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UPIR: Universal Plan Intermediate Representation

Formal verification, automatic synthesis, and continuous optimization of distributed system architectures.

Overview

UPIR (Universal Plan Intermediate Representation) is an open-source framework for formally specifying, verifying, synthesizing, and optimizing distributed system architectures. It bridges the gap between high-level architectural requirements and production-ready implementations.

What UPIR Does

  • Formal Verification: Prove that your architecture satisfies correctness properties using SMT solvers
  • Automatic Synthesis: Generate implementation code from architectural specifications using CEGIS
  • Continuous Optimization: Learn from production metrics to improve architectures using reinforcement learning
  • Pattern Management: Extract and reuse proven architectural patterns
  • Incremental Verification: Cache proofs for faster iteration

Why UPIR?

Designing distributed systems is hard. Traditional approaches rely on:

  • Manual design prone to errors
  • Ad-hoc validation that misses edge cases
  • Trial-and-error optimization that wastes resources
  • Reinventing patterns instead of reusing proven solutions

UPIR automates these processes using formal methods, program synthesis, and machine learning.

Key Features

Formal Specifications with Temporal Logic

Define requirements using Linear Temporal Logic (LTL):

from upir.core.temporal import TemporalOperator, TemporalProperty

# ALWAYS: Data consistency must always hold
always_consistent = TemporalProperty(
    operator=TemporalOperator.ALWAYS,
    predicate="data_consistent"
)

# WITHIN: Respond within 100ms
low_latency = TemporalProperty(
    operator=TemporalOperator.WITHIN,
    predicate="respond",
    time_bound=100  # milliseconds
)

SMT-Based Verification

Verify architectures satisfy specifications:

from upir.verification.verifier import Verifier
from upir.verification.solver import VerificationStatus

verifier = Verifier()
results = verifier.verify_specification(upir)

if results.status == VerificationStatus.PROVED:
    print("✓ Architecture verified!")

CEGIS Synthesis

Generate code from specifications:

from upir.synthesis.cegis import Synthesizer

synthesizer = Synthesizer(max_iterations=10)
result = synthesizer.synthesize(upir, sketch)

if result.status.value == "SUCCESS":
    print(f"Generated code:\n{result.implementation}")

Reinforcement Learning Optimization

Optimize architectures from production metrics:

from upir.learning.learner import ArchitectureLearner

learner = ArchitectureLearner(upir)
optimized_upir = learner.learn(production_metrics, episodes=100)
print(f"Improved cost by {improvement}%")

Pattern Library

Store and reuse proven patterns:

from upir.patterns.library import PatternLibrary

library = PatternLibrary()
matches = library.match_architecture(upir, threshold=0.8)

for pattern, similarity in matches:
    print(f"{pattern.name}: {similarity:.2%} match")

Attribution

This is a clean-room implementation based solely on public sources:

Primary Source: Automated Synthesis and Verification of Distributed Systems Using UPIR by Subhadip Mitra, published at TD Commons under CC BY 4.0 license.

Author: Subhadip Mitra

License: Apache 2.0

Project Status: Personal open source project, no affiliations

Getting Help

Next Steps

Ready to get started?

  1. Install UPIR
  2. Follow the Quick Start Guide
  3. Explore Core Concepts
  4. Try the Streaming Pipeline Example