We plan to develop a solution to the well-known data placement problem caused by the growing data processing demands of increasingly complex and heterogeneous computing systems. The resulting compiler and runtime techniques will ensure that the applications that support national missions can be adapted to emerging extreme-scale architectures with complex memory hierarchies.
Publications, Presentations, Etc.
Mendonca, G., et al. 2019. "AutoParBench: A Unified Test Framework for OpenMP-based Parallelizers." International Parallel & Distributed Processing Symposium. LLNL-CONF-795158.
Mishra, A., et al. 2019. "Data Reuse Analysis for GPU Offloading using OpenMP." ACM SRC at SuperComputing 2019. LLNL-ABS-784874.
Nan, Z., et al. 2019. "Deep Semantic-Based Co-Evolvement for Synthesizing CodeAnalysis from Natural Language." International Conference on Software Engineering. LLNL-CONF-794949.
Pirkelbauer, P., et al. 2019. "XPlacer: Automatic Analysis of CPU/GPU Access Patterns." International Parallel & Distributed Processing Symposium. LLNL-CONF-795057.
Wang, A., et al. 2019. "Leveraging Smart Data Transfer and Metadirective in AdaptiveComputing." SC19. LLNL-ABS-795180.
Xu, H., et al. 2019. "Machine Learning Guided Optimal Use of GPU Unified Memory." MCHPC'19: Workshop on Memory Centric High Performance Computing. LLNL-CONF-793704.