Call For Papers

High-Performance Computing and Big Data are two of the areas where NPC 2022 will provide a dynamic forum to explore, discuss and debate state-of-the-art technology issues and challenges. High-performance computers and big-data systems are tied inextricably to the broader computing ecosystem and its designs and market adoption. They also highlight information security needs and economic competitiveness in ways that distinguish them from most other scientific instruments. The stakes are high, and far beyond the boundaries of nations and continents, which is why NPC seeks to foster broad international participation.

We share the view that, during the past decade, the tools and cultures of high-performance computing and big data analytics have been diverging to the detriment of both, and the international community should find a unified path that can best serve the need of a broad spectrum of major application areas. Unlike other tools, which are limited to particular scientific domains, computational modeling and data analytics are applicable to all areas of science and engineering, as they breathe life into the underlying mathematics of scientific models.

Topics of interest include, but are not limited to:

Parallel and distributed applications and algorithms

• Parallel and distributed issues and opportunities with Artificial intelligence applications.

• Parallel algorithms for computational and data-enabled scientific, engineering, biological, and medical applications.

• Parallel algorithms for accelerators, neuromorphic architectures, and other emerging architectures.

Parallel and distributed architectures and systems

• Domain-Specific Accelerators for AI, deep learning and applications in industry sectors (such as health: genomics, finance: blockchain, etc.).

• Non-traditional Computing Technology (Quantum/Optical/Superconducting computers).

• Emerging architectures and systems at all scales, from embedded to cloud.

• Instruction-, thread-, and data-level parallelism.

• Systems for enabling parallelism at extreme scale.

• Power-efficient and green computing systems.

• Neuromorphic architectures and cognitive computing accelerators.

• Heterogeneous multicore architectures and accelerators.

• In-Memory and near-data computing.

• Network and interconnect architectures.

• Storage systems in novel big data architectures.

Parallel and distributed software environments and tools

• Programming models and compilation for existing and emerging platforms.

• Dataflow programming models, frameworks, languages and environments for data-enabled platforms.

• Virtualization of machines, networks, and storage.

• I/O, file systems, and data management.

• Resource management, scheduling, and load balancing.