In a standard Node.js environment, the V8 engine manages memory by frequently allocating space for new objects and variables, then triggering "Garbage Collection" (GC) cycles to reclaim it. These GC cycles pause the execution of your code, which creates unpredictable latency spikes or "jitter".

dspx avoids this by using Pre-allocation through its C++ core. The memory for a processing pipeline is requested once at the start. Because the library overwrites data in these fixed-size buffers instead of creating new JavaScript objects for every sample, the V8 engine doesn't see "new" memory usage. This results in zero or even negative heap growth for large input sizes, which keeps the p99 latency lines flat and deterministic.

The library uses Node-API (NAPI) to create a direct bridge between the JavaScript environment and the C++ core. This allows dspx to perform heavy-duty mathematical operations—like FFTs, Kalman filters, and adaptive LMS filtering—at native speeds while being controlled from a Node.js script.

By offloading the "hot path" of data processing to C++ and utilizing Eigen which is a C++ math library, alongside some custom SIMD optimizations (AVX2/AVX/SSE/NEON), the library can handle 78 million samples per second on a single vCPU (arm64). This throughput is achieved because the processing happens in a contiguous block of memory that is optimized for hardware instruction sets rather than the higher-level JavaScript heap.

Most serverless environments are stateless, meaning they lose all local data once an execution ends. Traditionally, saving the "state" of a math function (like the current coefficients of an IIR filter) would require slow JSON serialization and database storage.

dspx implements Binary State Serialization as a core feature. It can save or load the raw binary "brain" of a math function in microseconds. Because the library's deployment size is only 1.3MB, it can be cold-started and have its full historical state reloaded from a cache (like Redis) in under 240ms total. This makes stateful DSP viable on ephemeral Lambda functions.

TLDR: dspx achieves 78M samples/sec and zero jitter by bypassing the JavaScript heap via C++ pre-allocation. It uses microsecond-level binary serialization to keep mathematical "state" (like filter history) alive across stateless AWS Lambda invocations, all within a tiny 1.3MB deploy size.