GPU & Hardware Accelerator Options for Shared-Nothing Architecture

This document evaluates GPU and hardware accelerator options for extending the shared-nothing library with compute acceleration capabilities.

Performance Overview

Technology	Type	Latency	Throughput	Power	Platform
CUDA	GPU	~1-10μs	20+ TFLOPS	250W	NVIDIA
ROCm	GPU	~1-10μs	15+ TFLOPS	300W	AMD
Vulkan	GPU	~1-10μs	Varies	Varies	Cross-vendor
Metal	GPU	~1-5μs	10+ TFLOPS	20-50W	Apple
wgpu	GPU	~1-10μs	Varies	Varies	All
OpenCL	GPU/CPU	~1-10μs	Varies	Varies	All
FPGA	Custom	<1μs	Custom	25-75W	Xilinx/Intel
TPU	ML	~1-5μs	100+ TOPS	75-250W	Google
QAT	Crypto	<1μs	100 Gbps	20W	Intel
DPU	Network	<1μs	200 Gbps	25W	NVIDIA/AMD

1. CUDA (NVIDIA GPUs)

Overview

NVIDIA's proprietary parallel computing platform and programming model for GPU acceleration.

Key Features

Mature ecosystem (15+ years)
Extensive libraries (cuBLAS, cuDNN, cuFFT)
Best ML/AI performance
NVIDIA GPUs only

Rust Crates

rustacuda

Crate: rustacuda = "0.1"
GitHub: https://github.com/bheisler/RustaCUDA
Maturity: ⭐⭐⭐⭐ Stable
Documentation: ⭐⭐⭐⭐ Good
Features:
- Safe CUDA API wrapper
- Memory management
- Kernel launching
- Stream management
- Device queries

cudarc

Crate: cudarc = "0.9"
GitHub: https://github.com/coreylowman/cudarc
Maturity: ⭐⭐⭐⭐ Stable
Features:
- Modern safe wrapper
- NVRTC support (runtime compilation)
- Zero-copy where possible
- Better ergonomics than rustacuda

cuda-sys

Crate: cuda-sys = "0.3"
Type: Low-level bindings
Use: Direct CUDA API access

Implementation Example

use cudarc::driver::*;
use cudarc::nvrtc::compile_ptx;

pub struct CudaAccelerator {
    device: CudaDevice,
    stream: CudaStream,
}

impl CudaAccelerator {
    pub fn new() -> Result<Self> {
        let device = CudaDevice::new(0)?;
        let stream = device.fork_default_stream()?;
        
        Ok(Self { device, stream })
    }
    
    pub fn vector_add(&self, a: &[f32], b: &[f32]) -> Result<Vec<f32>> {
        // Allocate GPU memory
        let dev_a = self.device.htod_copy(a)?;
        let dev_b = self.device.htod_copy(b)?;
        let mut dev_c = self.device.alloc_zeros::<f32>(a.len())?;
        
        // Compile kernel
        let ptx = compile_ptx(r#"
            extern "C" __global__ void vector_add(float* a, float* b, float* c, int n) {
                int i = blockIdx.x * blockDim.x + threadIdx.x;
                if (i < n) c[i] = a[i] + b[i];
            }
        "#)?;
        
        self.device.load_ptx(ptx, "vector_add", &["vector_add"])?;
        let kernel = self.device.get_func("vector_add", "vector_add")?;
        
        // Launch kernel
        let cfg = LaunchConfig {
            grid_dim: ((a.len() + 255) / 256, 1, 1),
            block_dim: (256, 1, 1),
            shared_mem_bytes: 0,
        };
        
        unsafe {
            kernel.launch(
                cfg,
                (&dev_a, &dev_b, &mut dev_c, a.len() as i32)
            )?;
        }
        
        // Copy result back
        let result = self.device.dtoh_sync_copy(&dev_c)?;
        Ok(result)
    }
}

Use Cases

Deep learning training/inference
Scientific computing
Video processing
Cryptography
Ray tracing

Pros

✅ Best performance for NVIDIA GPUs
✅ Mature ecosystem
✅ Extensive documentation
✅ Large community

Cons

❌ NVIDIA hardware only
❌ Proprietary
❌ Requires CUDA toolkit installation
❌ Version compatibility issues

2. ROCm (AMD GPUs)

Overview

AMD's open-source platform for GPU computing, HIP (Heterogeneous Interface for Portability) provides CUDA compatibility.

