprintln!("Failed to get current device: {:?}", e),

Device context API

The DeviceContext is embedded into NTTConfig, MSMConfig and PoseidonConfig, meaning you can simply pass a device_id to your existing config and the same computation will be triggered on a different device.


Represents the configuration a CUDA device, encapsulating the device's stream, ID, and memory pool. The default device is always 0.

pub struct DeviceContext<'a> {
pub stream: &'a CudaStream,
pub device_id: usize,
pub mempool: CudaMemPool,
  • stream: &'a CudaStream

    A reference to a CudaStream. This stream is used for executing CUDA operations. By default, it points to a null stream CUDA's default execution stream.

  • device_id: usize

    The index of the GPU currently in use. The default value is 0, indicating the first GPU in the system.

    In some cases assuming CUDA_VISIBLE_DEVICES was configured, for example as CUDA_VISIBLE_DEVICES=2,3,7 in the system with 8 GPUs - the device_id=0 will correspond to GPU with id 2. So the mapping may not always be a direct reflection of the number of GPUs installed on a system.

  • mempool: CudaMemPool

    Represents the memory pool used for CUDA memory allocations. The default is set to a null pointer, which signifies the use of the default CUDA memory pool.

Implementation Notes
  • The DeviceContext structure is cloneable and can be debugged, facilitating easier logging and duplication of contexts when needed.

DeviceContext::default_for_device(device_id: usize) -> DeviceContext<'static>

Provides a default DeviceContext with system-wide defaults, ideal for straightforward setups.


A DeviceContext instance configured with:

  • The default stream (null_mut()).
  • The default device ID (0).
  • The default memory pool (null_mut()).


  • device_id: usize: The ID of the device for which to create the context.


A DeviceContext instance with the provided device_id and default settings for the stream and memory pool.

check_device(device_id: i32)

Validates that the specified device_id matches the ID of the currently active device, ensuring operations are targeted correctly.


  • device_id: i32: The device ID to verify against the currently active device.


  • Panics if the device_id does not match the active device's ID, preventing cross-device operation errors.


let device_id: i32 = 0; // Example device ID
// Ensures that the current context is correctly set for the specified device ID.