Sparse matrices in matlab: design and implementation
SIAM Journal on Matrix Analysis and Applications
The primal-dual method for approximation algorithms and its application to network design problems
Approximation algorithms for NP-hard problems
Optimizing Sparse Matrix Computations for Register Reuse in SPARSITY
ICCS '01 Proceedings of the International Conference on Computational Sciences-Part I
Sparsity: Optimization Framework for Sparse Matrix Kernels
International Journal of High Performance Computing Applications
Memory hierarchy optimizations and performance bounds for sparse ATAx
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
Poster: I/O workload analysis with server-side data collection
Proceedings of the 2011 companion on High Performance Computing Networking, Storage and Analysis Companion
| Hi-index | 0.00 |
Sparse matrix computations arise in many scientific and engineering applications, but their performance is limited by the growing gap between processor and memory speed. In this paper, we present a case study of an important sparse matrix triple product problem that commonly arises in primal-dual optimization method. Instead of a generic two-phase algorithm, we devise and implement a single pass algorithm that exploits the block diagonal structure of the matrix. Our algorithm uses fewer floating point operations and roughly half the memory of the two-phase algorithm. The speed-up of the one-phase scheme over the two-phase scheme is 2.04 on a 900 MHz Intel Itanium-2, 1.63 on an 1 GHz Power-4, and 1.99 on a 900 MHz Sun Ultra-3.