This book constitutes the proceedings of the 15th IFIP International Conference on Network and Parallel Computing, NPC 2018, held in Muroran, Japan, in November/December 2018.

The 22 full and 12 short papers presented in this volume were carefully reviewed and selected from 72 submissions. The papers cover traditional areas of network and parallel computing, including parallel applications, distributed algorithms, parallel architectures, software environments, and distributed tools.

This book presents a hybrid static-dynamic approach for efficient performance analysis of parallel applications on HPC systems. Performance analysis is essential to finding performance bottlenecks and understanding the performance behaviors of parallel applications on HPC systems. However, current performance analysis techniques usually incur significant overhead. Our book introduces a series of approaches for lightweight performance analysis.

We combine static and dynamic analysis to reduce the overhead of performance analysis. Based on this hybrid static-dynamic approach, we then propose several innovative techniques for various performance analysis scenarios, including communication analysis, memory analysis, noise analysis, computation analysis, and scalability analysis. Through these specific performance analysis techniques, we convey to readers the idea of using static analysis to support dynamic analysis.

To gain the most from the book, readers should have a basicgrasp of parallel computing, computer architecture, and compilation techniques.

This book constitutes the proceedings of the 15th IFIP International Conference on Network and Parallel Computing, NPC 2018, held in Muroran, Japan, in November/December 2018.

The 22 full and 12 short papers presented in this volume were carefully reviewed and selected from 72 submissions. The papers cover traditional areas of network and parallel computing, including parallel applications, distributed algorithms, parallel architectures, software environments, and distributed tools.

This book presents a hybrid static-dynamic approach for efficient performance analysis of parallel applications on HPC systems. Performance analysis is essential to finding performance bottlenecks and understanding the performance behaviors of parallel applications on HPC systems. However, current performance analysis techniques usually incur significant overhead. Our book introduces a series of approaches for lightweight performance analysis.

We combine static and dynamic analysis to reduce the overhead of performance analysis. Based on this hybrid static-dynamic approach, we then propose several innovative techniques for various performance analysis scenarios, including communication analysis, memory analysis, noise analysis, computation analysis, and scalability analysis. Through these specific performance analysis techniques, we convey to readers the idea of using static analysis to support dynamic analysis.

To gain the most from the book, readers should have a basicgrasp of parallel computing, computer architecture, and compilation techniques.