Distributed Z80 Parallel Execution Engine with Adaptive Instruction Stream Dispatcher

Z80 processor farm concept
Conceptual Z80 processor farm — experimental platform for adaptive parallel execution

Abstract

This project investigates the feasibility of parallel execution of inherently sequential instruction streams by combining architecture-level processor emulation with adaptive scheduling driven by machine learning–assisted analysis.

The system is built around a scalable array of emulated Zilog Z80 processors, coordinated by an intelligent front-end dispatcher operating on an architecture-level representation of program execution. The objective is not to universally parallelize arbitrary programs, but to explore to what extent structured patterns, partial independence, and execution regularities can be identified and exploited in practice.

Core Concept

At the center of the system lies an adaptive instruction stream dispatcher, implemented as a hybrid of:

The dispatcher observes the incoming instruction stream together with its evolving execution state (register flows, memory access patterns, and control-flow transitions). Rather than assuming full decomposability, it incrementally constructs a dependency-aware execution model, identifying regions of conditionally parallelizable computation.

Workload partitioning is therefore not treated as a purely syntactic transformation of instructions, but as a runtime-informed segmentation problem under correctness constraints.

Execution Model

Each Z80 instance is emulated at the architecture level, capturing:

This abstraction allows both the dispatcher and the execution layer to operate on a shared semantic representation, enabling consistent reasoning about data dependencies and execution ordering.

The system constructs a dynamic execution graph (DAG-like structure), where:

Parallel execution is scheduled only where dependency constraints permit, ensuring semantic equivalence with the original sequential execution.

Adaptive Scheduling and Learning

The dispatcher employs a learning component inspired by Reinforcement learning to refine scheduling strategies over time.

Learning is guided by two explicit feedback channels:

The learning component does not replace formal dependency constraints; rather, it operates within them, improving:

This establishes a bounded learning framework, where correctness is guaranteed by construction, while efficiency is subject to adaptive optimization.

Speculative Partitioning

The system supports speculative task partitioning, where candidate decompositions are explored under controlled conditions. Speculative executions are validated against the reference model before being incorporated into the scheduling policy.

This enables gradual discovery of non-trivial execution patterns without compromising determinism or correctness.

System Architecture

The architecture consists of:

The system explicitly acknowledges the cost of communication and synchronization, incorporating these factors into the scheduling objective.

BmysOS Compatibility Layer

Once the underlying execution architecture reaches sufficient maturity, the system can be extended by an additional BmysOS Compatibility Layer positioned above the massively parallel Z80 execution substrate and below end-user software expectations. The purpose of this layer is not to expose the internal distributed topology directly, but to present a coherent logical machine model compatible with software written for BmysOS.

This compatibility layer would act as a system-level mediation interface, translating conventional single-system assumptions of an operating system into services backed by the parallel execution engine underneath. In practical terms, it would provide a stable execution contract for memory visibility, task dispatch, interrupt semantics, device abstraction, and timing behavior, allowing BmysOS to operate as if it were running on a unified Z80-based platform while the actual computation is being supported by the deeper adaptive multi-processor architecture.

Such a layer would create a bridge between the experimental research platform and an already existing, highly capable 8-bit operating system ecosystem, making the project not only architecturally ambitious but also demonstrable through a recognizable software environment. In this sense, BmysOS would serve as a visible proof-of-concept software target for the completed platform, while the compatibility layer would become the boundary at which classical 8-bit operating system assumptions meet the new distributed execution model.

For inspiration regarding the capabilities, user-facing behavior, and architectural significance of BmysOS, see:

Research Objectives

The project aims to experimentally evaluate:

Rather than assuming universal applicability, the project focuses on identifying classes of programs and execution patterns where meaningful parallelization emerges.

Positioning

This work can be viewed as an experimental bridge between:

Conceptually, it aligns with ideas explored in systems such as LLVM and Apache Spark, but reinterpreted in the context of low-level processor emulation and fine-grained execution control.

Conclusion

The project does not claim that arbitrary instruction streams can be fully parallelized. Instead, it formulates a controlled experimental framework in which:

The central research question is therefore not whether sequential computation can be transformed into parallel execution in general, but:

to what extent structured parallelism can be uncovered and exploited in practice when combining formal analysis with adaptive, learning-driven scheduling.