CUDA Backend

The CUDA backend provides high-performance tensor operations on NVIDIA GPUs using the CUDA toolkit. It offers the highest performance for supported operations and integrates well with the broader CUDA ecosystem.

Features

• Peak Performance: Optimized kernels for maximum NVIDIA GPU utilization
• Optimized Kernels: Hardware-accelerated tensor operations
• Memory Optimization: Efficient GPU memory management
• Mature Ecosystem: Integration with existing CUDA libraries
• Production Ready: Battle-tested in production environments

Installation

Prerequisites

CUDA Toolkit: Install NVIDIA CUDA Toolkit 11.0 or later

• Download from NVIDIA Developer
• Ensure nvcc is in your PATH
• Verify installation: nvcc --version

Compatible GPU: NVIDIA GPU with compute capability 3.5+

• Check compatibility: nvidia-smi
• Verify compute capability: deviceQuery (CUDA samples)

Cargo Configuration

Enable the CUDA backend:

[dependencies]
tensor_frame = { version = "0.0.1-alpha", features = ["cuda"] }

Build Requirements:

• CUDA Toolkit installed
• NVIDIA GPU drivers
• C++ compiler (MSVC on Windows, GCC/Clang on Linux)

System Requirements

Hardware

• GPU: NVIDIA GPU with compute capability 3.5+
• Memory: Sufficient GPU memory for tensor operations
• PCIe: PCIe 3.0 x16 recommended for optimal memory bandwidth

Software

• CUDA Toolkit: Version 11.0+ (12.0+ recommended)
• Driver: NVIDIA driver supporting your CUDA version
• OS: Linux (preferred), Windows 10+, WSL2

Verified Configurations

Implementation Details

Storage

CUDA tensors use device memory pointers:

#![allow(unused)]
fn main() {
pub struct CudaStorage {
    pub ptr: *mut f32,    // Raw CUDA device pointer
    pub len: usize,       // Buffer length in elements
}
}

Memory Properties:

• Location: GPU global memory (VRAM)
• Layout: Contiguous, row-major layout
• Alignment: 256-byte aligned for optimal coalescing
• Synchronization: Explicit via CUDA streams

Kernel Implementation

Operations use optimized CUDA kernels:

// Element-wise addition kernel
__global__ void add_kernel(const float* a, const float* b, float* c, int n) {
    int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < n) {
        c[idx] = a[idx] + b[idx];
    }
}

Performance Characteristics

Strengths

• Compute Throughput: Maximum FP32/FP16 throughput on NVIDIA hardware
• Memory Bandwidth: Optimal utilization of GPU memory bandwidth
• Kernel Optimization: Hand-tuned kernels for each operation
• Library Integration: Designed for future integration with cuDNN, etc.

Performance Metrics

Example performance on RTX 4090:

Optimization Guidelines

Optimal Use Cases

#![allow(unused)]
fn main() {
// Large tensor operations
let a = Tensor::zeros(vec![4096, 4096])?;
let b = Tensor::zeros(vec![4096, 4096])?;
let c = (a * b) + 1.0;  // Excellent GPU performance

// Batch operations
for batch in large_dataset {
    let result = model.forward(batch)?;  // Amortizes GPU overhead
}

// Memory-bound operations
let result = ((a * b) + c) / d;  // GPU memory bandwidth utilized
}

Suboptimal Use Cases

#![allow(unused)]
fn main() {
// Very small tensors
let tiny = Tensor::ones(vec![8, 8])?;  // Kernel launch overhead dominates

// Frequent host-device transfers
let gpu_result = cpu_tensor.to_backend(BackendType::Cuda)?;
let back_to_cpu = gpu_result.to_vec()?;  // PCIe bandwidth bottleneck

// Scalar reductions with immediate use
let sum = tensor.sum(None)?.to_vec()?;  // Forces synchronization
}

Memory Management

Device Memory Allocation

CUDA tensors allocate GPU memory directly:

#![allow(unused)]
fn main() {
// Allocates 64MB of GPU memory
let large_tensor = Tensor::zeros(vec![4096, 4096])?
    .to_backend(BackendType::Cuda)?;
}

Memory Pool Management

The backend uses a memory pool for efficient allocation:

#![allow(unused)]
fn main() {
// Pool reduces allocation overhead
let tensors: Vec<Tensor> = (0..100)
    .map(|_| Tensor::zeros(vec![1024, 1024]))
    .collect::<Result<Vec<_>>>()?;
}

Memory Transfer Optimization

#![allow(unused)]
fn main() {
// Efficient: Batch transfers
let gpu_tensors = cpu_tensors
    .into_iter()
    .map(|t| t.to_backend(BackendType::Cuda))
    .collect::<Result<Vec<_>>>()?;

// Inefficient: Individual transfers  
for cpu_tensor in cpu_tensors {
    let gpu_tensor = cpu_tensor.to_backend(BackendType::Cuda)?;
    process(gpu_tensor)?;
}
}

Memory Debugging

Monitor GPU memory usage:

# Check GPU memory
nvidia-smi

# Continuous monitoring
watch -n 1 nvidia-smi

#![allow(unused)]
fn main() {
// Check available memory
let (free, total) = cuda::memory_info()?;
println!("GPU memory: {}/{} MB", free / 1024 / 1024, total / 1024 / 1024);

// Handle out-of-memory
match Tensor::zeros(vec![16384, 16384]).and_then(|t| t.to_backend(BackendType::Cuda)) {
    Ok(tensor) => println!("Allocated 1GB GPU tensor"),
    Err(TensorError::BackendError(msg)) if msg.contains("memory") => {
        eprintln!("GPU OOM, trying smaller allocation");
    }
    Err(e) => eprintln!("CUDA error: {}", e),
}
}

