pointerflow 0.1.0

High-performance Python library for zero-copy memory mapping.

PointerFlow

PointerFlow is a high-performance library for Python designed to eliminate the data transfer bottleneck between CPU and GPU. By using zero-copy memory mapping, it allows processors to share a common physical memory space completely asynchronously. It is powered by C++ and custom-made CUDA kernels.

The Edge of Low Latency

In applications where milliseconds also count, PointerFlow is faster than conventional frameworks like PyTorch in real-time data flow management applications.

• Round-Trip Latency: Reduced from 0.86 ms (industry standard) to 0.14 ms.
• Dispatch Overhead: Return of control to Python in < 0.1 ms using non-blocking CUDA Streams.
• True Zero-Copy: Complete elimination of cudaMemcpy. Data written to a NumPy view is instantly available to the GPU via the PCIe bus.

Technical Architecture

PointerFlow combines the efficiency of C++20 with the flexibility of Python through a low-level integration.

1. Memory Management (RAII): Automatic lifecycle management of Pinned Memory and CUDA Streams to prevent memory leaks in high-frequency executions.
2. NumPy Integration: Creating memory views using py::capsule, allowing Python to manipulate data in RAM that is read directly by the GPU.
3. Asynchronous Execution: Native support for CUDA kernels fired in independent streams, allowing real overlap of computation and business logic.