Architecture-Independent Characterization

• Architecture-independent characterization is a methodology that abstracts program features to enable uniform evaluation across CPUs, GPUs, FPGAs, and quantum devices.
• It employs standardized metrics such as instruction mix, memory-access patterns, and control-flow complexity to predict performance without relying on hardware-specific details.
• The approach is pivotal for optimizing compilers, enabling cross-platform device scheduling, and ensuring reproducible benchmarks in evolving computational environments.

Architecture-independent characterization refers to the systematic extraction and analysis of program, workload, or system features strictly decoupled from the idiosyncrasies of a specific hardware architecture. This paradigm enables rigorous comparison, prediction, optimization, and co-design across heterogeneous compute platforms—including CPUs, GPUs, FPGAs, ASICs, quantum devices, and future architectures—by focusing on abstract, portable properties such as instruction mixes, memory-access patterns, control-flow complexity, and algorithmic structure. Guarantees and performance models derived in this manner are inherently robust to hardware evolution and are directly transferable to previously unseen or rapidly emerging platforms.

1. Formal Definitions and Core Principles

Architecture-independent characterization is defined by the adoption of metrics, models, and representations that are invariant under changes in microarchitectural features such as cache configurations, pipeline depths, memory hierarchy, vector width, or device scheduler behavior. This includes:

• Program/Workload Features: Metrics defined at the level of intermediate representations (IR), source code, or, for quantum systems, standardized circuit or protocol formats, without recourse to hardware-specific event counters.
• Invariants Across Platforms: All measurements remain valid and interpretable when ported to new devices, including hybrid and evolving architectures.
• Abstraction from Execution Environment: The methodology relies neither on physical resource counts nor on empirical data tied to a particular hardware instance; for example, AIWC (Architecture-Independent Workload Characterization) captures only instruction-level activity, memory usage patterns, and parallelism semantics as exposed in OpenCL kernels, interpreted via an idealized simulator (Johnston et al., 2018).
• Generalization Theorems: Certain theoretical results, such as those bounding the generalization error of deep overparameterized networks, are proven strictly in terms of data geometry and functional regularity, eschewing classical VC-dimension or parameter-count dependencies for bounds independent of network width or depth (Chen et al., 8 Apr 2025).

2. Architectural Independence in Workload and Program Analysis

2.1 AIWC: Metrics and Methodology

AIWC formalizes a set of metrics to characterize OpenCL workloads for CPUs, GPUs, and MICs, independent of underlying processor details. Metrics include instruction count, opcode diversity, memory footprint, branching entropy, and parallelism descriptors such as work-item count and synchronization events. For memory, address entropy

$H_G = -\sum_a p(a) \log_2 p(a)$

and local memory address entropy, after omitting $k$ LSBs, quantify spatial locality without dependence on cache-line sizes (Johnston et al., 2018). Control metrics such as branch entropy capture predictability of execution paths as a hardware-agnostic indicator of SIMD divergence.

2.2 Parallel Spatial Locality and Pattern Analysis

The parallel spatial locality (PSL) metric, as implemented in the AIWC framework, measures the entropy of concurrent memory accesses by parallel work-items, parameterized by granularity (number of dropped LSBs). A falling PSL$_n$ curve as $n$ increases indicates synchronous access to contiguous memory regions—an architecture-independent hallmark of efficient memory coalescing. Empirical results confirm correlation between PSL curves and kernel performance across platforms (Chilukuri et al., 2020).

2.3 Control-Flow, Locality, and Composite Metrics

Advanced methodologies include instrumentation for control entropy, locality of reference modeled by distributional parameters (e.g., randomizing loop indices under user-specified distributions), and distinctive combinations into total workload vectors, e.g.: $$W = [\,C_{comp}(G), \|C_{comm}(G)\|_1, H(P), \rho_{reuse}\,]$$ where $C_{comp}(G)$ is total operation count, $C_{comm}(G)$ is the node-wise communication vector, $H(P)$ is branching entropy, and $\rho_{reuse}$ is average reuse distance. Each is computed from a canonical representation (e.g., LLVM IR, platform-neutral DAG models) and incorporates stochastic models for workload generation (Dhanasekar et al., 2018).