Key Features

Open source
CUDA-compatible (HIP)
AMD GPU support
Growing ecosystem

Rust Crates

hip-sys

Crate: Via FFI bindings
GitHub: Community maintained
Maturity: ⭐⭐⭐ Beta
Approach: Direct HIP API bindings

Implementation Example

use hip_sys::*;

pub struct RocmAccelerator {
    device: hipDevice_t,
    context: hipCtx_t,
}

impl RocmAccelerator {
    pub fn new() -> Result<Self> {
        unsafe {
            hipInit(0);
            
            let mut device = 0;
            hipDeviceGet(&mut device, 0);
            
            let mut context = std::ptr::null_mut();
            hipCtxCreate(&mut context, 0, device);
            
            Ok(Self { device, context })
        }
    }
    
    pub fn launch_kernel(&self, data: &[f32]) -> Result<Vec<f32>> {
        unsafe {
            // Allocate device memory
            let mut dev_ptr = std::ptr::null_mut();
            hipMalloc(&mut dev_ptr as *mut *mut c_void, 
                     data.len() * std::mem::size_of::<f32>());
            
            // Copy to device
            hipMemcpy(dev_ptr, 
                     data.as_ptr() as *const c_void,
                     data.len() * std::mem::size_of::<f32>(),
                     hipMemcpyHostToDevice);
            
            // Launch kernel
            // ... kernel code ...
            
            // Copy back
            let mut result = vec![0.0f32; data.len()];
            hipMemcpy(result.as_mut_ptr() as *mut c_void,
                     dev_ptr,
                     data.len() * std::mem::size_of::<f32>(),
                     hipMemcpyDeviceToHost);
            
            hipFree(dev_ptr);
            
            Ok(result)
        }
    }
}

Use Cases

GPU computing on AMD hardware
Machine learning
Scientific computing
HPC workloads

Pros

✅ Open source
✅ CUDA-compatible (HIP)
✅ Good AMD GPU support
✅ Growing ecosystem

Cons

❌ AMD hardware only
❌ Less mature than CUDA
❌ Smaller ecosystem
❌ Limited Rust support

3. Vulkan Compute

Overview

Cross-vendor GPU API supporting compute shaders for general-purpose GPU programming.

Key Features

Cross-vendor (NVIDIA, AMD, Intel, ARM)
Modern low-level API
Explicit control
Good for graphics + compute

Rust Crates

vulkano

Crate: vulkano = "0.34"
GitHub: https://github.com/vulkano-rs/vulkano
Maturity: ⭐⭐⭐⭐⭐ Stable
Documentation: ⭐⭐⭐⭐⭐ Excellent
Features:
- Safe Vulkan wrapper
- Compute shader support
- Memory management
- Pipeline creation

ash

Crate: ash = "0.37"
GitHub: https://github.com/ash-rs/ash
Maturity: ⭐⭐⭐⭐⭐ Stable
Type: Low-level bindings
Features:
- Direct Vulkan API
- Minimal overhead
- Complete API coverage

Implementation Example

use vulkano::device::{Device, DeviceExtensions, Features};
use vulkano::instance::{Instance, InstanceExtensions};
use vulkano::buffer::{BufferUsage, CpuAccessibleBuffer};
use vulkano::command_buffer::{AutoCommandBufferBuilder, CommandBufferUsage};
use vulkano::pipeline::ComputePipeline;
use vulkano::descriptor_set::{PersistentDescriptorSet, WriteDescriptorSet};
use vulkano::sync::{self, GpuFuture};

pub struct VulkanAccelerator {
    device: Arc<Device>,
    queue: Arc<Queue>,
}

impl VulkanAccelerator {
    pub fn new() -> Result<Self> {
        // Create instance
        let instance = Instance::new(None, Default::default(), None)?;
        
        // Select physical device
        let physical = instance
            .enumerate_physical_devices()?
            .next()
            .expect("no devices available");
        
        // Create logical device and queue
        let queue_family = physical
            .queue_families()
            .find(|&q| q.supports_compute())
            .expect("couldn't find a compute queue family");
        
        let (device, mut queues) = Device::new(
            physical,
            Features::none(),
            &DeviceExtensions::none(),
            [(queue_family, 0.5)].iter().cloned(),
        )?;
        
        let queue = queues.next().unwrap();
        
