Split Compilation: Modularity and Performance

• Split compilation is a modular technique that decomposes programs, circuits, or graphs into independent segments for targeted optimization.
• It employs methods like segment extraction, independent compilation, and synthesis to ensure the final recombined artifact matches the original semantics.
• The approach is applied in diverse domains—from classical programming to quantum circuit design—demonstrating significant speedups and practical performance gains.

Split compilation refers to the process of dividing a program, circuit, or specification into multiple segments or modules, each of which can be compiled, optimized, analyzed, or otherwise manipulated independently, and subsequently recombined into a single executable or interpretable artifact. The rationale for split compilation arises across domains—software engineering, logic programming, quantum compilation, and neural network optimization—with goals such as modularity, parallelism, scalability, metric-driven tuning, and even intellectual property (IP) protection.

1. Foundational Principles of Split Compilation

Split compilation involves decomposing the input (program, circuit, graph) into distinct units, each subject to separate compilation passes or independent manipulation before the final assembly. The key technical mechanisms include:

• Segment Extraction: Identification of units such as loop nests in C programs, subgraphs in neural network graphs, or modules/blocks in logic or quantum circuits.
• Independent Compilation: Each segment is compiled, optimized, or evaluated independently—potentially using different strategies, compilers, or tools.
• Synthesis or Linking: The compiled segments are assembled via linking, invocation dispatch, or circuit wiring into a final artifact whose semantics (program behavior, circuit unitary, or logic answerset) matches that of the monolithic input.
• Metric-Driven Selection: Segments may be compiled under metrics such as performance, energy, circuit fidelity, or code size, either by empirical profiling or predictive modeling.

These foundational ideas are instantiated in domain-specific workflows such as meta-compilation frameworks for code optimization (Shivam et al., 2019), separate compilation of logic modules (Holte et al., 2023), hierarchical quantum compilation (Jeng et al., 14 Jan 2025), split assembly of neural network subgraphs (Chen et al., 2023), and partial or split circuit compilation for quantum IP protection (Zhang et al., 6 Nov 2025).

2. Engineering Realizations Across Domains

Classical Program Compilation

The MCompiler framework demonstrates split compilation by extracting loop nests ("code segments") from C programs. Each loop nest is compiled with a selection of compilers/optimizers, empirically profiled (or ML-predicted) for optimal performance, and re-linked into the base executable. The process guarantees ABI compatibility through a uniform calling convention, and can optimize for diverse per-segment metrics, including performance and energy consumption (Shivam et al., 2019). The design achieves a geometric mean speedup of 1.96× (vectorized) to 2.62× (parallelized) across benchmarks.

Separate Compilation in Logic Programming

In logic programming, modules are formalized as existentially quantified blocks of Horn clauses, with signatures mediating the visibility and linkage of names (Holte et al., 2023). Holte and Nadathur's architecture allows every module to be compiled once—emitting code with unresolved references for imports—and then linked in a later phase that performs name resolution, α-renaming, and (optionally) inlining. The key correctness result is an observational equivalence: separately compiled and linked code is equivalent to monolithically inlined compilation.

Dynamic Neural Network Compilation

Dynamic neural networks with input-driven control flow (DyNNs) are statically intractable for traditional deep learning compilers. DyCL circumvents this by splitting the DyNN into sub-DNNs, each free of dynamic control, compiling each subgraph, and synthesizing a host dispatcher that models the original control logic and invokes the corresponding sub-DNN (Chen et al., 2023). This approach achieves a 100% compile success rate and empirical speedups up to 20.21×.

Quantum Circuits: Hierarchical and Split Compilation

Split compilation in quantum settings appears both as a means of hierarchical optimization for modular hardware (Jeng et al., 14 Jan 2025), and as a circuit obfuscation or IP-protection tool (Zhang et al., 6 Nov 2025). In the former, the Stratify-Elaborate Quantum Compiler (SEQC) splits large circuits across chiplet modules, parallelizes all per-chiplet compilation, and injects cross-module routing optimizations to maximize fidelity and minimize execution time. In the latter, circuits are divided into multiple partitions, with hidden wiring between segments; the goal is to prevent untrusted compilers from reconstructing the entire circuit.

3. Formalization, Algorithms, and Linking Strategies

Segment Identification and Extraction

• Classical Code: Segmenters traverse ASTs to extract maximal loop nests for independent compilation, subject to constraints on nesting and control flow (Shivam et al., 2019).
• Logic Programs: Modules are formal blocks with existential quantification over hidden names; separate compilation yields intermediate code with unresolved references to imported predicates (Holte et al., 2023).
• Neural Networks: The program control flow graph (CFG) is analyzed to find basic blocks, each mapped to a computational subgraph (sub-DNN); each conditional splits the overall graph into two paths (Chen et al., 2023).
• Quantum Circuits: Circuit blocks are segmented based on ansatz structure—fixed, parameter-invariant subcircuits or parameterized blocks corresponding to variational parameters θ_i (Gokhale et al., 2019). In IP protection, splits produce distinct subcircuits with secret wiring (permutation) between them (Zhang et al., 6 Nov 2025).

