[Paper] Compiler-supported reduced precision and AoS-SoA transformations for heterogeneous hardware

Source: arXiv - 2512.05516v1

Overview

The paper investigates how Array‑of‑Structures (AoS) ↔ Structure‑of‑Arrays (SoA) layout transformations combined with reduced‑precision data types can be leveraged on modern heterogeneous systems (CPU + GPU). By introducing lightweight compiler annotations, the authors show that developers can control where and when these transformations happen, yielding sizable speedups for particle‑simulation kernels on both Nvidia and AMD GPUs.

Key Contributions

• Compiler‑level annotations that let programmers specify AoS↔SoA and precision‑conversion points, and decide whether they run on the host CPU or the target GPU.
• In‑place, on‑the‑fly data layout conversion for accelerators that share a unified memory space with the CPU, avoiding costly explicit data copies.
• Empirical evaluation on two GPU families (Nvidia G200 and AMD MI300A) using a real‑world Lagrangian particle simulation, demonstrating up to 2.6× speedup on Nvidia hardware and more stable gains on AMD.
• Design guidelines for when reduced‑precision and layout transformations are beneficial in bandwidth‑bound kernels.
• A proof‑of‑concept implementation integrated into an existing compiler toolchain, showing that the approach can be adopted without rewriting the whole application.

Methodology

1. Baseline code – A particle‑simulation kernel written in a typical AoS layout and using IEEE‑754 double precision.
2. Annotation insertion – Developers add simple pragma‑style directives (e.g., #pragma aoS2soa or #pragma reduce_precision) around data structures or kernel launches.
3. Compiler extension – The extended compiler parses the annotations, generates two versions of the data layout (AoS for host‑side logic, SoA for GPU kernels) and inserts the necessary conversion kernels.
4. Execution strategies
   • Pre‑copy: Convert and copy data to the GPU before kernel launch.
   • On‑demand: Keep data in a unified memory region and let the GPU perform in‑place conversion just before it is consumed.
5. Benchmarking – Run the transformed kernels on Nvidia G200 and AMD MI300A, measuring execution time, memory bandwidth, and energy consumption.
6. Analysis – Compare the performance of each strategy and quantify the impact of reduced‑precision (e.g., FP16, bfloat16) versus full precision.

Results & Findings

Key take‑aways

• SoA layout is a natural fit for SIMT execution, allowing coalesced memory accesses.
• Reduced precision cuts memory bandwidth roughly in half, which is the dominant bottleneck for many Lagrangian kernels.
• In‑place conversion on the accelerator can be competitive with explicit host‑side copies, especially on hardware with unified memory.

Practical Implications

• For GPU‑accelerated developers: Adding a few compiler pragmas can unlock bandwidth savings without a full code rewrite.
• Performance‑critical Lagrangian codes (e.g., particle‑in‑cell, SPH, molecular dynamics) can adopt the AoS→SoA + reduced‑precision pattern to scale to larger problem sizes on existing hardware.
• Unified‑memory systems (e.g., Nvidia’s NVLink‑based superchips, AMD’s Infinity Fabric) can benefit from on‑the‑fly layout conversion, simplifying data‑movement pipelines.
• Energy efficiency improves because less data is moved across the PCIe or interconnect, which is increasingly important for exascale workloads.
• Tooling impact: The annotation approach can be integrated into existing build systems (CMake, Make) and works with standard CUDA/HIP kernels, lowering the barrier for adoption.

Limitations & Future Work

• Kernel scope – The study focuses on a handful of compute‑intensive kernels; results may vary for kernels with different arithmetic intensity or control flow.
• Hardware dependence – Speedups differ between Nvidia and AMD GPUs; the optimal strategy (pre‑copy vs. in‑place) is hardware‑specific.
• Compiler support – The prototype requires a custom compiler extension; mainstream compilers have yet to adopt these annotations.
• Precision safety – Reduced‑precision must be validated for numerical stability on a case‑by‑case basis; the paper does not provide a generic error‑analysis framework.

Future directions include extending the annotation system to automatically infer the best layout/precision per kernel, integrating with auto‑tuning frameworks, and evaluating the approach on upcoming heterogeneous architectures (e.g., ARM‑based GPUs, Intel Xe).

Authors

• Pawel K. Radtke
• Tobias Weinzierl

Paper Information

• arXiv ID: 2512.05516v1
• Categories: cs.PL, cs.DC, cs.MS
• Published: December 5, 2025
• PDF: Download PDF