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EduPar-23 proceedings editorial and oarganization 

Welcome to the 4 th Workshop on Education for High Performance Computing (EduHiPC 2022). The EduHiPC 2022 workshop, held in conjunction with the IEEE International Conference on High Performance Computing Data & Analytics (HiPC 2022), is devoted to the development and assessment of educational and curricular innovations and resources for undergraduate and graduate education in Parallel and Distributed Computing (PDC) and High Performance Computing (HPC). EduHiPC brings together individuals from academia, industry, and other educational and research institutes to explore new ideas, challenges, and experiences related to PDC pedagogy and curricula. The workshop is designed in coordination with the IEEE TCPP curriculum initiative on parallel and distributed computing ( hitps://tcpp.cs.gsu .edu/curriculum/) for undergraduates majoring in computer science and computer engineering. It is supported by C-DAC, India and the US National Science Foundation (NSF) supported Center for Parallel and Distributed Computing Curriculum Development and Educational Resources (CDER). Details for attending the workshop are available on the HiPC webpage (HiPC). The effect of pandemic on academic and research community seems now to be globally receding as was evident from the enthusiastic in-person participation of conference delegates. Please visit the EduHiPC-22 webpage for the complete online proceedings, including copies of papers and presentation slides: EduHiPC 2022 | NSF/IEEE-TCPP Curriculum Initiative. 

Parallel and distributed computing (PDC) has become pervasive in all aspects of computing, and thus it is essential that students include parallelism and distribution in the computational thinking that they apply to problem solving, from the very beginning. Computer science education is still teaching to a 20th century model of algorithmic problem solving. Sequence, branch, and loop are taught in our early courses as the only organizing principles needed for algorithms, and we invest considerable time in showing how best to sequentially process large volumes of data. All computing devices that students use currently have multiple cores as well as a GPU in many cases. Most of their favorite applications use multiple cores and numbers of distributed processors. Often concurrency offers simpler solutions than sequential approaches. Industry is desperate for software engineers who think naturally in terms of exploiting these capabilities, rather than seeing them as an exotic upper-level topic that gets layered over a sequential solution. However, we are still teaching students to solve problems using sequential thinking. In this workshop we overview key PDC concepts and provide examples of how they may naturally be incorporated in early computing classes. We will introduce plugged and unplugged curriculum modules that have been successfully integrated in existing computing classes at multiple institutions. We will highlight the upcoming summer training workshop, for which we have funding to support attendance, as well as other CDER (Center for Parallel and Distributed Computing Curriculum Development and Educational Resources) activities. 

Decision trees and tree ensembles are popular supervised learning models on tabular data. Two recent research trends on tree models stand out: (1) bigger and deeper models with many trees, and (2) scalable distributed training frameworks. However, existing implementations on distributed systems are IO-bound leaving CPU cores underutilized. They also only find best node-splitting conditions approximately due to row-based data partitioning scheme. In this paper, we target the exact training of tree models by effectively utilizing the available CPU cores. The resulting system called TreeServer adopts a column-based data partitioning scheme to minimize communication, and a node-centric task-based engine to fully explore the CPU parallelism. Experiments show that TreeServer is up to 10x faster than models in Spark MLlib. We also showcase TreeServer's high training throughput by using it to build big "deep forest" models.