Unified-Bench: Scalable Evaluation Framework

• Unified-Bench is a framework that standardizes and consolidates evaluation across heterogeneous systems and domains.
• It employs a layered methodology with micro, component, and application benchmarks to mirror real-world workflows.
• The framework enhances reproducibility and comparability by unifying datasets, protocols, and metrics, and automates evaluation pipelines for efficient hardware/software co-design.

Unified-Bench refers to a class of benchmarking frameworks and methodologies constructed to provide standardized, reproducible, and scalable evaluation across diverse tasks and domains in modern computational research. The unifying principle is to consolidate the evaluation of heterogeneous systems, algorithms, or models within a coherent benchmark suite—streamlining comparison, accelerating experimentation, and facilitating hardware/software co-design. Recent instantiations of this concept span big data and AI workloads, neural architecture search, image and video generation/editing, graph neural architecture evaluation, federated learning anomaly detection, LLM evaluation, and more.

1. Rationale and General Principles

Unified-bench frameworks emerge from the inadequacy of traditional, application-specific benchmarks in characterizing the diversity and rapid evolution of modern computational workloads. Real-world systems increasingly require cross-domain, cross-paradigm evaluation: for example, benchmarking a new system on both traditional analytics and deep learning workloads, or evaluating LLMs across domains and languages. Challenges addressed by unified-bench approaches include:

• Scalability: Avoiding the unscalable “one-benchmark-per-workload” methodology.
• Comparability: Enabling “apples-to-apples” comparisons across algorithms, architectures, or domains.
• Reproducibility: Enforcing consistent data splits, hyperparameters, and evaluation metrics.
• Extensibility: Supporting efficient integration of new tasks, datasets, or evaluation criteria as research domains evolve.

Benchmarks exemplifying this approach include BigDataBench for big data/AI pipelines (Gao et al., 2018), NAS-Bench-201 and NAS-Bench-Graph for neural and graph neural architecture search (Dong et al., 2020, Qin et al., 2022), ICE-Bench for image creation/editing (Pan et al., 18 Mar 2025), VF-Bench for video fusion (Zhao et al., 26 May 2025), BenchHub for LLMs (Kim et al., 31 May 2025), among others.

2. Benchmark Architecture and Layered Methodology

Unified-bench suites are typically organized in a hierarchical, modular fashion:

• Micro-benchmarks isolate fundamental computational primitives or motifs (e.g., matrix multiplication, sort, graph traversal), enabling detailed analysis of basic computation/data movement patterns. BigDataBench, for example, introduces eight “data motifs” capturing the core computation types underlying big data and AI workloads.
• Component benchmarks compose multiple micro-benchmarks into directed acyclic graph (DAG) pipelines, mirroring realistic fragments of end-to-end workflows.
• Application benchmarks combine component benchmarks to emulate entire real-world applications (e.g., a full search engine pipeline)—supporting both broad representativeness and scalability.

This architecture ensures benchmarks can both diagnose low-level system/algorithmic inefficiencies and assess holistic, real-world performance under a unified methodology (Gao et al., 2018).

3. Standardization of Data, Tasks, and Metrics

Unified-bench design emphasizes harmonization of:

• Datasets: Consolidation and provision of diverse, representative, real, and synthetic datasets. For example, BenchHub aggregates over 300K questions from 38 LLM benchmarks, while ICE-Bench curates hybrid data sources for image tasks (Kim et al., 31 May 2025, Pan et al., 18 Mar 2025).
• Evaluation protocols: Harmonized splits (e.g., training/validation/test), consistent training pipelines, and comprehensive metric suites. For instance, NAS-Bench-201 pre-defines splits for CIFAR-10, CIFAR-100, and ImageNet-16-120, and outputs fine-grained metric logs for each candidate architecture (Dong et al., 2020).
• Metrics: Multi-dimensional evaluation capturing various aspects (e.g., aesthetic, fidelity, prompt following, source/reference consistency, spatial and temporal coherence), often via novel metrics such as VLLM-QA in ICE-Bench or BiSWE/MS2R for temporal video quality in VF-Bench (Pan et al., 18 Mar 2025, Zhao et al., 26 May 2025).