Error Handling

CUDA operations can fail for various hardware and software reasons:

Runtime Errors

#![allow(unused)]
fn main() {
use tensor_frame::{Tensor, TensorError};

match tensor_operation() {
    Ok(result) => process(result),
    Err(TensorError::BackendError(msg)) => {
        if msg.contains("out of memory") {
            // GPU memory exhausted
            fallback_to_cpu()?;
        } else if msg.contains("invalid device") {
            // GPU not available or driver issue
            retry_with_cpu_backend()?;
        } else {
            // Other CUDA error
            eprintln!("CUDA error: {}", msg);
        }
    }
}
}

Common Error Scenarios

• GPU Out of Memory: Tensor too large for available GPU memory
• Invalid Device: GPU not found or not compatible
• Driver Mismatch: CUDA driver version incompatible
• Kernel Launch Failed: Invalid kernel parameters or GPU fault
• Memory Access Violation: Invalid GPU memory access

Error Recovery

#![allow(unused)]
fn main() {
// Graceful fallback strategy
fn robust_tensor_operation(tensor: Tensor) -> Result<Tensor> {
    // Try CUDA first
    if let Ok(cuda_tensor) = tensor.to_backend(BackendType::Cuda) {
        match cuda_operation(cuda_tensor) {
            Ok(result) => return Ok(result),
            Err(TensorError::BackendError(_)) => {
                // CUDA failed, fall back to CPU
                eprintln!("CUDA operation failed, falling back to CPU");
            }
        }
    }
    
    // CPU fallback
    cpu_operation(tensor.to_backend(BackendType::Cpu)?)
}
}

Debugging and Profiling

CUDA Debugging Tools

NVIDIA Nsight Systems: System-wide performance analysis

nsys profile --stats=true ./your_app

NVIDIA Nsight Compute: Kernel-level profiling

ncu --metrics sm__throughput.avg.pct_of_peak_sustained_elapsed ./your_app

cuda-memcheck: Memory error detection

cuda-memcheck ./your_app

Performance Analysis

#![allow(unused)]
fn main() {
// GPU timing with CUDA events
use std::time::Instant;

let start = Instant::now();
let result = gpu_tensor_a.matmul(&gpu_tensor_b)?;
// Note: matmul is asynchronous!
let _sync = result.to_vec()?;  // Force synchronization
let elapsed = start.elapsed();
println!("Matrix multiplication took: {:?}", elapsed);
}

Memory Leak Detection

#![allow(unused)]
fn main() {
// Monitor for memory leaks in long-running applications
fn check_memory_usage() -> Result<()> {
    let (free_before, total) = cuda::memory_info()?;
    
    // Perform operations
    {
        let tensor = Tensor::zeros(vec![1000, 1000])?.to_backend(BackendType::Cuda)?;
        let result = expensive_operation(tensor)?;
    } // tensor should be freed here
    
    let (free_after, _) = cuda::memory_info()?;
    
    if free_after < free_before {
        eprintln!("Potential memory leak detected!");
        eprintln!("Memory delta: {} MB", (free_before - free_after) / 1024 / 1024);
    }
    
    Ok(())
}
}

Production Deployment

Docker Configuration

# Use NVIDIA CUDA base image
FROM nvidia/cuda:12.0-devel-ubuntu20.04

# Install Rust
RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
ENV PATH="/root/.cargo/bin:${PATH}"

# Copy and build your application
COPY . /app
WORKDIR /app
RUN cargo build --release --features cuda

# Runtime with CUDA
FROM nvidia/cuda:12.0-runtime-ubuntu20.04
COPY --from=0 /app/target/release/your_app /usr/local/bin/
CMD ["your_app"]

Kubernetes Deployment

apiVersion: v1
kind: Pod
spec:
  containers:
  - name: tensor-app
    image: your-app:latest
    resources:
      limits:
        nvidia.com/gpu: 1
    env:
    - name: CUDA_VISIBLE_DEVICES
      value: "0"

Environment Variables

# Limit GPU memory growth
export CUDA_MEMORY_POOL_TYPE=pool

# Enable GPU timing
export CUDA_LAUNCH_BLOCKING=1

# Select specific GPU
export CUDA_VISIBLE_DEVICES=0

Optimization Best Practices

Memory Access Patterns

#![allow(unused)]
fn main() {
// Coalesced memory access (efficient)
let result = tensor_a + tensor_b;  // Sequential element access

// Strided access (less efficient)
let transposed = tensor.transpose()?;  // May require memory reshape
}

Kernel Fusion

#![allow(unused)]
fn main() {
// Fused operations (single kernel launch)
let result = ((a * b) + c).relu();  // Ideally fused into one kernel

// Separate operations (multiple kernel launches)
let temp1 = a * b;
let temp2 = temp1 + c;
let result = temp2.relu();  // Three separate kernels
}

Stream Management

#![allow(unused)]
fn main() {
// Future: Async operations with CUDA streams
// Currently synchronous, but optimizations planned
let stream_a = cuda::create_stream()?;
let stream_b = cuda::create_stream()?;

// Parallel execution on different streams
let result_a = tensor_a.sum(None).execute_on(stream_a)?;
let result_b = tensor_b.mean(None).execute_on(stream_b)?;
}

Integration with CUDA Ecosystem

cuDNN (Future)

Planned integration for neural network operations:

#![allow(unused)]
fn main() {
// Future: Convolution operations
let output = input.conv2d(&kernel, stride, padding)?;
}

NCCL (Future)

Multi-GPU communication for distributed computing:

#![allow(unused)]
fn main() {
// Future: Multi-GPU operations
let distributed_result = tensor.all_reduce_sum()?;
}