Hardware-Agnostic Performance Models

• Hardware-agnostic performance models are frameworks that predict workload performance without relying on hardware-specific details.
• They employ methods like symbolic extraction, linear combination, and statistical regression to relate workload properties to performance metrics.
• These models enable fair benchmarking, transparent tuning, and rapid deployment across heterogeneous and evolving computing environments.

A hardware-agnostic performance model is a mathematical or computational framework that predicts, analyzes, or benchmarks the performance of workloads without being tied to the internal specifics, proprietary features, or vendor-dependent idiosyncrasies of any particular hardware platform. The purpose is to enable portability of predictions or insights across disparate devices—CPUs, GPUs, FPGAs, NPUs, or even emerging architectures—by abstracting the relevant program, memory, and computational behaviors away from hardware-specific details. Such models form the scientific and engineering foundation for portable tuning, algorithmic co-design, portable benchmarking, and system-scale optimization in highly heterogeneous computing contexts.

1. Fundamental Principles and Motivations

Hardware-agnostic performance modeling replaces direct hardware characterization (e.g., by cycle-accurate emulation or vendor-internal microarchitectural timing) with abstractions that are independent of instructions or platforms. The underlying strategy is to describe the application's resource consumption or computational structure using symbolic, statistical, or algebraic representations, and then to relate these to performance by combining them with generic or fitted parameters. The primary motivations are:

• Portability: Predicting and optimizing workload performance across varied architectures (e.g., CPU, GPU, FPGA) or vendors (e.g., AMD, NVIDIA, Intel) using a common model.
• Transparency and Reproducibility: Avoiding reliance on vendor-proprietary internals.
• Separation of Concerns: Decoupling the specification/optimization of algorithms from hardware deployment.
• Scalability: Enabling rapid model deployment and forecasting in emerging or rapidly evolving hardware where native tools may lag, as with RISC-V (Batashev, 30 Jul 2025).
• Fair Benchmarking: Establishing standardized, technology-agnostic protocols for comparison (&&&1&&&).

2. Formalisms and Modeling Methodologies

The construction of hardware-agnostic models typically involves three canonical steps:

a. Extraction of Workload Properties

Most approaches begin by extracting symbolic or statistical representations of the fundamental kernel operations required by the workload. For accelerated kernels or subprograms, operation counts and properties are gathered automatically via polyhedral frameworks (e.g., Loopy IR (Stevens et al., 2016)) or intermediate representations (LLVM IR (Chennupati et al., 2020), ISAMIR (Sotoudeh et al., 2018)). Properties may include:

• Operation type (arithmetic, memory load/store, synchronization).
• Operation frequency or count as a symbolic function of problem size, loop bounds, or domain parameters.
• Data access patterns and reuse distances, via explicit memory traces or instrumented counters.

For instance, in the unified cross-GPU performance model: $$T_{\text{wall}}(\mathbf{n}) \approx \sum_{i=1}^N \alpha_i \cdot p_i(\mathbf{n})$$ where $p_i(\mathbf{n})$ represents the symbolic operation count for feature $i$ as a function of parameters $\mathbf{n}$, and $\alpha_i$ is a hardware-dependent scaling factor (Stevens et al., 2016).

b. Model Construction

Once kernel properties are obtained, they are embedded in a predictive model. Common forms include:

• Linear combination models: Execution time or energy is a sum over kernel properties, weighted by device-fitted parameters (e.g., the $\alpha_i$ above).
• Reuse distance models: Cache or memory performance is predicted from global or per-basic-block reuse histograms $P^C(d, |I|)$, with architecture-dependent miss-rate integration (Chennupati et al., 2020).
• Analytical operator-level models: For LLM inference, operator-level analytical estimates of compute and memory time, using parameters like TOPS (Tera Operations Per Second) and memory bandwidth, allow for predictions such as

$\text{TTFT} = \max(t_{tc}, t_{tm})$

with $$t_{tc} = (\sum_\text{op} \text{ops}_\text{op} \cdot e_{c,\text{op}}) / \text{TOPS} + t_\text{dispatch}$$, and $$t_{tm} = (\text{Mem}_\text{op} \cdot e_{m,\text{op}})/(\text{BW}) + t_\text{dispatch}$$ (Patwari et al., 29 Jul 2025).

