The field of high-performance computing is moving towards optimizing and scaling heterogeneous computing environments. Researchers are exploring innovative approaches to integrate heuristics, meta-heuristics, machine learning, and emerging quantum computing techniques to improve workload optimization. Hybrid optimization methods are being developed to strategically integrate different techniques and significantly improve scalability, efficiency, and adaptability. Noteworthy papers in this area include:

• A Review of Tools and Techniques for Optimization of Workload Mapping and Scheduling in Heterogeneous HPC System, which highlights the need for hybrid optimization approaches.
• Task-parallelism in SWIFT for heterogeneous compute architectures, which presents novel combinations of algorithms for leveraging CPUs and GPUs in a manner that minimizes CPU-GPU communication latency.
• PSMOA: Policy Support Multi-Objective Optimization Algorithm for Decentralized Data Replication, which proposes a novel algorithm for optimizing data replication in decentralized storage systems.