Browsing by Subject "Storage allocation (computer)"
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Item Open Access Adaptive decomposition and remapping algorithms for object-space-parallel direct volume rendering of unstructured grids(Academic Press, 2007-01) Aykanat, Cevdet; Cambazoglu, B. B.; Findik, F.; Kurc, T.Object space (OS) parallelization of an efficient direct volume rendering algorithm for unstructured grids on distributed-memory architectures is investigated. The adaptive OS decomposition problem is modeled as a graph partitioning (GP) problem using an efficient and highly accurate estimation scheme for view-dependent node and edge weighting. In the proposed model, minimizing the cutsize corresponds to minimizing the parallelization overhead due to the data communication and redundant computation/storage while maintaining the GP balance constraint corresponds to maintaining the computational load balance in parallel rendering. A GP-based, view-independent cell clustering scheme is introduced to induce more tractable view-dependent computational graphs for successive visualizations. As another contribution, a graph-theoretical remapping model is proposed as a solution to the general remapping problem and is used in minimization of the cell-data migration overhead. The remapping tool RM-MeTiS is developed by modifying the GP tool MeTiS and is used in partitioning the remapping graphs. Experiments are conducted using benchmark datasets on a 28-node PC cluster to evaluate the performance of the proposed models. © 2006 Elsevier Inc. All rights reserved.Item Open Access A decoupled local memory allocator(Association for Computing Machinery, 2013) Diouf, B.; Hantaş, C.; Cohen, A.; Özturk, Ö.; Palsberg, J.Compilers use software-controlled local memories to provide fast, predictable, and power-efficient access to critical data. We show that the local memory allocation for straight-line, or linearized programs is equivalent to a weighted interval-graph coloring problem. This problem is new when allowing a color interval to "wrap around," and we call it the submarine-building problem. This graph-theoretical decision problem differs slightly from the classical ship-building problem, and exhibits very interesting and unusual complexity properties. We demonstrate that the submarine-building problem is NP-complete, while it is solvable in linear time for not-so-proper interval graphs, an extension of the the class of proper interval graphs. We propose a clustering heuristic to approximate any interval graph into a not-so-proper interval graph, decoupling spill code generation from local memory assignment. We apply this heuristic to a large number of randomly generated interval graphs reproducing the statistical features of standard local memory allocation benchmarks, comparing with state-of-the-art heuristics. © 2013 ACM.Item Open Access Sparse matrix decomposition with optimal load balancing(IEEE, 1997-12) Pınar, Ali; Aykanat, CevdetOptimal 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.