DecodeX: Exploring and Benchmarking of LDPC Decoding across CPU, GPU, and ASIC Platforms

TL;DR

DecodeX is a comprehensive benchmarking framework that evaluates LDPC decoding performance across CPU, GPU, and ASIC platforms, revealing platform-specific trade-offs and guiding future heterogeneous vRAN designs.

Contribution

The paper introduces DecodeX, a unified framework for benchmarking LDPC decoding across diverse hardware, enabling systematic analysis and optimization for next-generation wireless systems.

Findings

Distinct trade-offs in parallel efficiency and offload overhead across platforms.

Decoding latency varies significantly with data movement and workload granularity.

Insights support adaptive scheduling and co-design for energy-efficient vRANs.

Abstract

Emerging virtualized radio access networks (vRANs) demand flexible and efficient baseband processing across heterogeneous compute substrates. In this paper, we present DecodeX, a unified benchmarking framework for evaluating low-density parity-check (LDPC) decoding acceleration across different hardware platforms. DecodeX integrates a comprehensive suite of LDPC decoder implementations, including kernels, APIs, and test vectors for CPUs (FlexRAN), GPUs (Aerial and Sionna-RK), and ASIC (ACC100), and can be readily extended to additional architectures and configurations. Using DecodeX, we systematically characterize how different platforms orchestrate computation-from threading and memory management to data movement and accelerator offload-and quantify the resulting decoding latency under varying Physical layer parameters. Our observations reveal distinct trade-offs in parallel efficiency and offload overhead, showing that accelerator gains strongly depend on data-movement and workload granularity. Building on these insights, we discuss how cross-platform benchmarking can inform adaptive scheduling and co-design for future heterogeneous vRANs, enabling scalable and energy-efficient baseband processing for NextG wireless systems.