• Data-driven statistical or ML models: Machine learning regression (including deep neural networks) map hardware- or software-extracted features to performance metrics (e.g., R²=0.96–0.98 for CNN predictors of SPEC 2017 scores (Cengiz et al., 2023)).

c. Parameterization and Fitting

Hardware-agnostic model structure is often fit to each device using a standard protocol:

• For each device or architecture, a set of “measurement kernels” or benchmarks is executed.
• The actual runtimes, power, or other performance measures are recorded.
• Least-squares, nonnegative least squares, or ML regression is used to fit per-device coefficients, ensuring physical meaning (e.g., nonnegative operation times or PMC weights).

3. Representative Applications Across Domains

Hardware-agnostic performance models have been developed and validated across a breadth of computational domains:

Domain	Model Approach/Abstraction	Reference
GPU kernel time modeling	Polyhedral IR + linear feature model	(Stevens et al., 2016)
Deep learning compiler optimization	Operator-centric IR/graph mapping and scheduling	(Sotoudeh et al., 2018)
LLM inference forecasting	Modular per-operator analytical model, software config-aware	(Patwari et al., 29 Jul 2025)
Power/energy prediction in heterogeneous SoCs	PMC-driven per-subsystem linear regression models	(Mazzola et al., 3 Jan 2024)
Code performance prediction in scientific apps	LLVM IR, memory reuse, pipeline simulation, ML-driven extrapolation	(Chennupati et al., 2020)
Knowledge graph embedding training	High-level API abstraction and model/data parallelism	(Demir et al., 2022)
Quantum co-processor benchmarking	Application-centric Q-AOA MaxCut benchmarks, analytical scaling	(Martiel et al., 2021)
Robotics/embedded pipeline benchmarking	Vendor-agnostic ROS2-based graph, standard metrics	(Mayoral-Vilches et al., 2023)
RRAM characterization	Sequential, protocol-based, technology-agnostic electrical assessment	(Stathopoulos et al., 2018)

4. Model Validation: Predictive Accuracy and Portability

Benchmarking and validation data across the literature indicate that hardware-agnostic models, when properly constructed and fitted, often achieve predictive power rivaling or exceeding more hardware-specific approaches:

• Unified cross-GPU models achieve geometric mean relative errors of 6%–16% on diverse Nvidia and AMD devices, competitive with vendor-tuned models (Stevens et al., 2016).
• Machine learning statistical models for code benchmarks yield R² > 0.95, sharply outperforming linear regression, and able to extrapolate to new, unseen hardware (Cengiz et al., 2023).
• Analytical LLM inference models (LIFE) match measured TTFT, TPOT, and TPS metrics on platforms from AMD NPUs and CPUs to NVIDIA V100, validating hardware independence (Patwari et al., 29 Jul 2025).
• For PMU-based power monitoring, per-subsystem linear models achieve a mean absolute percent error (MAPE) of 7.5% for power, and 1.3% for energy, with kernel overheads below 1% (Mazzola et al., 3 Jan 2024).

A plausible implication is that the inherent modularity and interpretability of such models (e.g., additive linear or analytical decomposition) make them robust to hardware evolution and amenable to real-time adaptation, compared to black-box benchmarking.

5. Technological Abstractions and Software Infrastructure

Critical to hardware-agnostic modeling are software and protocol abstractions:

