In these architectures, data is distributed across several specialized data stores ... More importantly, the query optimizer should leverage in-memory parallel processing to perform post-processing ...

It, too, is a library for distributed parallel computing in Python ... accelerating machine-learning tasks or a particular data-processing framework. Pandaral·lel, as the name implies, is ...

Developing the infrastructure to support this capability starts with a distributed in-memory computing (IMC ... have been optimized for massively parallel processing. This enables each machine ...

Parallelization is the one of the effective ways to enhance the performance of processing in the cloud. However, conventional parallel distributed processing has issues involving lower processing ...

Big data solutions often involve distributed storage ... performance by leveraging parallel processing as well. Apache Spark, a popular big data framework, uses in-memory processing to boost ...

Arif, Moiz, et al. "Application-Attuned Memory Management for Containerized HPC Workflows." Proceedings of the Proceedings of the 38th IEEE International Parallel & Distributed Processing Symposium ...

Researchers at Tohoku University have discovered that there are two parallel processes involved in memory formation when ... be involved in the information processing in the brain, albeit in ...

Matsuoka, "Scaling Distributed Deep Learning Workloads beyond the Memory Capacity with KARMA," in Proceedings ... in Proceedings of the 33th IEEE International Parallel & Distributed Processing ...