Improving the scalability of parallel N-body applications with an event driven constraint based execution model

The scalability and efficiency of graph applications are significantly constrained by conventional systems and their supporting programming models. Technology trends like multicore, manycore, and heterogeneous system architectures are introducing further challenges and possibilities for emerging application domains such as graph applications. This paper explores the space of effective parallel execution of ephemeral graphs that are dynamically generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The workloads are expressed using the semantics of an Exascale computing execution model called ParalleX. For comparison, results using conventional execution model semantics are also presented. We find improved load balancing during runtime and automatic parallelism discovery improving efficiency using the advanced semantics for Exascale computing.Comment: 11 figure

Analysis, classification and comparison of scheduling techniques for software transactional memories

Transactional Memory (TM) is a practical programming paradigm for developing concurrent applications. Performance is a critical factor for TM implementations, and various studies demonstrated that specialised transaction/thread scheduling support is essential for implementing performance-effective TM systems. After one decade of research, this article reviews the wide variety of scheduling techniques proposed for Software Transactional Memories. Based on peculiarities and differences of the adopted scheduling strategies, we propose a classification of the existing techniques, and we discuss the specific characteristics of each technique. Also, we analyse the results of previous evaluation and comparison studies, and we present the results of a new experimental study encompassing techniques based on different scheduling strategies. Finally, we identify potential strengths and weaknesses of the different techniques, as well as the issues that require to be further investigated

Mining question-answer pairs from web forum: a survey of challenges and resolutions

Internet forums, which are also known as discussion boards, are popular web applications. Members of the board discuss issues and share ideas to form a community within the board, and as a result generate huge amount of content on different topics on daily basis. Interest in information extraction and knowledge discovery from such sources has been on the increase in the research community. A number of factors are limiting the potentiality of mining knowledge from forums. Lexical chasm or lexical gap that renders some Natural Language Processing techniques (NLP) less effective, Informal tone that creates noisy data, drifting of discussion topic that prevents focused mining and asynchronous issue that makes it difficult to establish post-reply relationship are some of the problems that need to be addressed. This survey introduces these challenges within the framework of question answering. The survey provides description of the problems; cites and explores useful publications to the reader for further examination; provides an overview of resolution strategies and findings relevant to the challenges

On the effect of exploiting gpus for a more Eco-sustainable lease of life

It has been estimated that about 2% of global carbon dioxide emissions can be attributed to IT systems. Green (or sustainable) computing refers to supporting business critical computing needs with the least possible amount of power. This phenomenon changes the priorities in the design of new software systems and in the way companies handle existing ones. In this paper, we present the results of a research project aimed to develop a migration strategy to give an existing software system a new and more eco-sustainable lease of life. We applied a strategy for migrating a subject system that performs intensive and massive computation to a target architecture based on a Graphics Processing Unit (GPU). We validated our solution on a system for path finding robot simulations. An analysis on execution time and energy consumption indicated that: (i) the execution time of the migrated system is less than the execution time of the original system; and (ii) the migrated system reduces energy waste, so suggesting that it is more eco-sustainable than its original version. Our findings improve the body of knowledge on the effect of using the GPU in green computing. © 2015 World Scientific Publishing Company

Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models

International audienceWe present a dual-view mixture model to cluster users based on their features and latent behavioral functions. Every component of the mixture model represents a probability density over a feature view for observed user attributes and a behavior view for latent behavioral functions that are indirectly observed through user actions or behaviors. Our task is to infer the groups of users as well as their latent behavioral functions. We also propose a non-parametric version based on a Dirichlet Process to automatically infer the number of clusters. We test the properties and performance of the model on a synthetic dataset that represents the participation of users in the threads of an online forum. Experiments show that dual-view models outperform single-view ones when one of the views lacks information