Independent Compilation/Optimization

Segments are compiled or optimized per the available strategies, with empirical evaluations or predictive modeling (e.g., random forest classifiers on hardware counters in MCompiler). In DyCL, each sub-DNN is compiled statically using existing DL compilers, while SEQC parallelizes all per-chiplet compilation.

Synthesis and Linking

• Recombination: Classical code segments are linked as object files via standard ABI conventions. Logic program modules are linked by patching call-symbols to code labels and ensuring unique export/import signature resolution. Sub-DNNs are orchestrated by a host dispatcher mirroring the control logic.
• Runtime Invocation: Host modules invoke submodules per a computed state machine or dispatcher (e.g., DyCL's host module uses a transition function over states and tensors).
• Quantum Circuits: Pulse-level segments are concatenated in time; wiring between split subcircuits may be obfuscated by hidden permutations.

Correctness and Observational Equivalence

The link phase is formally proved to yield observable results equivalent to monolithic inlining or gate-level composition (Holte et al., 2023, Chen et al., 2023, Shivam et al., 2019). In quantum setting, correctness is formulated in the unitary operator sense: the recombined circuit acts as the original.

4. Performance, Security, and Metric-Driven Trade-Offs

Performance Trends

• MCompiler: Speedup (geomean) of 1.96× (auto-vectorized) and 2.62× (parallelized); ML-based selection is within 4–8% of profiling-based optima; extensions to energy-optimized linking are practical (Shivam et al., 2019).
• DyCL: Execution time speedup between 1.12× and 20.21× across benchmarks and platforms; negligible correctness loss (numeric error δ = 10^{-10}..10^{-4.72}) (Chen et al., 2023).
• SEQC: 3.27× faster compilation vs. baseline; fidelity improvement up to 36% (average +9.3%); reduction in execution latency and inter-chip SWAPs (Jeng et al., 14 Jan 2025).
• Partial Quantum Pulse Compilation: Strict and flexible partial compilation yield speedups up to 3× in pulse duration, at up to 80× reduction in compile latency relative to full GRAPE (Gokhale et al., 2019).

Security in Split Quantum Compilation

IP-protective split compilation, such as hiding the permutation mapping between circuit segments, is empirically shown to be vulnerable: adversaries can reconstruct the hidden wiring (interconnection map ϕ) with modest numbers of oracle queries by exploiting gate reversibility and entanglement structure (Zhang et al., 6 Nov 2025). Empirical evaluation on RevLib benchmarks reveals that less than 100 queries can suffice, challenging the notion that split compilation alone protects quantum IP.

The practical complexity reduction compared to brute-force (m!) arises from hierarchical matching: local (block) entanglement structure, rather than global exhaustive search, enables efficient recovery of ϕ. Security can be restored by combining split compilation with techniques such as randomized reversible insertions, quantum logic locking, multi-level interlocks, entanglement-maximizing splits, or phase obfuscation.

5. Extensions, Generalizations, and Formal Connections

Split compilation is naturally extensible:

• Metric-Driven Optimization: The segment selection and linking phase can optimize for arbitrary metrics—performance, energy, fidelity, memory—by collecting segment-level measurements or predictors.
• Multi-Language Semantics: The "split" of compilation passes can be reflected semantically: by embedding source and target languages, with explicit boundary constructors and a single reduction semantics, one obtains uniform models for both AOT and JIT compilation, with confluence ensuring correctness and type-preservation (Bowman, 23 Sep 2025).
• Hierarchical and Parallel Architectures: Modular quantum hardware necessitates split compilation strategies for scalability; stratification plus parallel per-module elaboration realizes near-ideal acceleration on large designs (Jeng et al., 14 Jan 2025).
• Modular Soundness: Separate compilation in logic programming guarantees formal soundness: modularly compiled artifacts, when linked, are observationally equivalent to the monolithic executable (Holte et al., 2023).

6. Limitations, Challenges, and Future Directions

Split compilation is not universally optimal:

• Overhead of Segmentation: Excessive fragmentation may increase linking or synthesis overhead, and some optimizations spanning segments are precluded.
• Security Limitations: In IP-protective quantum compilation, naive splits without further obfuscation are vulnerable; proper co-design and injection of additional obfuscation and locking mechanisms are required (Zhang et al., 6 Nov 2025).
• Granularity and Partitioning: The benefits of parallelization or selective optimization are sensitive to the distribution of dependencies; highly entangled or tightly coupled programs/circuits may degrade locality, necessitating fine-grained or entanglement-driven cuts.
• Static Costs: One-time stratification or profile acquisition phases may have O(n^2) complexity (e.g., SEQC), amortized only over repeated runs (Jeng et al., 14 Jan 2025).

Future directions entail hybrid approaches: multi-level or interlocked quantum splits, dynamic segment selection driven by runtime metrics, and semantics-driven formalizations that internalize both static and dynamic compilation within unified models. Cross-domain applications—including secure computation, modular neural network deployment, and verifiable compilation—are likely to leverage split compilation as a key design idiom.