Data Partitioning Technique Research Articles

A hierarchical synchronization technique based on the EREC for robust transmission of H.263 bit stream

We propose an error-resilient transmission technique for the H.263 compatible video data stream, based on the data-partitioning technique. The proposed algorithm employs the bit rearrangement technique of the error-resilience entropy coding in each layer, providing unequal error protection against the channel errors, without requiring additional side information. In addition, we propose the recovery algorithm for the lost or erroneous motion vectors. The proposed algorithm is implemented, based on the H.263 standard, and evaluated through intensive computer simulation. The experimental results demonstrate that the proposed algorithm provides acceptable performance both subjectively and objectively at various bit error rates and burst lengths.

Performance analysis of cell discarding techniques for best effort video communications over ATM networks

With increasing interest in the transmission of audio–visual applications over ATM best effort services, efficient video-oriented control mechanisms for improving the video quality in the presence of loss have to be designed. In this paper, we propose and evaluate two new slice-based discard schemes for use with available bit rate and guaranteed frame rate services (e.g. formerly UBR+). The schemes adaptively and selectively adjust the discard level to switch buffer occupancy and video cell payload types. To improve their performance, we also introduce a dynamic frame-level priority data partition technique based on MPEG data structure and feedback from the network. To support these mechanisms, enhancements to the ATM adaptation layer 5 and a new MPEG-2 encapsulation strategy are also proposed. The presented quality of picture (QoP) control framework is evaluated using simulation and actual MPEG video data. The overall aim of the framework is double. First, ensuring a graceful picture quality degradation by minimizing cell loss probability for critical video data, and second optimizing the network effective throughput by reducing transmission of non useful data. In comparison to previous approaches, the performance evaluation have shown a significant reduction of the bad throughput and minimization of losses of intra- and predictive-coded frames at both cell and slice layers.

Data Partitioning for Parallel Spatial Join Processing

The cost of spatial join processing can be very high because of the large sizes of spatial objects and the computation-intensive spatial operations. While parallel processing seems a natural solution to this problem, it is not clear how spatial data can be partitioned for this purpose. Various spatial data partitioning methods are examined in this paper. A framework combining the data-partitioning techniques used by most parallel join algorithms in relational databases and the filter-and-refine strategy for spatial operation processing is proposed for parallel spatial join processing. Object duplication caused by multi-assignment in spatial data partitioning can result in extra CPU cost as well as extra communication cost. We find that the key to overcome this problem is to preserve spatial locality in task decomposition. In this paper we show that a near-optimal speedup can be achieved for parallel spatial join processing using our new algorithms.

Study on adaptive job assignment for multiprocessor implementation of MPEG2 video encoding

The high demand on computation performance by multimedia information processing such as digital video compression and decompression has made multiprocessor computation more and more popular. In this paper, we present our study on adaptive job assignment for multiprocessor implementation of a Motion Picture Expert Group 2 (MPEG2) video encoding algorithm. Data partitioning technique is used for job assignment to the processors in the multiprocessor environment to exploit the data structure adopted by the MPEG standard that divides a frame of a picture into macro blocks (MBs) which are processed independently during encoding. An adaptive data partitioning scheme is developed to cope with the inherent nonuniform spatial distribution of motion activities, such that the computation load distribution over processors is as uniform as possible, which helps improve the speedup of the whole multiprocessor system. Simulations with several video sequences have shown that, in comparison to its nonadaptive counterpart, the adaptive scheme can effectively improve the speedup of the multiprocessor system. In addition, the speedup scales well with the increase of the number of processors used in the computation.

A Systolic Algorithm for the Triangular Sylvester Equation in a Linear Array

Abstract The solution of the Sylvester equation. AX+XB=C. can be obtained byreducing it to triangular Schur form.Here we design systolic algorithms for solving this reduced equation in bidimensional arrays and in linear arrays. By using data partition techniques we obtain size independent algorithms in both cases.

Parallel ICCG on a hierarchical memory multiprocessor — Addressing the triangular solve bottleneck

The incomplete Cholesky conjugate gradient (ICCG) algorithm is a commonly used iterative method for solving large sparse systems of equations. In this paper, we study the parallel solution of sparse triangular systems of equations, the most difficult aspect of implementing the ICCG method on a multiprocessor. We focus on shared-memory multiprocessor architectures with deep memory hierarchies. On such architectures we find that previously proposed parallelization approaches result in little or no speedup. The reason is that these approaches cause significant increases in the amount of memory system traffic as compared to a sequential approach. Increases of as much as a factor of 10 on four processors were observed. In this paper we propose new techniques for limiting these increases, including data remappings to increase spatial locality, new processor synchronization techniques to decrease the use of auxiliary data structures, and data partitioning techniques to reduce the amount of interprocessor communication. With these techniques, memory system traffic is reduced to as little as one-sixth of its previous volume. The resulting speedups are greatly improved as well, although they are still much less than linear. We discuss the factors that limit further speedups. We present both simulation results and results of experiments on an SGI 4D/340 multiprocessor.

Optimal geometric algorithms for digitized images on fixed-size linear arrays and scan-line arrays

Linear arrays are characterized by a small communication bandwidth and a large communication diameter rendering them unsuited to the implementation of global computations. This paper presents efficient data movement and partitioning techniques to overcome several shortcomings of linear arrays. These techniques are used to derive optimal parallel algorithms for several geometric problems on n × n images using a fixed-size linear array with p processors, where 1 ≤ p ≤ n. O(n2/p) time solutions are presented for labeling connected image regions, computing the convex hull of each region, and computing nearest neighbors. Consequently, a linear array with n processors can solve several image problems in O(n) time which is the same time taken by a two dimensional mesh-connected computer with n2 processors. Limitations of linear arrays are analyzed by presenting a class of image problems which can be solved sequentially in O(n2) time, but require Ω(n2) time on a linear array, irrespective of the number of processors used and the partitioning of the input image among the processors. An alternate communication-efficient fixed-size organization with p processors is proposed to solve such problems in O(n2/p) time, for 1 ≤ p ≤ n.

Real-time optical laboratory solution of parabolic differential equations

An optical laboratory matrix-vector processor is used to solve parabolic differential equations (the transient diffusion equation with two space variables and time) by an explicit algorithm. This includes optical matrix-vector nonbase-2 encoded laboratory data, the combination of nonbase-2 and frequency-multiplexed data on such processors, a high-accuracy optical laboratory solution of a partial differential equation, new data partitioning techniques, and a discussion of a multiprocessor optical matrix-vector architecture.