        Ok(Self { device, queue })
    }
    
    pub fn compute_operation(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Create input buffer
        let input_buffer = CpuAccessibleBuffer::from_iter(
            self.device.clone(),
            BufferUsage::all(),
            false,
            input.iter().cloned(),
        )?;
        
        // Create output buffer
        let output_buffer = CpuAccessibleBuffer::from_iter(
            self.device.clone(),
            BufferUsage::all(),
            false,
            (0..input.len()).map(|_| 0.0f32),
        )?;
        
        // Load compute shader
        mod cs {
            vulkano_shaders::shader! {
                ty: "compute",
                src: "
                    #version 450
                    layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
                    
                    layout(set = 0, binding = 0) buffer Input {
                        float data[];
                    } input_data;
                    
                    layout(set = 0, binding = 1) buffer Output {
                        float data[];
                    } output_data;
                    
                    void main() {
                        uint idx = gl_GlobalInvocationID.x;
                        output_data.data[idx] = input_data.data[idx] * 2.0;
                    }
                "
            }
        }
        
        let shader = cs::load(self.device.clone())?;
        let pipeline = ComputePipeline::new(
            self.device.clone(),
            shader.entry_point("main").unwrap(),
            &(),
            None,
            |_| {},
        )?;
        
        // Create descriptor set
        let layout = pipeline.layout().set_layouts().get(0).unwrap();
        let set = PersistentDescriptorSet::new(
            layout.clone(),
            [
                WriteDescriptorSet::buffer(0, input_buffer.clone()),
                WriteDescriptorSet::buffer(1, output_buffer.clone()),
            ],
        )?;
        
        // Create command buffer
        let mut builder = AutoCommandBufferBuilder::primary(
            self.device.clone(),
            self.queue.family(),
            CommandBufferUsage::OneTimeSubmit,
        )?;
        
        builder
            .bind_pipeline_compute(pipeline.clone())
            .bind_descriptor_sets(
                PipelineBindPoint::Compute,
                pipeline.layout().clone(),
                0,
                set,
            )
            .dispatch([input.len() as u32 / 64, 1, 1])?;
        
        let command_buffer = builder.build()?;
        
        // Execute
        let future = sync::now(self.device.clone())
            .then_execute(self.queue.clone(), command_buffer)?
            .then_signal_fence_and_flush()?;
        
        future.wait(None)?;
        
        // Read results
        let result = output_buffer.read()?;
        Ok(result.to_vec())
    }
}

Use Cases

Cross-platform GPU compute
Graphics + compute pipelines
Real-time rendering
Physics simulation

Pros

✅ Cross-vendor support
✅ Excellent Rust support (vulkano)
✅ Modern API
✅ Good performance

Cons

❌ Verbose API
❌ Steep learning curve
❌ Less optimized than CUDA for ML

4. Metal (Apple Silicon)

Overview

Apple's graphics and compute API for macOS, iOS, iPadOS.

Key Features

Native Apple Silicon support
Unified memory architecture
Low-level control
Excellent performance on Apple hardware

Rust Crates

metal-rs

Crate: metal = "0.27"
GitHub: https://github.com/gfx-rs/metal-rs
Maturity: ⭐⭐⭐⭐⭐ Stable
Documentation: ⭐⭐⭐⭐ Good
Features:
- Safe Metal API wrapper
- Compute shader support
- Metal Performance Shaders (MPS)
- Complete API coverage

Implementation Example

use metal::*;

pub struct MetalAccelerator {
    device: Device,
    command_queue: CommandQueue,
}

impl MetalAccelerator {
    pub fn new() -> Result<Self> {
        let device = Device::system_default()
            .ok_or("No Metal device found")?;
        let command_queue = device.new_command_queue();
        
        Ok(Self {
            device,
            command_queue,
        })
    }
    
    pub fn compute(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Create buffers
        let input_buffer = self.device.new_buffer_with_data(
            input.as_ptr() as *const _,
            (input.len() * std::mem::size_of::<f32>()) as u64,
            MTLResourceOptions::StorageModeShared,
        );
        
        let output_buffer = self.device.new_buffer(
            (input.len() * std::mem::size_of::<f32>()) as u64,
            MTLResourceOptions::StorageModeShared,
        );
        
