WHITE PAPER: EXECUTIVE PROCESSOR DEVICE (EPD) AND CALCULATION ENGINE ORCHESTRATION SYSTEM
1. EXECUTIVE SUMMARY
This document presents the conceptual architecture and operational orchestration of the Executive Processor Device (EPD) — a high-precision computational system designed for rule-based, AI-assisted, and database-anchored calculation orchestration. The EPD’s goal is to query, resolve, and execute every aspect of a computation — including definitions, constants, operators, operands, and metadata — through a modular orchestration framework.This white paper explains how throughput is balanced between the calculation engine, the database query orchestration layer, and the processor throughput model. It also introduces the class structure and object model in generic pseudocode (non-language-specific), illustrating how the EPD orchestrates data and ensures deterministic, auditable computations.
2. SYSTEM OVERVIEW
The EPD represents an intelligent middleware layer that sits between input data and decision outputs. It transforms abstract business formulas into dynamic, query-driven computational workflows.Core Components:
- Calculation Engine – Executes formulas and equations dynamically.
- Definition Repository (Database) – Stores definitions, constants, operators, operand types, and version history.
- Orchestration Layer – Manages data flow, queuing, and threading for throughput efficiency.
- Query Resolver – Retrieves all dependencies for a calculation.
- Result Pipeline – Aggregates, validates, and logs results for downstream analytics.
3. ARCHITECTURAL FLOW
The EPD follows a five-stage orchestration model:Stage 1: Definition Query Phase
- The system begins with a Calculation Request Object (CRO) that includes an equation name or unique identifier.
- The Query Resolver queries the database for:
- Formula definition
- Constants
- Operators (e.g., +, −, ÷, ×, %, etc.)
- Operand definitions (data fields, variables, or references)
- Data source connections
- Precision and tolerance rules
Stage 2: Dependency Compilation
- The Query Resolver builds a Calculation Graph, mapping:
- Parent and child dependencies
- Data lineage (source-to-target relationships)
- Execution order
- This stage ensures no redundant database queries and prepares data for parallel execution.
Stage 3: Execution Orchestration
- The Calculation Engine spawns micro-threads or concurrent tasks based on the dependency graph.
- Each node in the graph represents a Calculation Node Object (CNO) with properties such as:
- Operation type (Arithmetic / Logical / Statistical)
- Data inputs
- Execution priority
- Processor allocation slot
Stage 4: Processor and Database Throughput
- The Processor Scheduler manages computational throughput:
- High-volume constants are cached in memory.
- Operand retrieval uses pre-indexed queries.
- Query batches are throttled to avoid I/O saturation.
- Execution order dynamically adjusts based on processor load.
Stage 5: Aggregation and Audit
- All executed results are stored with traceable metadata:
- Timestamp
- Operator chain
- Inputs and outputs
- Execution time per node
- The Audit Logger persists this data in an immutable transaction log for traceability and governance.
4. DATABASE MODEL
The database operates as a Calculation Definition Repository (CDR).It is optimized for query performance and normalization, ensuring definitions can be reused dynamically.
Core Entities:
| Entity | Description |
|---|---|
| CalculationDefinition | Holds the unique identifier, name, and equation metadata. |
| ConstantDefinition | Stores all constants and their version history. |
| OperatorDefinition | Maps symbolic operators (+, −, ×, ÷, etc.) to executable methods. |
| OperandDefinition | Defines data inputs, including their source, datatype, and constraints. |
| ExecutionLog | Captures all runtime execution events and performance metrics. |
| PrecisionRule | Defines rounding, decimal, and accuracy policies. |
| CalculationResult | Final storage of computed values and contextual metadata. |
5. THROUGHPUT STRATEGY
To maximize throughput and scalability, the EPD uses parallelized query and compute orchestration.5.1 Processor Throughput
- Each calculation node is assigned a Processor Token, granting it access to available compute cycles.
- The system dynamically scales threads based on:
- CPU core utilization
- Query response latency
- Queue backlog
- The scheduler applies adaptive throttling — reducing the load when database response times increase.
5.2 Database Throughput
- Constants and formula fragments are pre-fetched into an in-memory cache layer.
- Complex joins are replaced with denormalized views or stored procedures optimized for lookup speed.
- Each query round trip is tracked to identify high-latency segments and optimize them over time.
5.3 Orchestration Balance
- The orchestrator uses a feedback loop:
- Monitor → Evaluate → Adjust thread pool size → Re-schedule.
- Resulting throughput approximates a self-optimizing pipeline that keeps CPU and I/O within ideal utilization thresholds.
6. DATA FLOW OVERVIEW
Code:
[User or System Request]
↓
[EPD Input Layer]
↓
[Query Resolver] → [Database Definitions: Constants, Operators, Operands]
↓
[Dependency Graph Builder]
↓
[Calculation Engine (Parallel Execution)]
↓
[Aggregation and Logging]
↓
[Result Storage and Output Delivery]7. ORCHESTRATION LOGIC (Conceptual)
Step 1: Intake
- Receive a calculation request such as “Compute Net Operating Margin.”
- Query all related constants, such as
OperatingRevenue,OperatingCost,TaxRate.
Step 2: Graph Assembly
- The graph engine identifies dependencies:
OperatingMargin = (OperatingRevenue - OperatingCost) / OperatingRevenue
Step 3: Execution
- Spawn threads:
- Thread A: Query revenue dataset.
- Thread B: Query cost dataset.
- Thread C: Perform subtraction and division after both complete.
Step 4: Audit
- Record each operation node with start/stop times and result.
Step 5: Result Delivery
- Send result to requesting service or dashboard.
8. PSEUDOCODE CLASS MODEL (Generic, Non-Language)
Code:
Class CalculationRequest
+ Id
+ FormulaName
+ InputParameters
+ ContextMetadata
Class QueryResolver
+ GetCalculationDefinition(request)
+ GetConstants(definition)
+ GetOperators(definition)
+ GetOperands(definition)
Class CalculationGraph
+ Nodes (List<CalculationNode>)
+ BuildDependencyTree(definition)
+ ValidateCompleteness()
Class CalculationNode
+ NodeId
+ OperationType
+ InputOperands
+ OutputValue
+ Dependencies
+ Execute()
Class CalculationEngine
+ ExecuteGraph(graph)
+ ManageThreads()
+ MonitorThroughput()
+ LogResults()
Class AuditLogger
+ WriteExecutionLog(node)
+ WriteSummary(result)9. GOVERNANCE AND TRACEABILITY
- Every calculation is deterministic and auditable.
- Historical versions of formulas and constants are preserved for compliance.
- The EPD supports “Replay Mode”, allowing recalculation with identical parameters to verify historical results.
10. EXECUTIVE-LEVEL BENEFITS
| Category | Benefit |
|---|---|
| Governance | Full traceability of how each number is derived. |
| Scalability | Processor-aware parallel execution allows large data computations in real time. |
| Maintainability | Updating definitions in one place automatically updates every dependent formula. |
| Transparency | Executives can view the “why” behind every financial or operational metric. |
| AI-Readiness | Designed to integrate with AI reasoning engines for predictive calculations. |
11. CONCLUSION
The EPD Calculation Engine transforms static formulas into living, dynamic, query-driven orchestration systems.By tightly coupling the calculation definitions with real-time query resolution, parallel execution, and full auditability, the EPD becomes the central nervous system of computational governance within an enterprise.
In essence, the EPD is not just a calculator — it is an executive-grade decision instrument, capable of explaining, validating, and optimizing every result it produces.
