Cristina Nicolescu

CPR: mixed task and data parallel scheduling for distributed systems

It is well-known that mixing task and data parallelism to solve large computational applications often yields better speedups compared to either applying pure task parallelism or pure data parallelism. Typically, the applications are modeled in terms of a dependence graph of coarse-grain data-parallel tasks, called a data-parallel task graph. In this paper we present a new compile-time heuristic, named Critical Path Reduction (CPR), for scheduling data-parallel task graphs. Experimental results based on graphs derived from real problems as well as synthetic graphs, show that CPR achieves higher speedup compared to other well-known existing scheduling algorithms, at the expense of some higher cost. These results are also confirmed by performance measurements of two real applications (i.e., complex matrix multiplication and Strassen matrix multiplication) running on a cluster of workstations.

A data and task parallel image processing environment

The paper presents a data and task parallel low-level image processing environment for distributed memory systems. Image processing operators are parallelized by data decomposition using algorithmic skeletons. Image processing applications are parallelized by task decomposition, based on the image application task graph. In this way, an image processing application can be parallelized both by data and task decomposition, and thus better speed-ups can be obtained. We validate our method on the multi-baseline stereo vision application.

EASY-PIPE - An "Easy to use" parallel image processing environment based on algorithmic skeletons

The paper presents an approach of using algorithmic skeletons for adding data parallelism to an image processing library. The method is used for parallelizing image processing applications composed of low-level image operators on a distributed memory system. In this way, a user who wants to parallelize an image processing application is not involved in the design and the implementation of parallel algorithms, but his only task is how to select for each low-level operator the appropriate skeleton to obtain the parallel version of the application. Example of the multibaseline stereo vision image processing aplication is given for reference.

Parallel Low-Level Image Processing on a Distributed-Memory System

The paper presents a method to integrate parallelism in the DIPLIB sequential image processing library. The library contains several framework functions for different types of operations. We parallelize the filter framework function (contains the neighborhood image processing operators). We validate our method by testing it with the geometric mean filter. Experiments on a cluster of workstations show linear speedup.

Distributed bucket processing: A paradigm embedded in a framework for the parallel processing of pixel sets

Large datasets, such as pixels and voxels in 2D and 3D images can usually be reduced during their processing to smaller subsets with less datapoints. Such subsets can be the objects in the image, features - edges or corners - or more general, regions of interest. For instance, the transformation from a set of datapoints representing an image, to one or more subsets of datapoints representing objects in the image, is due to a segmentation algorithm and may involve both the selection of datapoints as well as a change in datastructure. The massive number of pixels in the original image, points to a data parallel approach, whereas the processing of the various objects in the image is more suitable for task parallelism. In this paper we introduce a framework for parallel image processing and we focus on an array of buckets that can be distributed over a number of processors and that contains pointers to the data from the dataset. The benefit of this approach is that the processor activity remains focussed on the datapoints that need processing and, moreover, that the load can be distributed over many processors, even in a heterogeneous computer architecture. Although the method is generally applicable in the processing of sets, in this paper we obtain our examples from the domain of image processing. As this method yields speedups that are data dependent, we derived a run-time evaluation that is able to determine if the use of distributed buckets is beneficial.

A Data and Task Parallel Image Processing Environment

The paper presents a data and task parallel environment for parallelizing low-level image processing applications on distributed memory systems. Image processing operators are parallelized by data decomposition using algorithmic skeletons. At the application level we use task decomposition, based on the Image Application Task Graph. In this way, an image processing application can be parallelized both by data and task decomposition, and thus beter speed-ups can be obtained. The framework is implemented using C and MPI-Panda library and it can be easily ported to other distributed memory systems.

A data and task parallel image processing environment for distributed memory systems

The paper presents a data and task parallel low-level image processing environment for distributed memory systems. Image processing operators are parallelized by data decomposition using algorithmic skeletons. At the application level we use task decomposition, based on the Image Application Task Graph. In this way, an image processing application can be parallelized both by data and task decomposition, and thus better speed-ups can be obtained. We validate our method on the multi-baseline stereo vision application.

Framework to parallelize an image processing library on a distributed-memory system

This paper presents a framework to add data and task parallelism to a sequential image processing library. The library contains 3 modules, one for low-level operators, the second for intermediate-level operators and the third for high-level operators. We parallelize the low-level operators by data decomposition and we are working at adding task parallelism at the image processing application level. We validate our data parallel approach by testing it with the geometric mean filter and the multibaseline stereo vision algorithm. Experiments on a cluster of workstations show very good speedup.

Parallel Watershed Algorithm on Images from Cranial CT-Scans Using PVM and MPI on Distributed Memory System

In this paper we present a parallel implementation of an image segmentation transform - the watershed algorithm. The algorithm is implemented using the message passing paradigm, with both PVM and MPI and is adapted to a specific application of determining the volume of liquor from CT-scans. Our watershed algorithm is applied on 2D images from cranial CT-scans to extract the liquor parts from them. The evaluation on a distributed memory system is included.