        // Compile shader
        let library_source = r#"
            #include <metal_stdlib>
            using namespace metal;
            
            kernel void vector_multiply(
                device const float* input [[buffer(0)]],
                device float* output [[buffer(1)]],
                uint id [[thread_position_in_grid]])
            {
                output[id] = input[id] * 2.0;
            }
        "#;
        
        let compile_options = CompileOptions::new();
        let library = self.device
            .new_library_with_source(library_source, &compile_options)?;
        
        let kernel = library.get_function("vector_multiply", None)?;
        
        // Create pipeline
        let pipeline = self.device
            .new_compute_pipeline_state_with_function(&kernel)?;
        
        // Create command buffer
        let command_buffer = self.command_queue.new_command_buffer();
        let encoder = command_buffer.new_compute_command_encoder();
        
        encoder.set_compute_pipeline_state(&pipeline);
        encoder.set_buffer(0, Some(&input_buffer), 0);
        encoder.set_buffer(1, Some(&output_buffer), 0);
        
        let grid_size = MTLSize::new(input.len() as u64, 1, 1);
        let threadgroup_size = MTLSize::new(256, 1, 1);
        
        encoder.dispatch_threads(grid_size, threadgroup_size);
        encoder.end_encoding();
        
        command_buffer.commit();
        command_buffer.wait_until_completed();
        
        // Read results
        let output_ptr = output_buffer.contents() as *const f32;
        let result = unsafe {
            std::slice::from_raw_parts(output_ptr, input.len()).to_vec()
        };
        
        Ok(result)
    }
    
    pub fn use_mps(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Metal Performance Shaders for common operations
        // ... MPS implementation ...
        Ok(vec![])
    }
}

Use Cases

Apple device acceleration
ML inference on Apple Silicon
Image/video processing
Graphics rendering

Pros

✅ Excellent on Apple hardware
✅ Unified memory (zero-copy)
✅ Good Rust support
✅ Native integration

Cons

❌ Apple platforms only
❌ Proprietary
❌ Smaller ecosystem than CUDA
❌ Different API from CUDA/OpenCL

5. WebGPU/wgpu

Overview

Next-generation graphics and compute API based on WebGPU standard, portable across all platforms.

Key Features

Cross-platform (all vendors)
Modern API design
Safe and portable
Future-proof

Rust Crates

wgpu

Crate: wgpu = "0.18"
GitHub: https://github.com/gfx-rs/wgpu
Maturity: ⭐⭐⭐⭐⭐ Stable
Documentation: ⭐⭐⭐⭐⭐ Excellent
Features:
- Cross-platform GPU API
- Backends: Vulkan, Metal, DX12, WebGPU
- Compute shader support
- Safe abstraction

Implementation Example

use wgpu::*;

pub struct WgpuAccelerator {
    device: Device,
    queue: Queue,
}

impl WgpuAccelerator {
    pub async fn new() -> Result<Self> {
        // Create instance
        let instance = Instance::new(InstanceDescriptor {
            backends: Backends::all(),
            ..Default::default()
        });
        
        // Request adapter
        let adapter = instance
            .request_adapter(&RequestAdapterOptions {
                power_preference: PowerPreference::HighPerformance,
                ..Default::default()
            })
            .await
            .ok_or("No adapter found")?;
        
        // Create device
        let (device, queue) = adapter
            .request_device(
                &DeviceDescriptor {
                    label: None,
                    features: Features::empty(),
                    limits: Limits::default(),
                },
                None,
            )
            .await?;
        
        Ok(Self { device, queue })
    }
    
    pub async fn compute(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Create buffers
        let input_buffer = self.device.create_buffer_init(&util::BufferInitDescriptor {
            label: Some("Input Buffer"),
            contents: bytemuck::cast_slice(input),
            usage: BufferUsages::STORAGE | BufferUsages::COPY_DST,
        });
        
        let output_buffer = self.device.create_buffer(&BufferDescriptor {
            label: Some("Output Buffer"),
            size: (input.len() * std::mem::size_of::<f32>()) as u64,
            usage: BufferUsages::STORAGE | BufferUsages::COPY_SRC,
            mapped_at_creation: false,
        });
        
        // Create shader module
        let shader = self.device.create_shader_module(ShaderModuleDescriptor {
            label: Some("Compute Shader"),
            source: ShaderSource::Wgsl(r#"
                @group(0) @binding(0)
                var<storage, read> input: array<f32>;
                
                @group(0) @binding(1)
                var<storage, read_write> output: array<f32>;
                
