Runtime Daemon & Memory Mapping
Technical internals on zero-copy unified memory allocation, thread isolation, and process scheduling.
The Kanna runtime daemon is written in systems-grade Rust, prioritizing deterministic execution times and strict memory isolation.
Zero-Copy Memory Mapping
Traditional machine learning runtimes load model weights into RAM, parse protobuf structures, and then copy tensors across PCIe buses into GPU memory. This approach doubles memory footprint and causes perceptible system stutter.
Kanna uses direct memory-mapped I/O (mmap) paired with Apple Metal MTLResourceStorageModeShared or NVIDIA Unified Memory:
// Zero-copy weight mapping in Rust
let file = File::open(&weight_path)?;
let mmap = unsafe { MmapOptions::new().map(&file)? };
let tensor_buffer = device.create_buffer_from_slice(&mmap[..])?;
This guarantees that Kanna’s memory footprint is clamped at precisely 1.42 GB, with zero page swap allocations during operation.
Thread Priority and CPU Throttling
The daemon assigns its core polling loop to QOS_CLASS_BACKGROUND on macOS and SCHED_IDLE on Linux. If the system experiences high CPU contention (e.g. parallel rustc or clang compilation), Kanna immediately yields time slices to ensure developer compile times remain unhindered.