A Novel Low-Complexity Four-Way Parallel MDST Algorithm for GPU and Multicore CPU Architectures

Abstract

The Modified Discrete Sine Transform (MDST) is widely used in signal-processing applications where computational efficiency, latency, and energy consumption are critical design constraints. This paper introduces a low-complexity, parallel MDST algorithm that decomposes the transform into four independent computational sections, reducing arithmetic redundancy and enabling concurrent execution on GPU and multicore CPU architectures. The proposed implementation was evaluated on NVIDIA Jetson AGX Xavier, Jetson AGX Orin, and GeForce RTX 4090 GPUs, as well as on an Intel Core i9-14900K CPU. For isolated N = 34 transforms, the proposed formulation achieved GPU speedups of 7.55–76.85 relative to the classical MDST, depending on the platform and operating frequency, while CPU speedups ranged from 6.71 to 12.13 under controlled conditions. A complementary standard Linux experiment preserved a 6.58× CPU speedup, showing that the performance advantage does not depend on specialized core isolation. Compute-only measurements on the Jetson AGX Xavier further showed an approximately 99% reduction in GPU energy per transform. Memory profiling confirmed substantial reductions in global and local memory traffic. Because a single four-section transform provides insufficient parallelism to fully utilize a modern GPU, an additional batched implementation was developed to exploit inter-transform parallelism. On the Jetson AGX Orin, throughput reached approximately 82.17 × 106 transforms/s, with 98.34% SM activity and 80.84% SM throughput, demonstrating that the proposed decomposition is particularly effective for batched, multichannel, and high-throughput workloads. These results show that the proposed MDST reformulation combines reduced arithmetic and memory complexity with efficient deployment across heterogeneous parallel architectures.

The authors' abstract, as published at the source. Eng—Advances in Engineering, 2026 · DOI ↗