KernelBench Suite: Evaluating Kernel Performance

Updated 1 January 2026

KernelBench Suite is a robust benchmarking framework evaluating correctness, efficiency, and portability of various kernel implementations across multiple hardware platforms.
It supports diverse applications including ML-synthesized code, GPU autotuning, and domain-specific kernels such as LBM and sparse tensor primitives.
Its modular design enables automated evaluation, reproducibility, and cross-platform extension through standardized APIs and comprehensive metrics.

KernelBench Suite is a term encompassing several benchmark infrastructures for evaluating kernel performance, optimization, and—more recently—LLM–driven kernel synthesis, with explicit technical roots in GPU, CPU, and distributed computing research. The modern KernelBench Suite typifies a rigorous, multifaceted approach to measuring the correctness, efficiency, and portability of kernels ranging from hand-tuned numerics, sparse tensor primitives, and Lattice Boltzmann operators to ML-generated device code and autotunable GPU workloads. The concept underlies both targeted domain suites (e.g., LBM, UVM, sparse tensors, distributed algorithms) and general frameworks for kernel synthesis, performance tuning, and cross-platform benchmarking.

1. Suite Scope and Architectural Design

The KernelBench Suite comprises a heterogeneous collection of targeted benchmarks and full-stack evaluation frameworks. Its incarnations span the following domains:

Empirical GPU kernel generation and evaluation (e.g., KernelBench (Ouyang et al., 14 Feb 2025), MultiKernelBench (Wen et al., 20 Jul 2025)), which couple automated correctness and hardware-accelerated profiling to systematically assess LLM–synthesized code.
Tunable GPU kernel benchmarks for autotuning (KernelBench/BAT 2.0), evaluating optimizer convergence, search space ruggedness, parameter feature influence, and performance portability (Tørring et al., 2023).
Domain-specific suites: classical distributed algorithm kernels (IMSuite (Gupta et al., 2013)), Lattice Boltzmann benchmark kernels for CFD (LBM-KernelBench (Wittmann et al., 2017)), unified virtual memory kernel suites (UVMBench (Gu et al., 2020)), and reference suites for high-dimensional sparse tensor primitives (Li et al., 2020).

Architecturally, state-of-the-art KernelBench frameworks prioritize:

Modular, extensible backends (e.g., MultiKernelBench's backend-abstraction class hierarchy for CUDA, AscendC, and Pallas (Wen et al., 20 Jul 2025))
Task-centric evaluation pipelines: each task or workload is a top-level, self-contained module, supporting automated input generation, output validation, and resource control.
Automated correctness and profiling infrastructure: reproducible, tight-tolerance output checks, repeated trial-based measurement, and device-specific resource isolation.

2. Benchmarked Kernels, Domains, and Parameter Spaces

KernelBench instantiations are characterized by comprehensive kernel inventories, parametric configuration, and typological breadth.

ML and Operator Kernels: Modern KernelBench suites (e.g. (Ouyang et al., 14 Feb 2025)) benchmark 250 PyTorch workloads, stratified into single-operator, fused chains, and end-to-end mini-architectures (AlexNet, FlashAttention, etc.), with data types ranging across FP32/FP16/BF16/INT8.
Autotuning Benchmarks: BAT 2.0 covers seven highly parameterized GPU workloads (GEMM, N-body, Hotspot, Pnpoly, 2D-Conv, Expdist, Dedispersion), with configuration space sizes from 1k up to 21M (Tørring et al., 2023).
Platform Diversity: MultiKernelBench encompasses 14 DL kernel categories (Activation, Convolution, Fusion, Normalization, Indexing, etc.) with 285 tasks, distributed across NVIDIA CUDA, Huawei AscendC, and Google TPU/Pallas platforms (Wen et al., 20 Jul 2025).
Backward Compatibility: Sparse tensor suites and LBM kernels retain complete coverage of historical HPC and ML primitives, supporting both baseline and research-exotic data layouts, addressing schemes, and blocking/padding strategies (Li et al., 2020, Wittmann et al., 2017).

Suites expose their kernels and workloads via programmable APIs to facilitate both manual and automated sweeps of parameters.

Suite/Domain	Kernel Count/Types	Parameterization Level
KernelBench (LLMs)	250 ML tasks, various operator chains	Low–Medium
BAT 2.0	7 GPU kernels, tunable (82k–21M configs)	Very High
MultiKernelBench	285 DL tasks, 14 categories, 3 platforms	Medium–High
LBM-KernelBench	15 LBM kernels, SoA/AoS/list, SIMD	Low–Medium
Sparse Tensor	5 ops × 30 tensors × 2 formats	Low–Medium

3. Evaluation Protocols, Metrics, and Methodologies

KernelBench frameworks incorporate standardized, reproducible metrics for correctness, performance, and portability, with explicit ties to contemporary research drivers.

