@article{Lu2026, 
author = {Xiaoyi Lu},
title = {HEC: Heterogeneity-Enriched Communication for AI Symphony},
year = {2026},
journal = {Journal of Computer Science and Technology},
volume = {41},
number = {1},
pages = {170-192},
keywords = {heterogeneity, artificial intelligence, data communication, scalability, efficiency},
url = {https://www.sciopen.com/article/10.1007/s11390-026-6076-1},
doi = {10.1007/s11390-026-6076-1},
abstract = {Modern parallel and distributed computing systems are becoming increasingly complex as applications in high-performance computing (HPC) and artificial intelligence (AI) demand ever-greater levels of computation and communication efficiency. To address these demands, recent architectures integrate heterogeneous computing devices, such as CPUs, GPUs, and DPUs (or SmartNICs), within a single compute node, forming what we refer to as multi-rail heterogeneity. This trend offers substantial potential for scalability and performance but also amplifies the challenges of data movement, synchronization, and coordination across heterogeneous components. We propose Heterogeneity-Enriched Communication (HEC) as a new paradigm that embraces multi-rail heterogeneity by accurately analyzing communication primitives, adaptively composing multi-rail strategies, and scalably optimizing end-to-end pipelines. Through three representative case studies, including HCCL (collective communication), TrimEC (multi-rail erasure coding), and DPU-KV (edge data services), we demonstrate that HEC improves efficiency, scalability, and resilience in parallel and distributed systems for AI workloads. We envision HEC as a foundation for the next generation of AI infrastructure, harmonizing heterogeneous computing instruments into a symphony of scalable and efficient systems tailored for the emerging AI era.}
}