New Challenges in High-Performance Computing and Computer Architecture
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 June 2026 | Viewed by 84
Special Issue Editors
Interests: storage system; computer architecture
Special Issue Information
Dear Colleagues,
The relentless growth of data-intensive workloads in fields such as artificial intelligence, scientific simulation, and big data analytics is pushing the boundaries of modern computing systems. To meet these demands, continuous innovation in high-performance computing (HPC) and computer architecture is paramount, driving the evolution of next-generation computational infrastructures. These advancements are not only crucial for academic research but also serve as a key enabler for economic competitiveness and technological leadership across numerous sectors. This Special Issue aims to collate cutting-edge research that describes, defines, and addresses the critical challenges in achieving extreme performance, energy efficiency, and scalability in computing systems.
Topics of interest include, but are not limited to, the following:
- Novel computer architectures (e.g., heterogeneous, quantum-inspired, and neuromorphic computing);
- Accelerator designs for AI, machine learning, and deep learning workloads (e.g., GPUs, FPGAs, and domain-specific architectures);
- High-performance and energy-efficient computing systems;
- Parallel and distributed computing models, algorithms, and applications;
- Advances in memory and storage hierarchies (e.g., non-volatile memory, processing-in-memory);
- Interconnection networks and communication technologies for large-scale systems;
- Performance modeling, evaluation, and benchmarking of computing systems;
- Sustainability and cooling technologies for large-scale HPC data centers.
Dr. Qiao Li
Dr. Jinhua Cui
Guest Editors
Manuscript Submission Information
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Keywords
- high-performance computing
- computer architecture
- heterogeneous computing
- hardware accelerators
- memory systems
- energy efficiency
- parallel processing
- interconnection networks
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