NPBench: HPC & μNPU Benchmark Suite

• NPBench is a dual-purpose benchmark suite that standardizes high-performance scientific computing and ultra-low-power μNPU evaluations using rigorous, reproducible methodologies.
• Its HPC application, such as NPB-Rust, reimplements NAS Parallel Benchmarks in Rust to enhance memory safety, concurrency, and performance comparisons.
• The μNPU benchmarks break down neural inference into distinct stages, measuring latency, energy efficiency, and memory usage to guide hardware-software co-design.

NPBench is a multi-faceted initiative that refers, in recent literature, to two major lines of high-performance benchmarking: (1) scientific computing benchmarks—specifically the porting and evaluation of the NAS Parallel Benchmarks (NPB) in modern programming languages such as Rust; and (2) standardized frameworks for benchmarking ultra-low-power neural processing unit ($\mu$NPU) platforms. Each usage shares a rigorous methodology that emphasizes cross-platform and cross-language evaluation, reproducibility, and a rich suite of performance and efficiency metrics.

1. Origin and Definition

NPBench, in the context of recent research, may denote either the recasting of the NAS Parallel Benchmarks (NPB)—widely employed in evaluating scalable hardware and parallelism strategies—or an independent benchmarking suite for ultra-low-power neural accelerators, particularly $\mu$NPUs. Both usages share the core goal of providing standardized, portable, and authoritative benchmarks for rigorous performance evaluation in hardware/software co-design and scientific computation.

The NAS Parallel Benchmarks were originally devised to measure the performance of parallel computers using kernels that represent typical computational and communication patterns. The $\mu$NPU benchmark methodology referred to as NPBench articulates end-to-end neural network inference assessment on microcontroller-scale dedicated neural hardware.

2. Scientific Benchmarking: NAS Parallel Benchmarks

The first major usage of NPBench centers around reimplementing NPB in emerging languages for HPC, most notably Rust (Martins et al., 21 Feb 2025). This implementation, "NPB-Rust," preserves NPB algorithms and naming from the C++ suite while refactoring to idiomatic Rust to leverage its memory safety and concurrency model. Key features include:

• Replacement of mutable global variables (from C++) with main-scoped variables and explicit mutable reference passing in Rust.
• Substitution of preprocessor macros and statically defined constants with Rust's pub const declarations.
• Transformation of for-loops into iterators, with retention of for-loop patterns where index arithmetic (e.g., in the MG kernel) precludes safe iterator-based refactoring.
• Introduction of controlled unsafe blocks for performance-critical multi-dimensional index manipulations, ensuring thread-private regions to maintain safety.
• Parallelization via the Rayon library, which uses work-stealing and exposes Map and MapReduce paradigms for data-parallel computations.

The suite was subjected to extensive comparison against Fortran and C++/OpenMP implementations, exposing both performance and expressiveness trade-offs in sequential and parallel contexts.

3. Ultra-Low-Power Neural Accelerator Benchmarking

The second major application of NPBench is as a standardized benchmarking framework for commercially available ultra-low-power $\mu$NPUs (Millar et al., 28 Mar 2025). This suite targets on-device neural network inference, capturing not only raw computation but also pipeline initialization, memory I/O (weight and tensor movement), power, and overall efficiency:

• Each device is configured to a standard CPU frequency for fair comparison (typically 100 MHz, with normalization formulas where necessary).
• The inference pipeline is decomposed: initialization, memory transfer, actual NPU execution, and CPU-side postprocessing are timed and analyzed independently.
• NPBench employs an open-source model compilation workflow that transforms base models (from Torch, ONNX, etc.) into device-specific binaries, supporting cross-platform deployment and INT8 quantization for standardization.
• Metrics include stage-by-stage latency, energy efficiency (inferences per mJ), and memory utilization parsed from linker map files. Power is measured externally using precision equipment, and results are visualized in both tabular and graphical form.
• Evaluation highlights disparities between theoretical compute capability (GOPS) and realized performance, underscoring the impact of architectural details such as memory I/O optimization.

This facilitates comparative studies across specialized $\mu$NPUs (MAX78000, HX-WE2, GAP8, MILK-V) and general-purpose MCUs, illuminating both hardware- and software-level inefficiencies.

4. Comparative Performance and Methodological Analysis

Both NPBench lines establish methodologies emphasizing reproducibility, detailed metric collection, and nuanced interpretation of platform/language trade-offs:

Dimension	NAS Parallel Benchmarks (NPB-Rust)	Ultra-Low-Power $\mu$NPU Benchmark
Language/Platform Focus	Rust, C++, Fortran; HPC servers/clusters	Dedicated $\mu$NPU, MCUs; embedded devices
Metric Suite	Sequential/parallel runtime; scalability	Latency, power, efficiency, memory footprint
Parallelization	Rayon (Rust), OpenMP (C++, Fortran)	Online NPU/CPU execution, multi-stage timing
Benchmark Objective	Language/parallel library side-by-side	Accelerator hardware and deployment strategy

In NPB-Rust, sequential Rust is 1.23% slower than Fortran and 5.59% faster than C++; for parallel, Rayon trails OpenMP in most kernels but demonstrates competitive scaling in embarrassingly parallel scenarios. $\mu$NPU benchmarks reveal that memory I/O can incur up to 90% of inference latency (MAX78000), counteracting raw compute gains shown by platforms like HX-WE2 or MILK-V.

5. Benchmarks as Cross-Domain Foundations

NPBench interfaces with related benchmarking and optimization efforts across scientific computing and machine learning. The DaCe AD framework (Boudaoud et al., 2 Sep 2025) demonstrates how NPBench kernels, both scientific and data-centric, can serve as rigorous testbeds for automatic differentiation (AD) engines. DaCe AD operates by transforming scientific kernels into stateful dataflow graphs (SDFGs) and extracting critical computation subgraphs (CCS) to instantiate efficient, memory-optimized backward passes. This leads to substantial performance improvements (over 92× average speedup against JAX for NPBench), attained without code modification and further supporting NPBench's role in bridging high-performance computing and modern ML development.

6. Limitations and Implications

NPBench, as described, is subject to several inherent limitations and practical considerations:

• In the Rust/NPB context, performance bottlenecks in parallelization stem from library-level features (lack of barrier control in Rayon) and the necessity for unsafe blocks to handle multi-dimensional index computations.
• $\mu$NPU benchmarks highlight architectural constraints: memory layout and buffer sizing, nontrivial initialization overheads, and discrepancies between specification-theoretic expectations and practical results.
• Both approaches provide open-source workflows to reduce deployment complexity and promote reproducibility, yet real-world use may be limited by operator support, fallback penalties (CPU execution for unsupported ops on $\mu$NPUs), and hardware-specific toolchain integration.

A plausible implication is that future benchmark suites and optimization frameworks will be increasingly interdependent, requiring unified models for scientific and ML workloads to guide hardware-software co-design at both the language and accelerator levels.

7. Future Directions and Research Significance

NPBench establishes comprehensive benchmarks that impact software engineering, hardware design, and scientific reproducibility standards. In scientific computing, suites like NPB-Rust validate Rust's safety guarantees and parallel strategies, providing testbeds for language and library advances. In $\mu$NPU research, the framework exposes efficiency bottlenecks and guides targeted improvements in memory hierarchy and operator support.

Further implication is that continued evolution of NPBench—encompassing both scientific kernels and neural inference pipelines—will steer best practices for heterogeneous deployments, inform compiler optimizations and automatic differentiation systems, and foster a more rigorous, standardized methodology for benchmarking in both academic and industrial settings.