Architecture
Why analytical data systems are difficult
New instrument models often involve advanced technology whose real performance characteristics are not fully determined from theory alone. Without a solid, written validation approach, it is difficult to design software with confidence. In addition, new software must often support upgraded instruments derived from previous models, while preserving access to historical archived data for review and re-processing.
Presentation / Controller / Model style (PVC / HMVC)
For complex instrument systems, a hierarchical model-view-controller style (often described as Presentation–Abstraction–Controller or HMVC) is a strong match. Model components represent coherent business capabilities (e.g., MS processing, calibration, diagnostics), controllers coordinate logic and presentation behavior, and views remain as thin as possible.
Hardware abstraction layer
A layered approach separates:
- Top-level machine state controller: controls global system state and safe transitions
- Fundamental machine interface: common command/event handling and transport glue
- Concrete machine interfaces: device-specific implementations
Machine state model
A clear state model prevents unsafe operations and simplifies diagnostics. A practical state set includes: Off, Initializing, Calibrating, Diagnostic, Maintenance, Busy, Ready, Preparing for Run, Ready to Run, Run, Acquisition in progress, Warning, Error, Stop.
High-resolution throughput and memory topology
Modern LC–TOFMS systems routinely operate at acquisition rates that generate sustained memory pressure. A single spectrum may contain ~250k points; with a 2.5 GS/s 12-bit ADC, raw storage can be ~1 MB per spectrum. With 10–20 Hz acquisition, a single instrument may generate ~10–20 MB/sec of raw data.
Sub-second chromatographic peak widths are now common, and 24/7 high-throughput operation is routine in factory environments. Under these conditions, memory management becomes a core architectural constraint.
In practice, long-duration instability from heap fragmentation manifested most prominently on Windows platforms. Unix/Linux-based systems including macOS showed significantly more stable behavior under identical workloads. The difference reflects allocator behavior and free-list topology under sustained mixed-size allocation patterns.
Deterministic large-block allocation strategy
HRMS processing often requires large contiguous buffers repeatedly. General-purpose heaps can lose the ability to provide sufficiently large contiguous regions when small allocations churn over time.
A practical mitigation is to reserve and recycle a comfortable number of large linear blocks (e.g., 1 MB or larger, depending on instrument configuration) using an application-managed free list. By controlling reuse patterns and reducing heap entropy (as discussed in classical analyses of dynamic storage allocation algorithms), long-duration behavior becomes predictable, and the system can theoretically run indefinitely without fragmentation-induced failure.