Session

DescriptionDue to the heterogeneous datasets they process, data-intensive applications employ diverse methods and data structures, exhibiting irregular data accesses, control flows, and communication patterns. Modern data analytics applications additionally require supporting dynamic data structures, asynchronous control flows, and mixed parallel programming models. Supercomputing systems are organized around software and hardware optimized for data locality and bulk synchronous computations. Managing irregular behaviors requires a substantial programming effort and lacks integration, leading to poor performance. Holistic solutions to these challenges emerge only by considering the problem from multiple perspectives: from micro- to system architectures, from compilers to languages, from libraries to runtimes, and algorithm design to data characteristics. Only collaborative efforts among researchers with different expertise, including domain experts and end users, can lead to significant breakthroughs. This workshop brings together scientists from different backgrounds to discuss methods and technologies for efficiently supporting irregular applications in current and future architectures.