                @compute @workgroup_size(256)
                fn main(@builtin(global_invocation_id) global_id: vec3<u32>) {
                    let idx = global_id.x;
                    output[idx] = input[idx] * 2.0;
                }
            "#.into()),
        });
        
        // Create compute pipeline
        let compute_pipeline = self.device.create_compute_pipeline(&ComputePipelineDescriptor {
            label: Some("Compute Pipeline"),
            layout: None,
            module: &shader,
            entry_point: "main",
        });
        
        // Create bind group
        let bind_group_layout = compute_pipeline.get_bind_group_layout(0);
        let bind_group = self.device.create_bind_group(&BindGroupDescriptor {
            label: Some("Bind Group"),
            layout: &bind_group_layout,
            entries: &[
                BindGroupEntry {
                    binding: 0,
                    resource: input_buffer.as_entire_binding(),
                },
                BindGroupEntry {
                    binding: 1,
                    resource: output_buffer.as_entire_binding(),
                },
            ],
        });
        
        // Create command encoder
        let mut encoder = self.device.create_command_encoder(&CommandEncoderDescriptor {
            label: Some("Compute Encoder"),
        });
        
        {
            let mut compute_pass = encoder.begin_compute_pass(&ComputePassDescriptor {
                label: Some("Compute Pass"),
            });
            compute_pass.set_pipeline(&compute_pipeline);
            compute_pass.set_bind_group(0, &bind_group, &[]);
            compute_pass.dispatch_workgroups((input.len() as u32 + 255) / 256, 1, 1);
        }
        
        // Submit
        self.queue.submit(Some(encoder.finish()));
        
        // Read results
        let staging_buffer = self.device.create_buffer(&BufferDescriptor {
            label: Some("Staging Buffer"),
            size: output_buffer.size(),
            usage: BufferUsages::MAP_READ | BufferUsages::COPY_DST,
            mapped_at_creation: false,
        });
        
        let mut encoder = self.device.create_command_encoder(&Default::default());
        encoder.copy_buffer_to_buffer(
            &output_buffer,
            0,
            &staging_buffer,
            0,
            output_buffer.size(),
        );
        self.queue.submit(Some(encoder.finish()));
        
        let buffer_slice = staging_buffer.slice(..);
        let (sender, receiver) = futures::channel::oneshot::channel();
        buffer_slice.map_async(MapMode::Read, move |result| {
            sender.send(result).unwrap();
        });
        
        self.device.poll(Maintain::Wait);
        receiver.await??;
        
        let data = buffer_slice.get_mapped_range();
        let result: Vec<f32> = bytemuck::cast_slice(&data).to_vec();
        
        drop(data);
        staging_buffer.unmap();
        
        Ok(result)
    }
}

Use Cases

Portable GPU compute
Web applications
Cross-platform tools
Future-proof code

Pros

✅ Cross-platform (all GPUs)
✅ Modern safe API
✅ Excellent Rust support
✅ Active development
✅ Web support

Cons

❌ Less mature than CUDA/Vulkan
❌ May not expose all hardware features
❌ Still evolving

6. OpenCL

Overview

Open standard for parallel programming across heterogeneous platforms (CPUs, GPUs, FPGAs).

Key Features

Cross-platform
Cross-vendor
CPU and GPU support
Mature ecosystem

Rust Crates

ocl

Crate: ocl = "0.19"
GitHub: https://github.com/cogciprocate/ocl
Maturity: ⭐⭐⭐⭐ Stable
Documentation: ⭐⭐⭐⭐ Good

opencl3

Crate: opencl3 = "0.9"
GitHub: https://github.com/kenba/opencl3
Maturity: ⭐⭐⭐⭐ Stable
Type: OpenCL 3.0 bindings

Implementation Example

use ocl::{ProQue, Buffer, SpatialDims};

pub struct OpenClAccelerator {
    pro_que: ProQue,
}

impl OpenClAccelerator {
    pub fn new() -> Result<Self> {
        let src = r#"
            __kernel void vector_add(
                __global float* a,
                __global float* b,
                __global float* c)
            {
                int gid = get_global_id(0);
                c[gid] = a[gid] + b[gid];
            }
        "#;
        
        let pro_que = ProQue::builder()
            .src(src)
            .dims(SpatialDims::One(1024))
            .build()?;
        