Correctness Validation: Automated output comparison against references (usually tight numerical tolerance; five random input instances (Ouyang et al., 14 Feb 2025, Wen et al., 20 Jul 2025)).
Performance Measurement: Wall-clock evaluation via device timers (e.g., torch.cuda.Event); repeated trials after warmup to dampen scheduling variability (Ouyang et al., 14 Feb 2025).
fastₚ Metric (KernelBench): For $N$ tasks, $fast_p = (1/N) \sum_{i=1}^N \mathbf{1}[\mathrm{correct}_i \wedge \mathrm{speedup}_i > p]$ ; allows fine-grained tracing of LLMs' ability to simultaneously produce correct and faster-than-baseline code (Ouyang et al., 14 Feb 2025).
MultiMetric Evaluation (MultiKernelBench): Compilation@k (success rate), Pass@k (numerical correctness), SpeedUp $_\alpha$ @k (fraction of kernels at least $\alpha\times$ faster) (Wen et al., 20 Jul 2025).
Tuning/Portability Metrics (BAT 2.0): Convergence curves, local minima proportional centrality, maximum speedup over median, parameter feature importance (PFI), and performance portability for optimal versus transferred configurations (Tørring et al., 2023).
Memory and Bandwidth Rooflines: Classical suite implementations (LBM, Sparse Tensor) are annotated with analytic and empirical loop balance, bandwidth ceilings, and bottleneck analysis (Wittmann et al., 2017, Li et al., 2020).
Test-Time and Prompting Regimes: One-shot, repeated sampling, iterative refinement (feedback-driven), few-shot, hardware-aware prompting evaluated for LLM-synthesized kernels (Ouyang et al., 14 Feb 2025, Wen et al., 20 Jul 2025).

4. Supported Platforms and Backends

KernelBench frameworks demonstrate broad device and platform coverage, with explicit mechanisms for backend extension.

CUDA GPUs: Nearly all modern KernelBench deployments support NVIDIA CUDA devices, with PyTorch cpp_extension integration (MultiKernelBench), batched compilation, and resource pinning (Ouyang et al., 14 Feb 2025, Wen et al., 20 Jul 2025).
Ascend NPUs, TPU XLA/Pallas: MultiKernelBench implements abstracted backend subclasses for AscendC (glue-compiled into PyTorch), Google TPUs via Pallas/JAX/XLA, with device selection at runtime through decorator-based registration (Wen et al., 20 Jul 2025).
OpenCL/CLTune/KTT: GPU tuner suites interface with CLBlast, KTT, and alternative code-generation APIs via a common Problem interface (Tørring et al., 2023).
x86/AVX2/AVX-512, AMD, ARM: Domain-specific suites (LBM, Sparse Tensor) instrument all major server architectures, supporting runtime toggle of SIMD, NUMA/core/threading parameters (Wittmann et al., 2017, Li et al., 2020).

The modular plugin abstraction enables rapid integration of new or proprietary backends (e.g., ROCm/HIP, Triton, ThunderKittens), contingent on implementation of a fixed API (initialize_device, compile_and_load, run_and_verify, cleanup) (Wen et al., 20 Jul 2025).

5. Experimental Findings and Comparative Analyses

Recent empirical studies on KernelBench suites elucidate both the potential and the current limitations of kernel optimization and synthesis.

LLM-Generated Kernels: State-of-the-art LLMs achieve fast $_1$ (correct and faster) rates of 3–36% out-of-the-box on PyTorch/CUDA; iterative refinement with profiler feedback can raise this to 72% on some operator chains (Ouyang et al., 14 Feb 2025). Category-aware prompting (MultiKernelBench) can produce >100% relative boosts on challenging platforms and categories (Wen et al., 20 Jul 2025).
Task Difficulty: Kernel categories vary widely in learnability/generatability; activation/reduction/broadcast kernels exceed 85% Pass@1 in greedy decoding, whereas convolution or full-architecture tasks are unsolved by current LLMs (Wen et al., 20 Jul 2025).
Portability and Generalization: Top-tuned configs from one GPU architecture transfer with widely varying success (58–99% of optimal), underscoring the necessity of re-tuning for new hardware (Tørring et al., 2023).
Parameter Influence: Certain tunable kernel features (block sizes, tiling, shared memory use) are robustly high-importance, but optimal values differ per architecture (Tørring et al., 2023).
UVM Overheads, Data Reuse, and Prefetch: Automatic UVM incurs major overheads (up to 96% slowdown) unless mitigated with repeated invocation (data reuse) or explicit cudaMemPrefetchAsync (bringing slowdown to below 1%) (Gu et al., 2020).
Classical Microarchitectural Bottlenecks: LBM and sparse tensor benchmarks display textbook memory bandwidth saturation and well-predicted loop balance effects, with kernel optimizations (non-temporal stores, two-step AA patterns, reduced indirect addressing, partial vectorization) approaching or achieving measured hardware rooflines (Wittmann et al., 2017, Li et al., 2020).

6. Extensibility, Practical Guidance, and Future Directions

KernelBench suites are explicitly extensible, with best practices codified to drive future research:

Backend Integration: Implement the required interface methods and register the platform (Wen et al., 20 Jul 2025).
New Kernel Tasks: Add tasks by dropping reference definitions and input/validation routines into suite benchmarks directories (Ouyang et al., 14 Feb 2025).
Portable Benchmarking: Test across multiple devices, report portability matrices, prune parameter spaces using feature importance, and compare both random/global and local search algorithms (Tørring et al., 2023).
Metrics Reporting: Report all core metrics—fast $_p$ , Pass@k/Comp@k/SpeedUp $_\alpha$ @k, tuning convergence curves, centrality, speedup, and feature importance.
Reproducibility: Use provided Dockerfiles, track baseline timings, and maintain validation harnesses and integration tests (Ouyang et al., 14 Feb 2025).

Emergent directions include supporting partial hardware synchrony/failure, dynamic graph and adaptive input spaces, further hardware heterogeneity, and the development of cache-polluting workloads for memory-system stress testing (Gupta et al., 2013, Wittmann et al., 2017, Wen et al., 20 Jul 2025). A plausible implication is continued expansion toward comprehensive, platform-independent standards for ML/AI kernel synthesis and optimization.