3. Prediction, Classification, and Optimization Using Architecture-Independent Features

Architecture-independent characterization frameworks enable both cross-platform performance prediction and algorithmic optimization selection. Notably, AIWC features, when regressed via random-forest models, enable execution time predictions with average error $\approx1.2\%$ across 15 device types, without any hardware-specific instrumentation (Johnston et al., 2018). Similarly, the PSL curve and local memory entropy partition workloads into equivalence classes corresponding to computational dwarfs (e.g., GEM-like, stencil, pointer-chasing), facilitating automated optimization guidance and device scheduling (Chilukuri et al., 2020).

For near-memory computing and advanced hardware, metrics such as memory entropy, spatial locality (SLQ), and levels of instruction- or task-level parallelism (DLP, BBLP, PBBLP) are used to assess suitability for embedded compute-in-memory architectures, independent of CPU cache or vector unit configuration (Corda et al., 2019).

4. Architecture-Independent Theoretical Guarantees in Machine Learning

Recent theoretical work has produced generalization error bounds for deep networks that are strictly architecture-independent. For highly overparameterized deep ReLU nets, the test-train error gap can be bounded in terms of data geometry (diameters, Chamfer distance), activation regularity, and spectral norms: $k$0 with $k$1 dependent only on activation properties and operator norms, not on depth $k$2, layer widths, or total parameter count. These results hold deterministically for any train/test split, and the bounds remain nontrivial even for $k$3, in sharp contrast to classical VC-based theory (Chen et al., 8 Apr 2025). Complementarily, architecture-independent generalization gap predictors can be trained to estimate the generalization error of unseen neural architectures using margin statistics aggregated over variable-length depth networks, supporting device-agnostic machine learning model selection (Yak et al., 2019).

5. Compiler and System Design: Pattern-Based Characterization

Compiler retargetability benefits from architecture-independent characterization at the level of IR pattern extraction. RTL patterns, which abstract away from device-specific syntax in GCC’s machine descriptions, capture semantic operation structure. Quantitative analysis reveals that 28–70% of RTL expressions can be generated by pattern sets shared across ISAs, particularly among RISC architectures (P et al., 2013). Pattern-centric workflows thus enable partial automation of code generator construction and cross-ISA knowledge transfer, reducing manual porting effort and fostering generality in compiler backends.

6. Platform-Agnostic Characterization in Quantum Computing and Type-Independent Quantum Resource Theory

Quantum benchmarking architectures, exemplified by platform-agnostic stacks such as QED-C, define interoperable interfaces for problem generation, circuit execution, and results analysis. All components are parameterized over abstract data structures: $k$4 and admit modular plug-ins for circuit generation (e.g., Qiskit, CUDA-Q), execution on varied hardware, and analysis routines (fidelity, depth, execution time), thus ensuring portability and extensibility across quantum computing modalities. This decomposition enables the benchmarking suite to remain robust to ongoing quantum hardware and API proliferation, eliminating fragmentation and platform lock-in (Patel et al., 9 Oct 2025).

In quantum information theory, architecture- or type-independent resource characterizations are formalized by representing objects (states, boxes, assemblages, channels) as elements in an ambient convex cone, with nonclassicality witnessed and quantified via duality, robustness monotones, and SDP hierarchies, abstracted fully from any hardware constraints (Rosset et al., 2019). Such frameworks provide a uniform language for detection, comparison, and optimization of nonclassical resources irrespective of architectural specifics.

7. Applications, Validation, and Impact

Architecture-independent characterization is now fundamental in workload generation for co-design automation at exascale, performance prediction and scheduling on heterogeneous supercomputing platforms, cross-architecture compiler creation, and universal benchmarking for quantum devices. Experimental validations consistently show that architecture-independent methods (e.g., AIWC, RTL pattern re-use, type-independent monotones) yield tight correlation with empirical performance and device suitability, even as hardware landscapes evolve rapidly (Johnston et al., 2018, Chilukuri et al., 2020, Johnston et al., 2018, Patel et al., 9 Oct 2025, P et al., 2013, Rosset et al., 2019).

A plausible implication is that further integration of architecture-independent metrics into system design flows—spanning from workload synthesis and hardware simulation to dynamic resource management and end-to-end model selection in ML—will further reduce time-to-solution and increase the performance portability and reproducibility demanded in future scientific and engineering computing environments.