        Ok(Self { pro_que })
    }
    
    pub fn compute(&self, a: &[f32], b: &[f32]) -> Result<Vec<f32>> {
        // Create buffers
        let buffer_a = Buffer::builder()
            .queue(self.pro_que.queue().clone())
            .len(a.len())
            .copy_host_slice(a)
            .build()?;
        
        let buffer_b = Buffer::builder()
            .queue(self.pro_que.queue().clone())
            .len(b.len())
            .copy_host_slice(b)
            .build()?;
        
        let buffer_c = self.pro_que.create_buffer::<f32>()?;
        
        // Create and execute kernel
        let kernel = self.pro_que
            .kernel_builder("vector_add")
            .arg(&buffer_a)
            .arg(&buffer_b)
            .arg(&buffer_c)
            .build()?;
        
        unsafe { kernel.enq()?; }
        
        // Read results
        let mut result = vec![0.0f32; a.len()];
        buffer_c.read(&mut result).enq()?;
        
        Ok(result)
    }
}

Use Cases

Cross-platform compute
Legacy systems
CPU + GPU heterogeneous
Scientific computing

Pros

✅ Cross-platform
✅ Mature standard
✅ CPU support
✅ Wide hardware support

Cons

❌ Declining adoption
❌ Verbose API
❌ Less performant than native APIs
❌ Limited vendor optimization

7. FPGA (Xilinx/Intel)

Overview

Field-Programmable Gate Arrays provide custom hardware acceleration with microsecond latency.

Key Features

Custom hardware logic
Ultra-low latency (<1μs)
Deterministic timing
Reconfigurable

Xilinx Vitis/Vivado Approach

// Rust calls C++ HLS code via FFI

// C++ HLS kernel (xilinx_kernel.cpp)
extern "C" {
    void custom_accelerator(
        const float* input,
        float* output,
        int length)
    {
        #pragma HLS INTERFACE m_axi port=input offset=slave bundle=gmem
        #pragma HLS INTERFACE m_axi port=output offset=slave bundle=gmem
        #pragma HLS INTERFACE s_axilite port=length bundle=control
        #pragma HLS INTERFACE s_axilite port=return bundle=control
        
        for (int i = 0; i < length; i++) {
            #pragma HLS PIPELINE II=1
            output[i] = input[i] * 2.0f + 1.0f;
        }
    }
}

// Rust FFI wrapper
use std::ffi::CString;

#[link(name = "xilinx_kernel")]
extern "C" {
    fn custom_accelerator(
        input: *const f32,
        output: *mut f32,
        length: i32,
    ) -> i32;
}

pub struct FpgaAccelerator {
    device_handle: i32,
}

impl FpgaAccelerator {
    pub fn new(bitstream_path: &str) -> Result<Self> {
        // Load FPGA bitstream
        // Initialize device
        Ok(Self { device_handle: 0 })
    }
    
    pub fn process(&self, input: &[f32]) -> Result<Vec<f32>> {
        let mut output = vec![0.0f32; input.len()];
        
        unsafe {
            custom_accelerator(
                input.as_ptr(),
                output.as_mut_ptr(),
                input.len() as i32,
            );
        }
        
        Ok(output)
    }
}

Use Cases

Ultra-low latency trading
Network packet processing
Signal processing
Custom protocols

Pros

✅ Lowest latency possible
✅ Deterministic timing
✅ Custom logic
✅ Power efficient

Cons

❌ Complex development
❌ Long compilation times (hours)
❌ Requires hardware expertise
❌ Limited Rust tooling
❌ Expensive hardware

8. TPU (Tensor Processing Units)

Overview

Google's custom ASICs optimized for ML workloads, available via Google Cloud.

Key Features

100+ TOPS performance
Optimized for TensorFlow/JAX
Matrix multiplication acceleration
Systolic array architecture

Implementation Approach

// Via TensorFlow/JAX bindings

use tensorflow::*;

pub struct TpuAccelerator {
    session: Session,
    graph: Graph,
}

impl TpuAccelerator {
    pub fn new() -> Result<Self> {
        let mut graph = Graph::new();
        
        // Define computation graph
        // ...
        
        let session = Session::new(&SessionOptions::new(), &graph)?;
        
        Ok(Self { session, graph })
    }
    
    pub fn infer(&self, input: &[f32]) -> Result<Vec<f32>> {
        // Run inference on TPU
        // ...
        Ok(vec![])
    }
}

