Browsing by Subject "Optimal load balancing"

Now showing 1 - 3 of 3

Open Access
Fast optimal load balancing algorithms for 1D partitioning
(Academic Press, 2004) Pınar, A.; Aykanat, Cevdet
The one-dimensional decomposition of nonuniform workload arrays with optimal load balancing is investigated. The problem has been studied in the literature as the "chains-on-chains partitioning" problem. Despite the rich literature on exact algorithms, heuristics are still used in parallel computing community with the "hope" of good decompositions and the "myth" of exact algorithms being hard to implement and not runtime efficient. We show that exact algorithms yield significant improvements in load balance over heuristics with negligible overhead. Detailed pseudocodes of the proposed algorithms are provided for reproducibility. We start with a literature review and propose improvements and efficient implementation tips for these algorithms. We also introduce novel algorithms that are asymptotically and runtime efficient. Our experiments on sparse matrix and direct volume rendering datasets verify that balance can be significantly improved by using exact algorithms. The proposed exact algorithms are 100 times faster than a single sparse-matrix vector multiplication for 64-way decompositions on the average. We conclude that exact algorithms with proposed efficient implementations can effectively replace heuristics. © 2004 Elsevier Inc. All rights reserved.
Open Access
One-dimensional partitioning for heterogeneous systems: theory and practice
(Academic Press, 2008-11) Pınar, A.; Tabak, E. K.; Aykanat, Cevdet
We study the problem of one-dimensional partitioning of nonuniform workload arrays, with optimal load balancing for heterogeneous systems. We look at two cases: chain-on-chain partitioning, where the order of the processors is specified, and chain partitioning, where processor permutation is allowed. We present polynomial time algorithms to solve the chain-on-chain partitioning problem optimally, while we prove that the chain partitioning problem is NP-complete. Our empirical studies show that our proposed exact algorithms produce substantially better results than heuristics, while solution times remain comparable. © 2008 Elsevier Inc. All rights reserved.
Open Access
Sparse matrix decomposition with optimal load balancing
(IEEE, 1997-12) Pınar, Ali; Aykanat, Cevdet
Optimal load balancing in sparse matrix decomposition without disturbing the row/column ordering is investigated. Both asymptotically and run-time efficient exact algorithms are proposed and implemented for one-dimensional (1D) striping and two-dimensional (2D) jagged partitioning. Binary search method is successfully adopted to 1D striped decomposition by deriving and exploiting a good upper bound on the value of an optimal solution. A binary search algorithm is proposed for 2D jagged partitioning by introducing a new 2D probing scheme. A new iterative-refinement scheme is proposed for both 1D and 2D partitioning. Proposed algorithms are also space efficient since they only need the conventional compressed storage scheme for the given matrix, avoiding the need for a dense workload matrix in 2D decomposition. Experimental results on a wide set of test matrices show that considerably better decompositions can be obtained by using optimal load balancing algorithms instead of heuristics. Proposed algorithms are 100 times faster than a single sparse-matrix vector multiplication (SpMxV), in the 64-way 1D decompositions, on the overall average. Our jagged partitioning algorithms are only 60% slower than a single SpMxV computation in the 8×8-way 2D decompositions, on the overall average.