LaTeX is frequently employed to specify metric formulas and scoring procedures, such as $$IPC = \frac{\text{Number of retired instructions}}{\text{Total cycles}}$$ for processor efficiency or $S_{PF} = (\text{CLIP}_{cap} + f_{QWEN}(\cdot))/2$ for prompt-following in image editing.

4. Efficiency, Lookup, and Automation

Several unified benchmarks sidestep the computational cost of repeated model training or inference by:

• Precomputing model performance for all enumerated architectures (e.g., all 15,625 architectures in NAS-Bench-201 (Dong et al., 2020), 26,206 in NAS-Bench-Graph (Qin et al., 2022)), and packaging results as lookup tables.
• Automating dataset assimilation, categorization, and evaluation pipeline construction (e.g., BenchHub’s LLM-guided rule-based sample reformatting and categorization, full automation of etl-to-benchmark pipeline (Kim et al., 31 May 2025)).
• Emphasizing modular, extensible codebases (e.g., N$^2$ for nearest neighbor matrix completion implements object-oriented and composite design patterns to rapidly instantiate new algorithms and data types (Chin et al., 4 Jun 2025)).

A common result is democratization of algorithm research: enabling fair, scalable, low-cost evaluation even for computation-intensive tasks such as neural architecture or GNN search.

5. Hardware/Software Co-design and Real-world Systems Analysis

Unified benchmarks are designed to enable domain-specific hardware/software co-design and comprehensive system evaluation:

• By abstracting workloads into motifs or fundamental operations, as in BigDataBench, benchmark suites allow hardware designers to analyze computational, memory, and I/O behaviors representative of a broad class of workloads, rather than single applications (Gao et al., 2018).
• CPU pipeline bottleneck breakdowns (retiring, speculation, frontend/backend bound) are computed across unified workloads to inform architectural optimizations.
• For systems research (e.g., GPU unified virtual memory via UVMBench (Gu et al., 2020)), benchmarks specifically quantify the performance impact of novel features (e.g., page faults, data migration, PCIe throughput, and oversubscription/eviction behavior), using a representative suite of memory access patterns and domains.

6. Impact and Insights from Unified-Benchmarks

Adoption of unified-bench approaches has led to significant progress across multiple computational domains:

• They standardize evaluation, exposing the nuanced performance and reliability trade-offs overlooked by ad hoc benchmarking.
• Extensive cross-domain, fine-grained diagnostic logging supports research on algorithm transferability, robustness to heterogeneity, and regularization effects (as demonstrated in federated anomaly detection with FedAD-Bench (Anwar et al., 8 Aug 2024)).
• Unified-benchmarks often stimulate new research directions: for example, the trade-off analysis between prompt-adherence and reference-consistency in image generation (ICE-Bench), discovery of regularizing effects in federated aggregation (FedAD-Bench), and cross-domain ranking variance in LLM evaluation (BenchHub (Kim et al., 31 May 2025)).

7. Open-source Platforms and Future Directions

Unified-bench projects typically underpin their frameworks with open-source codebases and dataset repositories, promoting transparency and reproducibility. Most provide:

• Evaluation code and standardized scripts.
• Full datasets and pre-trained models.
• Interactive interfaces for exploring and customizing benchmarks (e.g., BenchHub’s web front-end).

Future research is expected to drive more sophisticated dataset curation (addressing additional domains and multilinguality), development of new metrics for alignment, controllability, or fairness, and refined automation for benchmark updates as fields evolve.

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Unified-bench frameworks distill and aggregate heterogeneous, rapidly evolving evaluation requirements into extensible, reproducible, and representative benchmarks—enabling objective, efficient progress in computational research, architecture co-design, and system performance understanding across modalities and domains.