// Or via JAX (Python interop)
use pyo3::prelude::*;

pub struct JaxTpuAccelerator {
    py: Python<'static>,
    module: PyObject,
}

impl JaxTpuAccelerator {
    pub fn new() -> Result<Self> {
        let gil = Python::acquire_gil();
        let py = gil.python();
        
        let code = r#"
import jax
import jax.numpy as jnp

@jax.jit
def compute(x):
    return jnp.matmul(x, x.T)
"#;
        
        let module = PyModule::from_code(py, code, "jax_compute.py", "jax_compute")?;
        
        Ok(Self {
            py,
            module: module.to_object(py),
        })
    }
}

Use Cases

Large-scale ML training
ML inference at scale
Research experiments
Cloud ML workloads

Pros

✅ Highest ML performance
✅ Cost-effective (Cloud TPU)
✅ TensorFlow/JAX optimized
✅ Large-scale distributed

Cons

❌ Google Cloud only
❌ TensorFlow/JAX required
❌ Not general-purpose
❌ Limited direct Rust support

9. Intel QAT (QuickAssist Technology)

Overview

Intel's hardware acceleration for cryptography, compression, and security functions.

Key Features

100+ Gbps crypto throughput
Compression/decompression
Public key operations
Symmetric crypto offload

Implementation Example

// Via QAT C library FFI

#[link(name = "qat")]
extern "C" {
    fn cpaCySymPerformOp(
        instanceHandle: *const c_void,
        pOpData: *const c_void,
        pSrcBuffer: *const c_void,
        pDstBuffer: *mut c_void,
    ) -> i32;
}

pub struct QatAccelerator {
    instance: *mut c_void,
}

impl QatAccelerator {
    pub fn new() -> Result<Self> {
        // Initialize QAT device
        let instance = unsafe {
            // ... QAT initialization ...
            std::ptr::null_mut()
        };
        
        Ok(Self { instance })
    }
    
    pub fn encrypt_aes(&self, plaintext: &[u8], key: &[u8]) -> Result<Vec<u8>> {
        let mut ciphertext = vec![0u8; plaintext.len()];
        
        unsafe {
            // Setup operation data
            // ... QAT crypto operation ...
            
            cpaCySymPerformOp(
                self.instance,
                std::ptr::null(),
                plaintext.as_ptr() as *const c_void,
                ciphertext.as_mut_ptr() as *mut c_void,
            );
        }
        
        Ok(ciphertext)
    }
    
    pub fn compress(&self, data: &[u8]) -> Result<Vec<u8>> {
        // QAT compression
        // ...
        Ok(vec![])
    }
}

Use Cases

IPsec/SSL termination
Database encryption
Storage compression
VPN gateways

Pros

✅ 100+ Gbps throughput
✅ Low CPU overhead
✅ Multiple algorithms
✅ Enterprise-grade

Cons

❌ Intel hardware only
❌ Complex API
❌ Limited Rust support
❌ Requires dedicated HW

10. NVIDIA BlueField DPU

Overview

Data Processing Unit combining network, storage, and compute acceleration.

Key Features

200+ Gbps networking
Programmable packet processing
Storage acceleration
ARM cores for control plane

Implementation Approach

// Via DOCA SDK

use doca_sys::*;

pub struct BluefieldDpu {
    dev: *mut doca_dev,
}

impl BluefieldDpu {
    pub fn new() -> Result<Self> {
        unsafe {
            let mut dev = std::ptr::null_mut();
            doca_dev_open(&mut dev);
            
            Ok(Self { dev })
        }
    }
    
    pub fn process_packets(&self, packets: &[&[u8]]) -> Result<()> {
        // Offload packet processing to DPU
        // ...
        Ok(())
    }
    
    pub fn rdma_transfer(&self, data: &[u8], remote: u64) -> Result<()> {
        // RDMA operations on DPU
        // ...
        Ok(())
    }
}

Use Cases

Smart NICs
Network function virtualization
Storage offload
Security processing

Pros

✅ Line-rate networking
✅ Compute + network
✅ Offload CPU tasks
✅ Programmable

Cons

❌ NVIDIA hardware only
❌ Expensive
❌ Complex programming
❌ Limited documentation

Integration with Shared-Nothing Library

Architecture

// src/accelerator/mod.rs

pub enum AcceleratorType {
    Cuda,
    Rocm,
    Vulkan,
    Metal,
    Wgpu,
    OpenCl,
    Fpga,
    Tpu,
    Qat,
    Dpu,
}

pub trait Accelerator: Send + Sync {
    fn name(&self) -> &str;
    fn compute(&self, input: &[f32]) -> Result<Vec<f32>>;
    fn async_compute(&self, input: &[f32]) -> Pin<Box<dyn Future<Output = Result<Vec<f32>>>>>;
}

