Graciela Román-Alonso

Easing Message-Passing Parallel Programming Through a Data Balancing Service

The message passing model is now widely used for parallel computing, but is still difficult to use with some applications. The explicit data distribution or the explicit dynamic creation of parallel tasks can require a complex algorithm. In this paper, in order to avoid explicit data distribution, we propose a programming approach based on a data load balancing service for MPI-C. Using a parallel version of the merge sort algorithm, we show how our service avoids explicit data distribution completely, easing parallel programming. Some performance results are presented which compare our approach to a version of merge sort with explicit data distribution.

Reducing communication overhead under parallel list processing in multicore clusters

The Data List Management Library (DLML) processes data lists in parallel, balancing the workload transparently to programmers. Its first design was targeted at clusters of uniprocessor nodes, and based on multiprocess parallelism and on message-passing communication. This paper presents a multithreaded design of DLML aimed at clusters of multicore nodes to better capitalise on intra-node parallelism. On applications tested, MultiCore DLML runs twice as fast as DLML when message-passing communication is not excessive. Good performance was achieved only after addressing issues relating to MPI communication overhead, cache locality and memory consumption.

Pore networks subjected to variable connectivity and geometrical restrictions: A simulation employing a multicore system

Abstract Pore networks considering variable connectivity and geometrical restrictions among voids of assorted sizes are simulated using an 8-multicore computing system. The topology of the resulting networks is visualized in terms of the sizes and connectivity of the pores through color graphics. Results allow the calculation of percolation thresholds, correlation lengths among pores, fractal dimensions of percolation clusters, and conditional probabilities among connected pore sizes. Besides, it is possible to observe disconnected pore islands of different sizes, depending on the structural correlation among pores.

Load Balancing Algorithms with Partial Information Management for the DLML Library

Load balancing algorithms are an essential component of parallel computing reducing the response time of applications. Frequently, balancing algorithms have a centralized behavior requiring a lot of messages to operate, thus causing scalability problems. A solution to improve scalability is to define a decentralized algorithm, avoiding the generation of bottlenecks. DLML (Data List Management Library) is a tool that, in a transparent way, allows the parallel processing of data that are organized through a List. One drawback of this tool is the global bidding algorithm used to distribute the data (work) generated during the execution. In this paper two load balancing algorithms for DLML handling partial information are proposed. The first algorithm considers a logical Torus topology and the second one follows a Binary Tree topology for communications. Results show how the scalability of DLML was improved, using two clusters of 40 and 1024 processing units, and executing dynamic and static applications.

Model Checking for Integrating Dynamic Load Distribution into Parallel Applications

Many parallel applications running on a distributed memory cluster generate data dynamically to process during their execution. In this case it is possible that some cluster nodes become overloaded. To improve performance we can integrate a dynamic data distribution algorithm.The integration of a dynamic load distribution policy into an application must consider the correct programming of several synchronisation/communication points in order to avoid dead-lock or data lost problems. In this work we show how a Model checking technique can be used to verify formally and automatically whether an application along with a load distribution algorithm work properly.We first propose a model for a parallel application that uses a dynamic load distribution policy to transfer its generated data to other processors (when there are some processors that can help with data processing). In our model in particular we defined a cyclic distribution policy. We also propose a set of functioning properties that our model and all parallel application that uses dynamic load distribution must fulfill. Then we apply a formal verification technique using the model checker spin to ensure that such properties are satisfied. To show an application of our model we used the MPI tool to implement it and solve the N-Queens problem, where milliards of possible solutions (data)are generated and processed. We show some results obtained by using a 16 processors system.

A Graphical Environment for Development of MPI Applications

This paper presents GD-MPI: a Graphical environment for Development of parallel MPI applications. GD-MPI offers users a web browser-based GUI to graphically specify both: workflows that represent a set of Java-MPI processes and communication between these processes including group creation, point-to-point and collective communications. GD-MPI also runs such processes remotely.

A software architecture for parallel list processing on grids

The Data List Management Library (DLML) processes data lists in parallel, balancing the workload transparently to programmers. Programmers only need to organise data into a list, use DLML functions to insert and get data items, and specify the sequential function(s) to process each data item according to the application logic. The first design of DLML was targeted for use at a single cluster. This paper presents DLML-Grid, a software architecture for DLML to run in Grid environments composed of multiple distributed clusters. The architecture is hierarchical and tends to localise communication within clusters, thus reducing communication overhead. Using OpenVPN, we implemented a prototype version of DLML-Grid to gather empirical results on its performance using two clusters and two applications whose workload is static and dynamically generated. DLML-Grid performs much better than DLML overall.

A parallel approach for a non-rigid image registration algorithm

Currently, the non-rigid registration of medical images has become an important issue in medical image processing. This work deals with the MPI parallel implementation of a non-rigid algorithm to speed-up its performance in processing image sub-regions with an Evolutionary Algorithm (EA). We are parallelizing the EA and implementing a distributed version of a Divide and Conquer algorithm, using groups of processes. Results show the influence of the number of groups and the number of processors in the execution time. Our implementation offers a speed-up of 15.6 on a cluster of 15 PC’s decreasing the execution time from 39min to 2.5min.