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This document describes a two-day meeting held for the Principal Investigators (PIs) of NSF CyberTraining grants. The report covers invited talks, panels, and six breakout sessions. The meeting involved over 80 PIs and NSF program managers (PMs). The lessons recorded in detail in the report are a wealth of information that could help current and future PIs, as well as NSF PMs, understand the future directions suggested by the PI community. The meeting was held simultaneously with that of the PIs of the NSF Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program. This co-location led to two joint sessions: one with NSF speakers and the other on broader impact. Further, the joint poster and refreshment sessions benefited from the interactions between CSSI and CyberTraining PIs., Comment: 38 pages, 3 main sections and 2 Appendix sections, 2 figures, 19 tables; updated version: author corrections

The Quadratic Assignment Problem (QAP) is an important combinatorial optimization problem with applications in many areas including logistics and manufacturing. QAP is known to be NP-hard, a computationally challenging problem, which requires the use of sophisticated heuristics in finding acceptable solutions for most real-world data sets. In this paper, we present GPU-accelerated implementations of a 2opt and a tabu search algorithm for solving the QAP. For both algorithms, we extract parallelism at multiple levels and implement novel code optimization techniques that fully utilize the GPU hardware. On a series of experiments on the well-known QAPLIB data sets, our solutions, on average run an order-of-magnitude faster than previous implementations and deliver up to a factor of 63 speedup on specific instances. The quality of the solutions produced by our implementations of 2opt and tabu is within 1.03% and 0.15% of the best known values. The experimental results also provide key insight into the performance characteristics of accelerated QAP solvers. In particular, the results reveal that both algorithmic choice and the shape of the input data sets are key factors in finding efficient implementations., Comment: 25 pages, 9 figures; parts of this work appeared as short papers in XSEDE14 and XSEDE15 conferences. This version of the paper is a substantial extension of previous work with optimizations for newer GPU platforms and extended experimental results

The lightning talks at EduHPC provide an opportunity to share early results and insights on parallel and distributed computing (PDC) education and training efforts. The four lightning talks at EduHPC 2022 cover a range of topics in broadening PDC education: (i) curriculum development efforts for the European Masters in HPC program, (ii) bootcamps for CI professionals who support the running of AI workloads on HPC systems, (iii) a GPU programming course following the Carpentries model and (iv) peer-review assignments to help students write efficient parallel algorithms within sustainable software libraries., QC 20230508

The lightning talks at EduHPC provide an opportunity to share early results and insights on parallel and distributed computing (PDC) education and training efforts. The four lightning talks at EduHPC 2022 cover a range of topics in broadening PDC education: (i) curriculum development efforts for the European Masters in HPC program, (ii) bootcamps for CI professionals who support the running of AI workloads on HPC systems, (iii) a GPU programming course following the Carpentries model and (iv) peer-review assignments to help students write efficient parallel algorithms within sustainable software libraries., Peer Reviewed, Article signat per 26 autors/es: Apan Qasem 1, Hartwig Anzt 2,3, Eduard Ayguade 4, Katharine Cahill 5, Ramon Canal 4, Jany Chan 6, Eric Fosler-Lussier 6, Fritz Göbel 2, Arpan Jain 6, Marcel Koch 2, Mateusz Kuzak 7, Josep Llosa 4, Raghu Machiraju 6, Xavier Martorell 4, Pratik Nayak 2, Shameema Oottikkal 5, Marcin Ostasz 8, Dhabaleswar K. Panda 6, Dirk Pleiter 9, Rajiv Ramnath 6, Maria-Ribera Sancho 4, Alessio Sclocco 7, Aamir Shafi 6, Hanno Spreeuw 7, Hari Subramoni 6, Karen Tomko 7 / 1 Department of Computer Science, Texas State University, USA; 2 Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany; 3 University of Tennessee (UTK), Knoxville, USA; 4 Barcelona Supercomputing Center and Universitat Politècnica de Catalunya, Spain; 5 Ohio Supercomputer Center, USA; 6 College of Engineering, The Ohio State University, USA; 7 Netherlands eScience Center, The Netherlands; 8 ETP4HPC, The Netherlands; 9 PDC Center for High Performance Computing, KTH Royal Institute of Technology, Sweden, Postprint (author's final draft)

Future computing systems, from handhelds to supercomputers, will undoubtedly be more parallel and heterogeneous than todays systems to provide more performance and energy efficiency. Thus, GPUs are increasingly being used to accelerate general purpose applications, including applications with data dependent, irregular control flow and memory access patterns. However, the growing complexity, exposed memory hierarchy, incoherence, heterogeneity, and parallelism will make accelerator based systems progressively more difficult to program. In the foreseeable future, the vast majority of programmers will no longer be able to extract additional performance or energy savings from next generation systems be-cause the programming will be too difficult. Automatic performance analysis and optimization recommendation tools have the potential to avert this situation. They embody expert knowledge and make it available to software developers when needed. In this paper, we describe and evaluate such a tool.