MapReduce-based Parallelization of Sparse Matrix Kernels for Large-scale Scientific Applications
| Main Author: | Gunduz Vehbi Demirci |
|---|---|
| Other Authors: | Ata Turk, R. Oguz Selvitopi, Kadir Akbudak, Cevdet Aykanat |
| Format: | info publication-workingpaper Journal |
| Terbitan: |
, 2014
|
| Online Access: |
https://zenodo.org/record/822922 |
Daftar Isi:
- This whitepaper addresses applicability of the MapReduce paradigm for scientific computing by realizing it on the widely used sparse matrix-vector multiplication (SpMV) operation with a recent library developed for this purpose. Scaling SpMV operations proves vital as it is a kernel that finds its applications in many scientific problems from different domains. Generally, the scalability improvement of these operations is negatively affected by high communication requirements of the multiplication, especially at large processor counts in the case of strong scaling. We propose two partitioning-based methods to reduce these requirements and allow SpMV operations to be performed more efficiently. We demonstrate how to parallelize SpMV operations using MR-MPI, an efficient and portable library that aims at enabling usage of MapReduce paradigm in scientific computing. We test our methods extensively with different matrices. The obtained results show that utilization of communication-efficient methods and constructs are required on the road to Exascale.