The Materials Commons Data Repository G. Tarcea, B. Puchala, T. Berman, G. Scorzelli, V. Pascucci, M, Taufer, J. Allison. In 2022 IEEE 18th International Conference on e-Science (e-Science), pp. 405--406. 2022. DOI: 10.1109/eScience55777.2022.00060 Repositories are increasingly used for publishing and sharing scientific data. The Materials Commons is a data repository that follows the FAIR (Findable, Accessible, Inter-operable, Reusable) principles. We demonstrate the challenges with FAIR and how Materials Commons solves them. We also discuss the Nationals Science Data Fabric (NSDF) [1], a project that is democratizing data access, and show how Materials Commons with the NSDF software stack accelerates data access and scientific research. |
Adaptive and Implicit Regularization for Matrix Completion Subtitled arXiv preprint arXiv:2208.05640, Z. Li, T. Sun, H. Wang, B. Wang. 2022. The explicit low-rank regularization, e.g., nuclear norm regularization, has been widely used in imaging sciences. However, it has been found that implicit regularization outperforms explicit ones in various image processing tasks. Another issue is that the fixed explicit regularization limits the applicability to broad images since different images favor different features captured by different explicit regularizations. As such, this paper proposes a new adaptive and implicit low-rank regularization that captures the low-rank prior dynamically from the training data. The core of our new adaptive and implicit low-rank regularization is parameterizing the Laplacian matrix in the Dirichlet energy-based regularization, which we call the regularization AIR. Theoretically, we show that the adaptive regularization of AIR enhances the implicit regularization and vanishes at the end of training. We validate AIR’s effectiveness on various benchmark tasks, indicating that the AIR is particularly favorable for the scenarios when the missing entries are non-uniform. The code can be found at https://github.com/lizhemin15/AIR-Net. |
Assembling Portable In-Situ Workflow from Heterogeneous Components using Data Reorganization B. Zhang, P. Subedi, P. E. Davis, F. Rizzi, K. Teranishi, M. Parashar. In 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 41-50. 2022. DOI: 10.1109/CCGrid54584.2022.00013 Heterogeneous computing is becoming common in the HPC world. The fast-changing hardware landscape is pushing programmers and developers to rely on performance-portable programming models to rewrite old and legacy applications and develop new ones. While this approach is suitable for individual applications, outstanding challenges still remain when multiple applications are combined into complex workflows. One critical difficulty is the exchange of data between communicating applications where performance constraints imposed by heterogeneous hardware advantage different data layouts. We attempt to solve this problem by exploring asynchronous data layout conversions for applications requiring different memory access patterns for shared data. We implement the proposed solution within the DataSpaces data staging service, extending it to support heterogeneous application workflows across a broad spectrum of programming models. In addition, we integrate heterogeneous DataSpaces with the Kokkos programming model and propose the Kokkos Staging Space as an extension of the Kokkos data abstraction. This new abstraction enables us to express data on a virtual shared space for multiple Kokkos applications, thus guaranteeing the portability of each application when assembling them into an efficient heterogeneous workflow. We present performance results for the Kokkos Staging Space using a synthetic workflow emulator and three different scenarios representing access frequency and use patterns in shared data. The results show that the Kokkos Staging Space is a superior solution in terms of time-to-solution and scalability compared to existing file-based Kokkos data abstractions for inter-application data exchange. |
A scalable adaptive-matrix SPMV for heterogeneous architectures H. D. Tran, M. Fernando, K. Saurabh, B. Ganapathysubramanian, R. M. Kirby, H. Sundar. In 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), IEEE, pp. 13--24. 2022. DOI: 10.1109/IPDPS53621.2022.00011 In most computational codes, the core computational kernel is the Sparse Matrix-Vector product (SpMV) that enables specialized linear algebra libraries like PETSc to be used, especially in the distributed memory setting. However, optimizing SpMvperformance and scalability at all levels of a modern heterogeneous architecture can be challenging as it is characterized by irregular memory access. This work presents a hybrid approach (HyMV) for evaluating SpMV for matrices arising from PDE discretization schemes such as the finite element method (FEM). The approach enables localized structured memory access that provides improved performance and scalability. Additionally, it simplifies the programmability and portability on different architectures. The developed HyMV approach enables efficient parallelization using MPI, SIMD, OpenMP, and CUDA with minimum programming effort. We present a detailed comparison of HyMV with the two traditional approaches in computational code, matrix-assembled and matrix-free approaches, for structured and unstructured meshes. Our results demonstrate that the HyMV approach achieves excellent scalability and outperforms both approaches, e.g., achieving average speedups of 11x for matrix setup, 1.7x for SpMV with structured meshes, 3.6x for SpMV with unstructured meshes, and 7.5x for GPU SpMV. |
Colza: Enabling Elastic In Situ Visualization for High-performance Computing Simulations M. Dorier, Z. Wang, U. Ayachit, S. Snyder, R. Ross, M. Parashar. In 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS), IEEE, pp. 538-548. 2022. DOI: 10.1109/IPDPS53621.2022.00059 In situ analysis and visualization have grown increasingly popular for enabling direct access to data from high-performance computing (HPC) simulations. As a simulation progresses and interesting physical phenomena emerge, however, the data produced may become increasingly complex, and users may need to dynamically change the type and scale of in situ analysis tasks being carried out and consequently adapt the amount of resources allocated to such tasks. To date, none of the production in situ analysis frameworks offer such an elasticity feature, and for good reason: the assumption that the number of processes could vary during run time would force developers to rethink software and algorithms at every level of the in situ analysis stack. In this paper we present Colza, a data staging service with elastic in situ visualization capabilities. Colza relies on the widely used ParaView Catalyst in situ visualization framework and enables elasticity by replacing MPI with a custom collective communication library based on the Mochi suite of libraries. To the best of our knowledge, this work is the first to enable elastic in situ visualization capabilities for HPC applications on top of existing production analysis tools. |