// Worker with accelerator
pub struct AcceleratedWorker<A: Accelerator> {
    accelerator: Arc<A>,
}

impl<A: Accelerator> Worker for AcceleratedWorker<A> {
    type State = Vec<f32>;
    type Message = ComputeTask;
    
    fn handle_message(&mut self, state: &mut State, msg: Envelope<Message>) -> Result<()> {
        let result = self.accelerator.compute(&msg.payload.data)?;
        state.extend(result);
        Ok(())
    }
}

Cargo.toml

[features]
cuda = ["cudarc"]
rocm = ["hip-sys"]
vulkan = ["vulkano"]
metal = ["metal-rs"]
wgpu-compute = ["wgpu"]
opencl = ["ocl"]
fpga = []
tpu = ["tensorflow"]
qat = []
dpu = []

all-accelerators = [
    "cuda",
    "vulkan",
    "metal",
    "wgpu-compute",
    "opencl"
]

Recommendation Matrix

Use Case	Primary	Alternative	Fallback
ML Training	CUDA	TPU	Vulkan
ML Inference	CUDA/TPU	Metal (Apple)	wgpu
General Compute	wgpu	Vulkan	OpenCL
Cross-Platform	wgpu	Vulkan	OpenCL
Apple Devices	Metal	wgpu	-
Ultra-Low Latency	FPGA	QAT	-
Network Offload	DPU	QAT	-
Crypto/Compress	QAT	GPU	CPU

Performance Comparison

// Benchmark results (approximate, varies by workload)

Vector Addition (1M elements):
- CPU (single-threaded):  ~2.0ms
- CUDA:                   ~0.05ms  (40x faster)
- Vulkan:                 ~0.08ms  (25x faster)
- Metal (M1):             ~0.06ms  (33x faster)
- wgpu:                   ~0.10ms  (20x faster)
- OpenCL:                 ~0.12ms  (16x faster)

Matrix Multiply (1024x1024):
- CPU (single-threaded):  ~800ms
- CUDA (cuBLAS):          ~1.5ms   (533x faster)
- TPU:                    ~0.8ms   (1000x faster)
- Metal (MPS):            ~2.0ms   (400x faster)
- Vulkan:                 ~3.0ms   (266x faster)

Crypto (AES-256, 1GB):
- CPU:                    ~500ms
- QAT:                    ~10ms    (50x faster)
- CUDA:                   ~50ms    (10x faster)

Next Steps

Phase 1: Implement wgpu (cross-platform baseline)
Phase 2: Add CUDA support (NVIDIA GPUs)
Phase 3: Add Metal support (Apple Silicon)
Phase 4: Add Vulkan compute (advanced users)
Phase 5: Specialized accelerators (QAT, FPGA)

Document Status: Ready for implementation Last Updated: October 31, 2025 Estimated Implementation: 6-8 weeks for GPU support

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GPU & Hardware Accelerator Options for Shared-Nothing Architecture

Performance Overview

1. CUDA (NVIDIA GPUs)

Overview

Key Features

Rust Crates

rustacuda

cudarc

cuda-sys

Implementation Example

Use Cases

Pros

Cons

2. ROCm (AMD GPUs)

Overview

Key Features

Rust Crates

hip-sys

Implementation Example

Use Cases

Pros

Cons

3. Vulkan Compute

Overview

Key Features

Rust Crates

vulkano

ash

Implementation Example

Use Cases

Pros

Cons

4. Metal (Apple Silicon)

Overview

Key Features

Rust Crates

metal-rs

Implementation Example

Use Cases

Pros

Cons

5. WebGPU/wgpu

Overview

Key Features

Rust Crates

wgpu

Implementation Example

Use Cases

Pros

Cons

6. OpenCL

Overview

Key Features

Rust Crates

ocl

opencl3

Implementation Example

Use Cases

Pros

Cons

7. FPGA (Xilinx/Intel)

Overview

Key Features

Xilinx Vitis/Vivado Approach

Use Cases

Pros

Cons

8. TPU (Tensor Processing Units)

Overview

Key Features

Implementation Approach

Use Cases

Pros

Cons