• Intermediate Representations: Loopy IR, LLVM IR, or domain-specific IRs (ISAMIR) uniformly describe code, decoupling it from backend specifics (Chennupati et al., 2020, Sotoudeh et al., 2018, Stevens et al., 2016).
• Abstraction libraries: Frameworks such as Alpaka in the Line Segment Tracking (LST) algorithm for LHC event reconstruction (Vourliotis et al., 25 Jul 2024) or OpenCL for deep learning (Perkins, 2016) enable identical source code execution across CPUs, GPUs, and FPGAs.
• Distributed APIs: HALO 1.0 uses a compute-centric MPI extension (C²MPI) so that the host application code remains portable, while accelerator-specific details are managed by virtualization agents (Riera et al., 2020).
• Instrumentation: Compiler-based passes (LLVM) can inject performance counters and isolate SESE regions for Roofline modeling independent of hardware PMU reliability (Batashev, 30 Jul 2025).
• Runtime monitoring: Integrated kernel modules (e.g., Runmeter) enable real-time model evaluation and are responsive to platform DVFS state changes (Mazzola et al., 3 Jan 2024).
• Standardized benchmarking protocols: Suites such as RobotPerf use ROS2 and black/grey-box measurement modalities to produce vendor-agnostic metrics across robot pipelines (Mayoral-Vilches et al., 2023).

6. Limitations, Open Problems, and Implications

Despite their utility, hardware-agnostic models must address certain intrinsic limitations:

• Computational Overhead vs. Fidelity: While linear models are inexpensive, complex codes (with pipeline interdependencies or intricate cache behavior) may exceed the expressivity of simple models, necessitating ML-driven or hybrid approaches (Chennupati et al., 2020, Cengiz et al., 2023).
• Feature Selection/Aggregation: The process of extracting robust, minimally redundant features from large operation or PMC sets is nontrivial (e.g., via correlation analysis and non-negative least squares (Mazzola et al., 3 Jan 2024)).
• Adaption to Architectural Change: Future devices with radically different compute/memory hierarchies (e.g., emerging RISC-V microarchitectures (Batashev, 30 Jul 2025)) may still require augmentation or refitting.
• Parameter Fitting and Benchmark Selection: Hardware-agnostic models inevitably depend on having sufficient measurements to fit per-platform scaling (or efficiency) factors; inadequate selection of training kernels or benchmarks will yield poor parameterization (Stevens et al., 2016).
• Operator-Level Abstractions: Models like LIFE (Patwari et al., 29 Jul 2025) must be kept in sync with the evolving operator set and kernel fusion paradigms of target software stacks.

Nevertheless, the separation between workload characterization (in platform-independent space) and device-dependent parameterization appears to offer a sustainable path to portable performance tuning, rapid benchmarking, efficient deployment, and system design in a heterogeneous and rapidly evolving hardware landscape.

7. Mathematical Formulations and Protocols

The key mathematical constructs for hardware-agnostic performance modeling, as evidenced across the literature, include:

• Linear feature models for runtime:

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

• Reuse distance–based cache models:

$$P^C(d,|I|) = \frac{1}{N(|I|)} \sum_{i=1}^m N_i(|I|) \cdot P_i(d,|I|)$$

$$P(h|d) = \sum_{a=0}^{A-1} \binom{d}{a} \left(\frac{A}{B}\right)^a \left(\frac{B-A}{B}\right)^{d-a}$$

• Roofline arithmetic intensity:

$$AI = \frac{\text{Arithmetic ops}}{\text{Bytes transferred}}$$

and performance:

$$P = \min\left(P_{\text{compute-peak}},\; B_{\text{mem}} \cdot AI\right)$$

• Power modeling:

$$P_{\text{SYS}} = \sum_{d\in D^*} P_d(X_{d,f_d}, W_{d,f_d})$$

where each

$$P_d(X_{d,f_d}, W_{d,f_d}) = L_d + \sum_{i=1}^{\#\text{units}} \sum_{j=1}^{N_d} \frac{1}{T} x_{ij} w_{ij}$$

• Deep learning prediction loss/minimization:

$$\text{MSE} = \frac{1}{N} \sum_{i=1}^N (y'_i - y_i)^2, \quad \text{R}^2 = 1 - \frac{\sum_{i=1}^N (y_i - \hat{y}_i)^2}{\sum_{i=1}^N (y_i - \bar{y})^2}$$

These equations, coupled with sophisticated but platform-agnostic software infrastructure, define the contemporary state of hardware-agnostic performance modeling in computational and scientific computing.