Heterogeneous Execution
Distributed execution inevitably introduces operational diversity.
Execution may occur on different processors, operating systems, virtualization layers, cloud providers, geographic regions, and network environments.
Forge does not attempt to eliminate this diversity.
Instead, Forge defines which properties of execution must remain invariant regardless of where execution occurs.
The objective of heterogeneous execution is therefore not identical infrastructure.
It is stable computational truth.
Why Heterogeneous Execution Exists
Modern distributed systems rarely execute on homogeneous infrastructure.
Production workloads naturally span:
- different CPU architectures
- different operating systems
- cloud and on-premise environments
- virtualized and bare-metal infrastructure
- geographically distributed execution nodes
- execution agents with different performance characteristics
Operational diversity is therefore the normal state of distributed computing rather than an exceptional condition.
Forge treats heterogeneous execution as a fundamental design assumption.
Two Kinds of Determinism
Distributed systems often use the word determinism without distinguishing what is actually expected to remain identical.
Forge separates two fundamentally different concepts.
Operational Determinism
Operational determinism describes infrastructure behavior.
Examples include:
- identical execution agents
- identical processor models
- identical operating systems
- identical scheduling decisions
- identical network routes
- identical execution timing
- identical geographic placement
Forge does not attempt to preserve operational determinism.
Operational variability is expected.
Computational Determinism
Computational determinism describes execution semantics.
It asks a different question:
Does the same execution contract produce the same computational truth under the same execution doctrine?
Forge is designed to preserve computational determinism.
Infrastructure may change.
Execution truth should not.
Execution Invariants
Computational determinism depends on preserving execution invariants.
The following execution properties define computational identity and must remain stable throughout the lifetime of an execution:
- execution contract
- primitive identity
- primitive version
- profile identity
- profile version
- canonical arguments
- execution policy
- seed discipline
- deterministic shard planning
- reducer semantics
- replay metadata
These properties define what the computation is.
Changing any of them creates a different execution.
Operational Variables
Other execution properties intentionally remain flexible.
These include:
- processor architecture
- operating system
- execution agent
- virtualization layer
- cloud provider
- geographic region
- network path
- transport protocol
- scheduler placement
- execution timing
- resource allocation
These influence operational behavior.
They do not define computational identity.
Runtime Doctrine
Execution correctness is evaluated against execution invariants rather than infrastructure similarity.
Equivalent execution environments are therefore defined by computational semantics—not by hardware identity.
This distinction allows Forge to execute across heterogeneous infrastructure while preserving a consistent execution model.
Floating-Point Considerations
Floating-point arithmetic introduces practical challenges for distributed execution.
Different processors, compiler optimizations, hardware instruction sets, or reduction ordering may produce small numerical differences even when executing equivalent computations.
Forge therefore distinguishes between:
- computational equivalence
- numerical representation
- operational implementation
Where workload classes require strict numerical stability, reducers, aggregation semantics, and execution policies must preserve deterministic behavior appropriate to the workload.
Forge intentionally does not claim universal bit-for-bit identity across all heterogeneous execution environments.
Instead, determinism is defined according to documented execution semantics.
Workload Classes
Different computational workloads require different determinism models.
Examples include:
| Workload Class | Primary Determinism Requirement |
|---|---|
| Monte Carlo Simulation | Statistical reproducibility |
| Financial Analytics | Stable numerical aggregation |
| Optimization | Convergent execution behavior |
| Scientific Simulation | Reproducible computational semantics |
| Machine Learning Inference | Stable model execution |
| Search & Discovery | Deterministic reduction |
| Graph Analytics | Consistent traversal semantics |
The appropriate determinism guarantee depends on the workload being executed.
Forge therefore treats determinism as workload-aware rather than universally identical.
Supported Determinism Guarantees
Forge execution is designed to preserve:
- execution contract identity
- primitive and profile identity
- deterministic execution planning
- deterministic reduction semantics
- controlled seed behavior
- replay compatibility
- verification consistency
- computational reproducibility within the documented execution model
These guarantees define the computational behavior of the system.
What Forge Does Not Guarantee
Forge intentionally does not guarantee:
- identical processors
- identical operating systems
- identical execution agents
- identical network paths
- identical execution timing
- identical infrastructure topology
- identical cloud providers
- universal bit-for-bit equality across all heterogeneous hardware
Forge preserves computational truth.
It does not preserve operational coincidence.
Practical Mental Model
A useful way to understand heterogeneous execution is:
Operational Diversity
│
▼
Execution Contract
│
▼
Execution Invariants
│
▼
Distributed Execution
│
▼
Deterministic Reduction
│
▼
Computational TruthInfrastructure may vary.
Execution semantics must remain stable.
Relationship to Verification
Verification determines whether execution faithfully followed the execution contract.
Heterogeneous execution defines which execution properties must remain invariant despite operational diversity.
Together they establish computational trust across distributed infrastructure.
Verification answers:
Was this execution faithful?
Heterogeneous execution answers:
Which aspects of execution were required to remain stable?
These are complementary responsibilities within the Forge execution model.
Relationship to Replay
Replay relies on the same execution invariants that define heterogeneous execution.
Because computational identity is preserved independently of infrastructure, replay evaluates execution semantics rather than infrastructure configuration.
Replay therefore reproduces execution according to the original execution contract, not according to the original hardware environment.
Execution Doctrine
Forge does not define determinism as identical infrastructure.
Forge defines determinism as preserved computational semantics.
This distinction allows distributed execution to scale across heterogeneous infrastructure without coupling computational truth to operational implementation.
Execution contracts define intent.
Execution invariants preserve identity.
Verification establishes integrity.
Replay preserves evidence.
Together they allow heterogeneous infrastructure to produce reproducible computational truth.