Browsing by Subject "Parallel Computing"

Now showing 1 - 4 of 4

Open Access
Efficient fast hartley transform algorithms for hypercube-connected multicomputers
(IEEE, 1995) Aykanat, Cevdet; Derviş, A.
Although fast Hartley transform (FHT) provides efficient spectral analysis of real discrete signals, the literature that addresses the parallelization of FHT is extremely rare. FHT is a real transformation and does not necessitate any complex arithmetics. On the other hand, FHT algorithm has an irregular computational structure which makes efficient parallelization harder. In this paper, we propose a efficient restructuring for the sequential FHT algorithm which brings regularity and symmetry to the computational structure of the FHT. Then, we propose an efficient parallel FHT algorithm for medium-to-coarse grain hypercube multicomputers by introducing a dynamic mapping scheme for the restructured FHT. The proposed parallel algorithm achieves perfect load-balance, minimizes both the number and volume of concurrent communications, allows only nearest-neighbor communications and achieves in-place computation and communication. The proposed algorithm is implemented on a 32-node iPSC/21 hypercube multicomputer. High-efficiency values are obtained even for small size FHT problems. © 1995 IEEE
Open Access
Model-driven approach for supporting the mapping of parallel algorithms to parallel computing platforms
(Springer, Berlin, Heidelberg, 2013) Arkin, E.; Tekinerdogan, Bedir; Imre, K.M.
The trend from single processor to parallel computer architectures has increased the importance of parallel computing. To support parallel computing it is important to map parallel algorithms to a computing platform that consists of multiple parallel processing nodes. In general different alternative mappings can be defined that perform differently with respect to the quality requirements for power consumption, efficiency and memory usage. The mapping process can be carried out manually for platforms with a limited number of processing nodes. However, for exascale computing in which hundreds of thousands of processing nodes are applied, the mapping process soon becomes intractable. To assist the parallel computing engineer we provide a model-driven approach to analyze, model, and select feasible mappings. We describe the developed toolset that implements the corresponding approach together with the required metamodels and model transformations. We illustrate our approach for the well-known complete exchange algorithm in parallel computing. © 2013 Springer-Verlag.
Open Access
Parallel processing for progressive refinement radiosity
(1993) Çapın, Tolga K.
Progressive refinement radiosity is an increasingly popular method for realistic image synthesis of non-existing environments. The method successfully approximates the light distribution in an environment, however it requires excessive amount of computation. In this thesis, the progressive refinement method is investigated for parallelization on ring and hypercube-connected multicomputers. Two different approaches for parallelization, based on synchronous parallelism witli static task assignment, are proposed, in order to achieve better coherence in parallel light distributions and obtain good performance on simple topologies. Efficient global circulation schemes are proposed in order to decrease the total volume of communication by asymptotical factors. The first scheme for parallelization is a modification of the sequential algorithm in that several patches shoot their energy at a time, while the second scheme is based on the parallelism level of one shooting patch at a time. The proposed parallel algorithms are evaluated theoretically and implemented for ring and hypercube-connected topologies on Intel’s iPSC72 multicomputer. Load balance quality of the proposed schemes are evaluated experimentally
Open Access
Parallel streaming graph partitioning utilizing multilevel framework
(2018-08) Jafari, Nazanin
Graph partitioning is widely used for e cient parallelization of a variety of applications. Streaming graph partitioning is a one pass partitioning solution provided to overcome high computation costs of o ine graph partitioners. Even though these streaming algorithms can be used for successively repartitioning, aiming at further improvements in partitioning qualities, quality improvements is limited to few passes that make o ine graph partitioning tools still a desirable solution for graph partitioning due to the generated high quality partitions. We propose a multilevel approach using streaming algorithms that can alleviate tradeo between quality and performance in graph partitioning problem. Moreover, our OpenMP based multi-threaded implementation, can generate fast and highly scalable solutions compared to mt-metis, a multi-threaded solution for METIS, the state-of-the-art o ine high quality graph partitioning tool. Our results show that our method can produce up to fteen times faster and more scalable results in large graph datasets. We also show that our method can improve quality of partitions signi cantly compared to state-of-the-art streaming graph partitioning algorithm LDG after repartitioning several times. On average we produce partitions with 29% better qualities than LDG algorithm.