Hardware-Agnostic Performance Model

• Hardware-agnostic performance models are methodologies for predicting, analyzing, and optimizing computational workloads by abstracting hardware-specific details.
• They employ techniques such as symbolic operation counting, unified intermediate representations, machine learning, and systematic benchmarking for accurate parameter fitting.
• These models drive applications in auto-tuning, performance guidance, runtime scheduling, and simplify deployment in heterogeneous computing environments.

A hardware-agnostic performance model is a class of methodologies and frameworks for predicting, analyzing, or optimizing the performance of computational workloads in a manner that is not tied to the quirks or specifics of any one hardware platform. These models abstract or systematically parameterize hardware-dependent details, enabling their application across diverse devices—ranging from CPUs, GPUs, FPGAs, and specialized accelerators to quantum co-processors. The goals of such models include portability, broad applicability, facilitation of auto-tuning, performance portability, and reduction in the cost of supporting multiple hardware environments.

1. Model Principles and Methodologies

Hardware-agnostic performance models are typically structured around the decoupling of software workload characteristics from hardware-specific behaviors. Key methodological patterns include:

• Symbolic Operation Counting: The execution time or another performance metric is modeled as a linear combination of symbolic cost components (e.g., number of arithmetic operations, memory accesses, synchronizations), expressed as quasi-polynomials or similar formulas parameterized by problem size or code geometry. Once these are symbolically extracted via code analysis, machine-specific weights (seconds per operation) are empirically fitted for each hardware platform through targeted benchmarking (Stevens et al., 2016).

$$T_{\text{wall}}(n) \approx \sum_{i=1}^N \alpha_i \cdot p_i(n)$$

• Intermediate Representations (IRs): Some frameworks introduce unified IRs that are independent of hardware specifics, enabling both software kernels and hardware instructions to be cast in a common form. Matching algorithms or compilers then map software computations to hardware instructions by discovering computational isomorphisms, often framed as subgraph isomorphism or matrix matching problems (Sotoudeh et al., 2018).
• Machine Learning Approaches: Statistical or learning-based models leverage datasets of workload/hardware measurements, embedding program traces and hardware configurations into high-dimensional spaces. Performance is then predicted by combining these representations (e.g., via dot products) in a way that allows reusability and generalization across unseen programs and hardware (Li et al., 2023). In quantum emulation, noise parameters are inferred from protocol-level calibration data using neural networks (Ho et al., 27 Feb 2025).
• Systematic Benchmarking and Calibration: Strategic use of performance-instructive kernels or Performance Representatives (PRs) enables accurate model fitting or statistical estimation with substantially fewer samples. PRs correspond to key configurations that capture the “stepwise” changes in hardware performance response, particularly in accelerators with highly regular architectures (Jung et al., 12 Jun 2024).
• Instrumentation and Abstraction Layers: Some frameworks establish hardware-agnostic evaluation environments by standardizing interface layers (e.g., OpenCL, HALO’s C²MPI abstraction for heterogeneous platforms (Riera et al., 2020)) or by using standardized benchmarking protocols (e.g., vendor-agnostic test harnesses for robotics (Mayoral-Vilches et al., 2023) and theorem provers (Huch et al., 2022)).

2. Model Construction, Fitting, and Extraction

The construction and calibration of hardware-agnostic models typically involve two main phases:

• Hardware-Independent Extraction: The first phase automates the symbolic extraction of performance-relevant operation counts or builds IR-based representations of computational workloads. Techniques include polyhedral code analysis (for symbolic operation counting (Stevens et al., 2016)), automatic differentiation in rendering workflows (Takimoto et al., 2022), or instruction-level embedding extraction in ML-based models (Li et al., 2023).
• Hardware-Specific Parameter Fitting / Mapping: In the second phase, the architecture-dependent aspects are measured and fitted. This may involve:
  • Running calibration kernels or PRs and fitting linear (or more complex) models to empirical execution times (Stevens et al., 2016, Jung et al., 12 Jun 2024).
  • Gathering measurements from representative workloads and using regression, symbolic regression, or machine learning to map observed behaviors to model parameters (Chennupati et al., 2020, Ho et al., 27 Feb 2025).
  • Building lookup tables indexed by hardware DVFS states and sub-hardware domains, where each entry encodes the model (often linear) for power or performance (Mazzola et al., 3 Jan 2024).
  • Abstracting hardware configuration into embeddings that are learnable and combinable with workload embeddings for performance prediction (Li et al., 2023).

3. Applications and Real-World Impact

Hardware-agnostic performance models have been applied across numerous domains:

• Auto-Tuning and Algorithm Selection: Their symbolic or statistical prediction capability allows rapid evaluation of multiple program variants or transformation options during autotuning, with particular benefit for numerical kernels on accelerators (Stevens et al., 2016, Sotoudeh et al., 2018).
• Performance Guidance and Bottleneck Analysis: Analytical decomposition of execution time or energy aids in identifying the dominant costs of algorithms, enabling informed optimization, resource allocation, and system design choices (Stevens et al., 2016, Mazzola et al., 3 Jan 2024).
• Runtime Scheduling and Load Balancing: Accurate cross-device predictions inform job scheduling and resource partitioning in distributed, heterogeneous, or exascale environments (Riera et al., 2020, Demir et al., 2022).
• Deployment Simplification and Portability: Multi-hardware models—especially in the context of deep learning—enable a single architecture to be deployed across diverse accelerators, reducing engineering overhead and ensuring output consistency (Chu et al., 2020, Perkins, 2016).
• Device Benchmarking and Standardization: Models and associated benchmarking protocols (such as the Atos Q-score for quantum processors (Martiel et al., 2021) or ROS 2-based RobotPerf (Mayoral-Vilches et al., 2023)) provide a basis for rigorous, comparable evaluation across platforms.
• Program Analysis, Design Space Exploration, and Continuous Training: ML-based representations, foundation models, and scalable partitioned tools (via DASK, PyTorch Lightning, etc.) support large-scale knowledge graph embedding (Demir et al., 2022), and generalized program optimization workflows (Li et al., 2023).

