ParaMo Workshop 2021

Welcome to ParaMo Workshop 2021

The notion of cloud computing has changed the way how we utilize computing resources. Since High Performance Computing (HPC) has long been suffered from under- or over-utilization of resources, many researchers are trying to adapt HPC applications, such as AI, big data, and computational science, to the cloud environment. With proper adaptation, HPC applications are able to enhance their resource utilization ratio and scalability by using virtualized and on-demand resources on clouds. While we discuss HPC on clouds, we should discuss the parallel programming models as well. Various parallel programming models and their frameworks (e.g., TensorFlow, PyTorch, MapReduce, MPI, OpenMP, OpenCL, CUDA) have been proposed for parallel computing. These parallel programming models and frameworks should be carefully designed for HPC applications to achieve high-performance and efficient resource usage in clouds as new architectures and workloads are emerging. For example, we have to address data locality [1], resource management [2, 3], programming environments and algorithms [4, 5, 6]. The ParaMo workshop will provide a venue for researchers to discuss such challenges to parallel programming models in high-performance cloud.

Important Date

Submission deadline:
June 14, 2021 (extended)

Acceptance notification:
June 30, 2021

Camera-ready due:
July 14, 2021

Workshop date:
August 30, 2021

Call For Papers

Reference

[1] M. Bae, S. Yeo, G. Park, and S. Oh, “Novel data‐placement scheme for improving the data locality of Hadoop in heterogeneous environments,” Concurrency and Computation: Practice and Experience, Mar. 2020.

[2] C.-G. Lee, J.-Y. Cho, J. Kim, and H.-W. Jin, “Transparent many-core partitioning for high- performance big data I/O,” Concurrency and Computation: Practice and Experience, Sep. 2020.

[3] L. Thamsen, J. Beilharz, V. T. Tran, S. Nedelkoski, and O. Kao, “Mary, Hugo, and Hugo*: Learning to schedule distributed data‐parallel processing jobs on shared clusters,” Concurrency and Computation: Practice and Experience, May 2020.

[4] G. Cordasco, M. D'Auria, A. Negro, V. Scarano, and C. Spagnuolo, “Toward a domain‐specific language for scientific workflow‐based applications on multicloud system,” Concurrency and Computation: Practice and Experience, Apr. 2020.

[5] M. Nowicki, L. Górski, and P. Bala, “Performance Evaluation of Java/PCJ implementation of parallel algorithms on the cloud,” The 2nd International Workshop on Parallel Programming Models in High-Performance Cloud (ParaMo 2020), Aug. 2020.

[6] T. Jiang, W. Wu, and Y. Pu, “Parallelizing automatic temporal cognitive tool for large-scale online learning analytics,” The 2nd International Workshop on Parallel Programming Models in High- Performance Cloud (ParaMo 2020), Aug. 2020.

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