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ferrotorch-gpu

CUDA GPU backend for ferrotorch.

What it provides

GpuDevice -- CUDA device management and context handling
Data transfer -- cpu_to_gpu, gpu_to_cpu, tensor_to_gpu, tensor_to_cpu, alloc_zeros
GpuTensor -- GPU-resident tensor wrapper with cuda() and cuda_default() helpers
CUDA buffer -- CudaBuffer for raw GPU memory, CudaAllocator for memory management
BLAS -- gpu_matmul_f32, gpu_matmul_f64 via cuBLAS, gpu_bmm_* batched
cuSOLVER -- gpu_lu_solve, gpu_cholesky, gpu_eigh, gpu_eig, gpu_lstsq via cuSOLVER
cuFFT -- gpu_fft_*, gpu_rfft_*, gpu_ifft_* interleaved-complex FFTs
Convolution -- gpu_conv2d_f32
Element-wise kernels -- gpu_add, gpu_sub, gpu_mul, gpu_neg, gpu_relu, plus 50+ PTX kernels for reductions, scans, masked ops, scatter/gather, strided copies
Graph capture -- GpuGraphPool for CUDA-graph stream capture and replay
Memory guard -- MemoryGuard, MemoryWatchdog, MemoryStats, OomPolicy for GPU memory management and pressure monitoring

Feature flags

Feature	Default	Description
`cuda`	yes	Links against CUDA driver API via cudarc

Quick start

use ferrotorch_gpu::{GpuDevice, cpu_to_gpu, gpu_to_cpu};

let device = GpuDevice::new(0)?;
let host_data = vec![1.0f32, 2.0, 3.0];
let gpu_buf = cpu_to_gpu(&host_data, &device)?;
let back = gpu_to_cpu(&gpu_buf, &device)?;
assert_eq!(back, host_data);

Part of ferrotorch

This crate is one component of the ferrotorch workspace. See the workspace README for full documentation.

License