Pinned Memory CUDA

Pinned (Page-Locked) Memory is host memory that is locked in physical RAM and cannot be swapped to disk — enabling the GPU to access host memory directly via DMA without CPU involvement and allowing asynchronous (overlapping) memory transfers.

Why Pinned Memory?

- Regular (pageable) memory: CPU can swap pages to disk. DMA transfer requires:
1. Allocate temporary pinned buffer.
2. Copy from pageable → pinned (CPU).
3. DMA transfer pinned → GPU.
- Double copy, synchronous.

Pinned memory: Skip step 1-2 → DMA directly from host.
1.5–2x faster transfer bandwidth.
Enables `cudaMemcpyAsync` — true asynchronous transfer.

Allocating Pinned Memory

float* h_data;
cudaMallocHost(&h_data, size);     // Pinned allocation
cudaFreeHost(h_data);              // Free pinned memory

// Async transfer (non-blocking)
cudaMemcpyAsync(d_data, h_data, size, cudaMemcpyHostToDevice, stream);

Zero-Copy Memory

Map pinned host memory into GPU address space.
GPU accesses host memory directly via PCIe (no explicit transfer).
`cudaHostAlloc(ptr, size, cudaHostAllocMapped)`
Useful when: Data accessed once (transfer + use = same latency as zero-copy), or host memory larger than GPU memory.
Slower than transfer + compute: PCIe bandwidth ~16 GB/s vs. GPU memory ~900 GB/s.

When to Use Pinned Memory

Always: For streaming/pipelined workloads with `cudaMemcpyAsync`.
Large transfers: Bandwidth gain justifies pinning overhead.
High-frequency small transfers: Saves per-transfer staging cost.

When NOT to Overuse

Pinned memory cannot be swapped → reduces available virtual memory.
Over-allocation: System runs low on physical memory → performance degradation.
Rule: Pin only the buffers actively used for DMA transfers.

Pinned memory is a prerequisite for achieving peak PCIe bandwidth and enabling the transfer-compute overlap that allows GPU inference and training pipelines to saturate GPU compute without waiting for data transfers.

Pinned Memory CUDA

Take pinned memory cuda further