What are the most promising new HPC architectures and paradigms?

1 Quantum Computing

Quantum computing is a paradigm that exploits the quantum mechanical properties of subatomic particles, such as superposition and entanglement, to perform computations that are impossible or impractical for classical computers. Quantum computers can potentially solve problems that are intractable for HPC systems, such as optimization, cryptography, simulation, and machine learning. However, quantum computing also faces many technical and practical challenges, such as noise, error correction, scalability, and interoperability. Therefore, current quantum computers are mainly used for proof-of-concept experiments and demonstrations, rather than for production applications. However, as the technology matures and becomes more accessible, quantum computing could revolutionize HPC and enable new discoveries and innovations.

2 Neuromorphic Computing

Neuromorphic computing is a paradigm that mimics the structure and function of biological neural networks, such as the human brain, to perform computations that are efficient and adaptive. Neuromorphic computing can offer advantages over traditional HPC systems, such as low power consumption, high parallelism, fault tolerance, and learning capability. Neuromorphic computing can be implemented using various hardware technologies, such as analog circuits, memristors, spintronics, and nanowires. Neuromorphic computing can be applied to domains and applications that require high levels of cognition, perception, and intelligence, such as image recognition, natural language processing, robotics, and bioinformatics.

3 Edge Computing

Edge computing is a paradigm that distributes the computation and data processing from centralized servers or clouds to the edge devices or nodes, such as sensors, smartphones, or vehicles. Edge computing can reduce the latency, bandwidth, and energy consumption of HPC applications, as well as improve the privacy, security, and reliability of the data. Edge computing can also enable real-time and interactive HPC applications, such as augmented reality, smart cities, autonomous driving, and Internet of Things. However, edge computing also poses challenges for HPC systems, such as heterogeneity, scalability, coordination, and management. Therefore, edge computing requires novel HPC architectures and paradigms that can integrate and optimize the resources and capabilities of the edge devices and nodes.

4 Exascale Computing

Exascale computing is a paradigm that aims to achieve HPC systems that can perform at least one exaflop, or one quintillion floating-point operations per second. Exascale computing can enable HPC applications that require extreme levels of computation and data processing, such as climate modeling, genomics, cosmology, and nuclear physics. However, exascale computing also requires significant advances in HPC architectures and paradigms, such as hardware design, software development, power efficiency, cooling systems, and fault tolerance. Therefore, exascale computing is a collaborative and multidisciplinary effort that involves various stakeholders, such as governments, industries, academia, and users.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?