Run on GPU

TFHE-rs now includes a GPU backend, featuring a CUDA implementation for performing integer arithmetics on encrypted data. In what follows, a simple tutorial is introduced: it shows how to update your existing program to use GPU acceleration, or how to start a new one using GPU.

Prerequisites

Cuda version >= 10
Compute Capability >= 3.0
gcc >= 8.0 - check this page for more details about nvcc/gcc compatible versions
cmake >= 3.24
Rust version - check this page

Importing to your project

To use the TFHE-rs GPU backend in your project, you first need to add it as a dependency in your Cargo.toml.

If you are using an x86 machine:

tfhe = { version = "0.6.1", features = [ "boolean", "shortint", "integer", "x86_64-unix", "gpu" ] }

If you are using an ARM machine:

tfhe = { version = "0.6.1", features = [ "boolean", "shortint", "integer", "aarch64-unix", "gpu" ] }

When running code that uses TFHE-rs, it is highly recommended to run in release mode with cargo's --release flag to have the best possible performance

Supported platforms

TFHE-rs GPU backend is supported on Linux (x86, aarch64).

OS x86 aarch64

OS	x86	aarch64
Linux	`x86_64-unix`	`aarch64-unix`*
macOS	Unsupported	Unsupported*
Windows	Unsupported	Unsupported

Linux

x86_64-unix

aarch64-unix*

macOS

Unsupported

Unsupported*

Windows

Unsupported

A first example

Configuring and creating keys.

In comparison with the CPU example, the only difference lies into the key creation, which is detailed here

Here is a full example (combining the client and server parts):

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);
}

Setting the keys

The configuration of the key is different from the CPU. More precisely, if both client and server keys are still generated by the Client (which is assumed to run on a CPU), the server key has then to be decompressed by the Server to be converted into the right format. To do so, the server should run this function: decompressed_to_gpu(). From then on, there is no difference between the CPU and the GPU.

Encrypting data

On the client-side, the method to encrypt the data is exactly the same than the CPU one, i.e.:

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

Computation.

The server must first set its keys up, like in the CPU, with: set_server_key(gpu_key); . Then, homomorphic computations are done with the same code than the one described here.

    //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 gets the decrypted results by computing:

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

Improving performance.

TFHE-rs includes the possibility to leverage the high number of threads given by a GPU. To do so, the configuration should be updated with

let config = ConfigBuilder::with_custom_parameters(PARAM_GPU_MULTI_BIT_MESSAGE_2_CARRY_2_GROUP_3_KS_PBS, None).build();

The complete example becomes:

use tfhe::{ConfigBuilder, set_server_key, FheUint8, ClientKey, CompressedServerKey};
use tfhe::prelude::*;
use tfhe::shortint::parameters::PARAM_GPU_MULTI_BIT_MESSAGE_2_CARRY_2_GROUP_3_KS_PBS;

fn main() {

    let config = ConfigBuilder::with_custom_parameters(PARAM_GPU_MULTI_BIT_MESSAGE_2_CARRY_2_GROUP_3_KS_PBS, None).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);
}

List of available operations

The GPU backend includes the following operations:

name

symbol

Enc/Enc

Enc/ Int

Neg

-

N/A

Add

+

Sub

-

Mul

*

Div

/

Rem

%

Not

!

N/A

BitAnd

&

BitOr

|

BitXor

^

Shr

>>

Shl

<<

Rotate right

rotate_right

Rotate left

rotate_left

Min

min

Max

max

Greater than

gt

Greater or equal than

ge

Lower than

lt

Lower or equal than

le

Equal

eq

Cast (into dest type)

cast_into

N/A

Cast (from src type)

cast_from

N/A

Ternary operator

if_then_else

The equivalent signed operations are also available.

All operations follow the same syntax than the one described in here.

Benchmarks

All GPU benchmarks presented here were obtained on a single H100 GPU, and rely on the multithreaded PBS algorithm. The cryptographic parameters PARAM_GPU_MULTI_BIT_MESSAGE_2_CARRY_2_GROUP_3_KS_PBS were used. Performance is the following when the inputs of the benchmarked operation are encrypted:

Operation \ Size

FheUint7

FheUint16

FheUint32

FheUint64

FheUint128

FheUint256

Negation (-)

46 ms

60 ms

75 ms

94 ms

150 ms

247 ms

Add / Sub (+,-)

46 ms

60 ms

75 ms

94 ms

150 ms

247 ms

Mul (x)

83 ms

121 ms

195 ms

456 ms

1.35 s

4.74 s

Equal / Not Equal (eq, ne)

25 ms

26 ms

38 ms

41 ms

52 ms

78 ms

Comparisons (ge, gt, le, lt)

46 ms

60 ms

74 ms

90 ms

109 ms

153 ms

Max / Min (max,min)

71 ms

86 ms

101 ms

124 ms

165 ms

236 ms

Bitwise operations (&, |, ^)

11 ms

12 ms

13 ms

15 ms

23 ms

32 ms

Left / Right Shifts (<<, >>)

71 ms

88 ms

109 ms

180 ms

279 ms

494 ms

Left / Right Rotations (left_rotate, right_rotate)

71 ms

88 ms

109 ms

180 ms

279 ms

494 ms

Performance is the following when the left input of the benchmarked operation is encrypted and the other is a clear scalar of the same size:

Operation \ Size

FheUint7

FheUint16

FheUint32

FheUint64

FheUint128

FheUint256

Add / Sub (+,-)

46 ms

60 ms

75 ms

94 ms

152 ms

251 ms

Mul (*)

67 ms

101 ms

149 ms

282 ms

727 ms

2.11 s

Equal / Not Equal (eq, ne)

26 ms

27 ms

41 ms

45 ms

57 ms

Comparisons (ge, gt, le, lt)

29 ms

41 ms

54 ms

69 ms

87 ms

117 ms

Max / Min (max,min)

53 ms

65 ms

81 ms

102 ms

142 ms

200 ms

Bitwise operations (&, |, ^)

11 ms

13 ms

15 ms

23 ms

32 ms

Left / Right Shifts (<<, >>)

11 ms

12 ms

13 ms

15 ms

23 ms

32 ms

Left / Right Rotations (left_rotate, right_rotate)

11 ms

12 ms

13 ms

15 ms

23 ms

32 ms

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Last updated 13 days ago