4. Model Validation, Evaluation, and Accuracy

Empirical results consistently show that well-constructed hardware-agnostic models can achieve accuracy comparable to (and often competitive with) hardware-specific models:

• Prediction Accuracy: Many approaches report mean absolute percentage errors (MAPE) in the range of 0.02%–16% for kernel run times, power, or energy estimation (Stevens et al., 2016, Mazzola et al., 3 Jan 2024, Jung et al., 12 Jun 2024).
• Portability Score: In multi-accelerator orchestration frameworks such as HALO, the “performance portability score” reaches 1.0, indicating that hardware-agnostic execution can match baseline hardware-tuned executions with minimal overhead (Riera et al., 2020).
• Efficiency of Training: Smart selection of Performance Representatives leads to drastic reductions in the number of required measurements while increasing sample efficiency compared to random sampling (Jung et al., 12 Jun 2024).
• Generalization: ML-based foundation models report low error rates even on unseen programs or hardware after minimal fine-tuning, enabled by independent program and microarchitecture representations (Li et al., 2023).
• In-Kernel Real-Time Estimation: Data-driven power models can be natively integrated into OS kernels, supporting online estimations for dynamic power management with overhead as low as 0.7% (Mazzola et al., 3 Jan 2024).

5. Architectural Abstraction and Algorithmic Innovations

Abstraction from hardware details is achieved via a variety of explicit and implicit strategies:

• API/Language Choices: Adoption of cross-vendor APIs (OpenCL, Vulkan, Alpaka) facilitates portability at both the programming and kernel execution level (Perkins, 2016, Takimoto et al., 2022, Vourliotis et al., 25 Jul 2024).
• Hierarchical, Modular Design: Models may be built at different levels—basic block, function, or dataflow graph—which can be composed and extended for broader scalability (Chennupati et al., 2020, Li et al., 2023).
• Parallelism and Portability-Agnostic Pattern Recognition: Algorithms are (re)architected for concurrent, data-local object construction to leverage device parallelism and facilitate effortless backend mapping (e.g., Line Segment Tracking for LHC HLT (Vourliotis et al., 25 Jul 2024)).
• Device-Agnostic Benchmarking Methodologies: Frameworks employ black-box and grey-box instrumentation to provide vendor-agnostic, yet fine-grained, performance measurement without modifying user code (Mayoral-Vilches et al., 2023).

6. Limitations, Challenges, and Prospects

While hardware-agnostic performance models have enabled considerable gains in efficiency and portability, several limitations persist:

• Omissions of Microarchitectural Details: Many models—by design—neglect fine-grained effects such as occupancy, cache and register interactions, or latency hiding through concurrency (Stevens et al., 2016). This abstraction may degrade accuracy for highly optimized workloads.
• Data and Implementation Overheads: Some ML-based models require substantial upfront effort for data generation and training, though advances in sample efficiency (e.g., use of PRs) are addressing this challenge (Jung et al., 12 Jun 2024, Li et al., 2023).
• Heuristic Tuning and Extensions: Extension to new domains (e.g., parallel workloads (Li et al., 2023), photorealistic rendering (Takimoto et al., 2022), device-level quantum noise estimation (Ho et al., 27 Feb 2025)) often necessitates domain adaptation and novel heuristics.
• Scalability to Heterogeneous and Evolving Hardware: While foundation models and abstraction frameworks improve portability, the increasing heterogeneity of modern hardware platforms continues to demand adaptive, modular modeling paradigms (Riera et al., 2020, Demir et al., 2022).
• Standardization and Community Evolution: Open-source and community-driven benchmarks are essential for sustaining interoperability and advancing methodology coverage, as emphasized in robotics and theorem proving (Mayoral-Vilches et al., 2023, Huch et al., 2022).

7. Representative Models and Protocol Examples

The landscape of hardware-agnostic performance models encompasses a broad array of practical frameworks, summarized in the table below:

Domain	Approach / Framework	Core Principle	Paper id
GPU kernels	Symbolic counting + linear model fitting	Code-structure driven	(Stevens et al., 2016)
Deep learning	Hardware-agnostic IR and isomorphism mapping	Representation equivalence	(Sotoudeh et al., 2018)
DNN accelerators	Performance Representatives (PR) sampling	Stepwise behavioral modeling	(Jung et al., 12 Jun 2024)
Power modeling	PMC-based linear regression, in-kernel eval	Data-driven per-DVFS models	(Mazzola et al., 3 Jan 2024)
Robotics	Modular ROS 2 black/grey box benchmarks	Vendor-agnostic instrumentation	(Mayoral-Vilches et al., 2023)
Quantum	GST+ML gate-based emulator	Model-free noise inference	(Ho et al., 27 Feb 2025)

Hardware-agnostic performance models are foundational to the continued evolution of performance engineering, enabling optimization, evaluation, and deployment workflows that transcend single-device boundaries and facilitate rapid adaptation as hardware landscapes diversify.