A simple example

The example shown in this section computes the sum of two integers using the GPU. It contains code that can be split into a client-side and a server-side part, but for simplicity it is shown as a single snippet. Only the server-side benefits from GPU acceleration.

This example shows how to use a single GPU to improve operation latency. It has the following structure:

1. Client-side: Generate client keys and GPU server keys. Encrypt two numbers

2. Server-side: Move server keys to GPU and perform the addition

3. Client-side: Decrypt the result

This example only performs an addition, but most FHE operations are supported on GPU. For a list see:

Operations

API elements discussed in this document

• tfhe::ConfigBuilder::default(): Instantiates the default cryptographic parameters. When the "gpu" feature is activated, the default parameters are GPU specific, which achieves optimal performance on GPU

• tfhe::ServerKey::decompress_to_gpu: decompresses a compressed ServerKey and copies it to all available GPUs

• tfhe::set_server_key: sets the current server key. When this is a GPU key, this function activates execution of integer operations on all GPUs assigned to this key.

A simple TFHE-rs program

use tfhe::{ConfigBuilder, set_server_key, FheUint8, ClientKey, CompressedServerKey};
use tfhe::prelude::*;

fn main() {

    let config = ConfigBuilder::default().build();

    let client_key= ClientKey::generate(config);
    let compressed_server_key = CompressedServerKey::new(&client_key);

    let gpu_key = compressed_server_key.decompress_to_gpu();

    let clear_a = 27u8;
    let clear_b = 128u8;

    let a = FheUint8::encrypt(clear_a, &client_key);
    let b = FheUint8::encrypt(clear_b, &client_key);

    //Server-side

    set_server_key(gpu_key);
    let result = a + b;

    //Client-side
    let decrypted_result: u8 = result.decrypt(&client_key);

    let clear_result = clear_a + clear_b;

    assert_eq!(decrypted_result, clear_result);
}

When the "gpu" feature is activated, calling: let config = ConfigBuilder::default().build(); instantiates cryptographic parameters that are different from the CPU ones.

Breakdown of the GPU TFHE-rs program

Key generation

Comparing to the CPU example, in the code snippet above, the server-side must call decompress_to_gpu to enable GPU-execution for the ensuing operations on ciphertexts. This function assigns all available GPUs to the server key.

    let gpu_key = compressed_server_key.decompress_to_gpu();

Once the key is decompressed to GPU and set with set_server_key, operations on ciphertexts execute on the GPU. In the example above:

• compressed_server_key is a CompressedServerKey, stored on CPU. The client-side should ensure this key is generated with GPU cryptographic parameters.

• gpu_key is the CudaServerKey corresponding to compressed_server_key and is stored on the GPU assigned to it.

• set_server_key sets either a CPU or GPU key. In this example, compressed_server_key and gpu_key have GPU cryptographic parameters. A GPU server key can enable automatic parallelization on multiple GPUs.

Encryption

On the client-side, the method to encrypt the data is exactly the same as the CPU one, as shown in the following example:

    let clear_a = 27u8;
    let clear_b = 128u8;
    
    let a = FheUint8::encrypt(clear_a, &client_key);
    let b = FheUint8::encrypt(clear_b, &client_key);

Server-side computation

The server first needs to set up its keys with set_server_key(gpu_key). Then, homomorphic computations are performed using the same approach as the CPU operations.

    //Server-side
    set_server_key(gpu_key);
    let result = a + b;

    //Client-side
    let decrypted_result: u8 = result.decrypt(&client_key);

    let clear_result = clear_a + clear_b;

    assert_eq!(decrypted_result, clear_result);

Decryption

Finally, the client decrypts the results using:

    let decrypted_result: u8 = result.decrypt(&client_key);

Optimizing for throughput

In order to improve operation throughput, you can use multiple GPUs with fine-grained GPU scheduling, as detailed on the following page:

https://github.com/zama-ai/tfhe-rs/blob/release/1.5.x/tfhe/docs/configuration/gpu-acceleration/multi_gpu.md