Browsing by Subject "Vectorization"

Now showing 1 - 5 of 5

Open Access
Efficient vectorization of forward/backward substitutions in solving sparse linear equations
(IEEE, 1994) Aykanat, Cevdet; Özgü, Özlem; Güven, N.
Vector processors have promised an enormous increase in computing speed for computationally intensive and time-critical power system problems which require the repeated solution of sparse linear equations. Due to short vectors processed in these applications, standard sparsity-based algorithms need to be restructured for efficient vectorization. This paper presents a novel data storage scheme and an efficient vectorization algorithm that exploits the intrinsic architectural features of vector computers such as sectioning and chaining. As the benchmark, the solution phase of the Fast Decoupled Load Flow algorithm is used in simulations. The relative performances of the proposed and existing vectorization schemes are evaluated, both theoretically and experimentally, on IBM 3090/VF.
Open Access
Kadastral harita vektörlemesi için sanal çizgi doğrulama yönteminin dal ve açı kontrolü ile iyileştirmesi
(IEEE, 2012-04) Balkoca, A.; Yergök, Ahsen İkbal; İlk, H. G.
Bu çalışmada balastro noktası bulunduran kadastral haritaların (pafta) vektörleme işlemi için daha önceki çalışmada geliştirilen siyah piksel oranı ile sanal çizgi doğrulaması yöntemi için bir iyileştirme önerilmiştir. Bu yöntemde ada/parsel alanlarını tespit etmek için ada/parsel köşe noktalarını oluşturan balastro noktaları arasında yapılan çizgi kontrolü, balastro noktalarından çıkan çizgilerin (dal) doğrultularının ve açılarının bulunmasıyla daha verimli ve hatasız hale getirilmiştir. Doğrultu ve açı kontrolü sayesinde balastro noktası için sadece kendisinden çıkan dalların bulunduğu açı aralığındaki aday balastro noktaları arasında sanal çizgi doğrulaması yapılması sağlanmıştır. Bu sayede işlem hızlı artarken yanlış yapılan bağlantıların sayısı en aza indirilmiştir. Balastro noktalarını topolojik olarak bağlayan çizgilerin bulunma başarımı ortalaması %79.16’dır.
Open Access
Kadastral haritalarin görüntü işleme teknikleri kullanilarak vektörizasyonu
(IEEE, 2011-04) Balkoca, A.; Yergök, Ahsen İkbal; Yücekaya, S.
This study aims vectorizing cadastral maps by using image processing techniques. Firstly, landing marks on cadastral maps are found by Moore Neighbor Tracing method then the connections are extracted and connecting lines are found. The system extracted about 80%-90% of the components that user should manually draw which is discussed in Section 4. © 2011 IEEE.
Open Access
Parallel sparse matrix vector multiplication techniques for shared memory architectures
(2014) Başaran, Mehmet
SpMxV (Sparse matrix vector multiplication) is a kernel operation in linear solvers in which a sparse matrix is multiplied with a dense vector repeatedly. Due to random memory access patterns exhibited by SpMxV operation, hardware components such as prefetchers, CPU caches, and built in SIMD units are under-utilized. Consequently, limiting parallelization efficieny. In this study we developed; • an adaptive runtime scheduling and load balancing algorithms for shared memory systems, • a hybrid storage format to help effectively vectorize sub-matrices, • an algorithm to extract proposed hybrid sub-matrix storage format. Implemented techniques are designed to be used by both hypergraph partitioning powered and spontaneous SpMxV operations. Tests are carried out on Knights Corner (KNC) coprocessor which is an x86 based many-core architecture employing NoC (network on chip) communication subsystem. However, proposed techniques can also be implemented for GPUs (graphical processing units).
Open Access
Vectorization and parallelization of the conjugate gradient algorithm on hypercube-connected vector processors
(Elsevier, 1990) Aykanat, Cevdet; Özgüner, F.; Scott, D. S.
Solution of large sparse linear systems of equations in the form constitutes a significant amount of the computations in the simulation of physical phenomena [1]. For example, the finite element discretization of a regular domain, with proper ordering of the variables x, renders a banded N × N coefficient matrix A. The Conjugate Gradient (CG) [2,3] algorithm is an iterative method for solving sparse matrix equations and is widely used because of its convergence properties. In this paper an implementation of the Conjugate Gradient algorithm, that exploits both vectorization and parallelization on a 2-dimensional hypercube with vector processors at each node (iPSC-VX/d2), is described. The implementation described here achieves efficient parallelization by using a version of the CG algorithm suitable for coarse grain parallelism [4,5] to reduce the communication steps required and by overlapping the computations on the vector processor with internode communication. With parallelization and vectorization, a speedup of 58 over a μVax II is obtained for large problems, on a two dimensional vector hypercube (iPSC-VX/d2).