MPI-dot2dot: A Parallel Tool to Find DNA Tandem Repeats on Multicore Clusters

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Tandem Repeats (TRs) are segments that occur several times in a DNA sequence, and each copy is adjacent to other. In the last few years, TRs have gained significant attention as they are thought to be related with certain human diseases. Therefore, identifying and classifying TRs have become a highly important task in bioinformatics in order to analyze their disorders and relationships with illnesses. Dot2dot, a tool recently developed to find TRs, provides more accurate results than the previous state-of-the-art, but it requires a long execution time even when using multiple threads. This work presents MPI-dot2dot, a novel version of this tool that combines MPI and OpenMP so that it can be executed in a cluster of multicore nodes and thus reduces its execution time. The performance of this new parallel implementation has been tested using different real datasets. Depending on the characteristics of the input genomes, it is able to obtain the same biological results as Dot2dot but more than 100 times faster on a 16-node multicore cluster (384 cores). MPI-dot2dot is publicly available to download from https://sourceforge.net/projects/mpi-dot2dot.This work was supported by the Ministry of Science and Innovation of Spain (PID2019-104184RB-I00 / AEI / 10.13039/501100011033), and by Xunta de Galicia and FEDER funds (Centro de Investigación de Galicia accreditation 2019-2022 and Consolidation Program of Competitive Reference Groups, under Grants ED431G 2019/01 and ED431C 2021/30, respectively). The authors would like to thank the Galician Supercomputing Center (CESGA) for providing access to the Finis Terrae II supercomputer. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer NatureXunta de Galicia; ED431G 2019/01Xunta de Galicia; ED431C 2021/3

Exploiting Graphics Processing Units for Massively Parallel Multi-Dimensional Indexing

Department of Computer EngineeringScientific applications process truly large amounts of multi-dimensional datasets. To efficiently navigate such datasets, various multi-dimensional indexing structures, such as the R-tree, have been extensively studied for the past couple of decades. Since the GPU has emerged as a new cost-effective performance accelerator, now it is common to leverage the massive parallelism of the GPU in various applications such as medical image processing, computational chemistry, and particle physics. However, hierarchical multi-dimensional indexing structures are inherently not well suited for parallel processing because their irregular memory access patterns make it difficult to exploit massive parallelism. Moreover, recursive tree traversal often fails due to the small run-time stack and cache memory in the GPU. First, we propose Massively Parallel Three-phase Scanning (MPTS) R-tree traversal algorithm to avoid the irregular memory access patterns and recursive tree traversal so that the GPU can access tree nodes in a sequential manner. The experimental study shows that MPTS R-tree traversal algorithm consistently outperforms traditional recursive R-Tree search algorithm for multi-dimensional range query processing. Next, we focus on reducing the query response time and extending n-ary multi-dimensional indexing structures - R-tree, so that a large number of GPU threads cooperate to process a single query in parallel. Because the number of submitted concurrent queries in scientific data analysis applications is relatively smaller than that of enterprise database systems and ray tracing in computer graphics. Hence, we propose a novel variant of R-trees Massively Parallel Hilbert R-Tree (MPHR-Tree), which is designed for a novel parallel tree traversal algorithm Massively Parallel Restart Scanning (MPRS). The MPRS algorithm traverses the MPHR-Tree in mostly contiguous memory access patterns without recursion, which offers more chances to optimize the parallel SIMD algorithm. Our extensive experimental results show that the MPRS algorithm outperforms the other stackless tree traversal algorithms, which are designed for efficient ray tracing in computer graphics community. Furthermore, we develop query co-processing scheme that makes use of both the CPU and GPU. In this approach, we store the internal and leaf nodes of upper tree in CPU host memory and GPU device memory, respectively. We let the CPU traverse internal nodes because the conditional branches in hierarchical tree structures often cause a serious warp divergence problem in the GPU. For leaf nodes, the GPU scans a large number of leaf nodes in parallel based on the selection ratio of a given range query. It is well known that the GPU is superior to the CPU for parallel scanning. The experimental results show that our proposed multi-dimensional range query co-processing scheme improves the query response time by up to 12x and query throughput by up to 4x compared to the state-of-the-art GPU tree traversal algorithm.ope