Parallel Branch-and-Bound in Multi-core Multi-CPU Multi-GPU Heterogeneous Environments

International audienceWe investigate the design of parallel B&B in large scale heterogeneous compute environments where processing units can be composed of a mixture of multiple shared memory cores, multiple distributed CPUs and multiple GPUs devices. We describe two approaches addressing the critical issue of how to map B&B workload with the different levels of parallelism exposed by the target compute platform. We also contribute a throughout large scale experimental study which allows us to derive a comprehensive and fair analysis of the proposed approaches under different system configurations using up to 16 GPUs and up to 512 CPU-cores. Our results shed more light on the main challenges one has to face when tackling B&B algorithms while describing efficient techniques to address them. In particular, we are able to obtain linear speed-ups at moderate scales where adaptive load balancing among the heterogeneous compute resources is shown to have a significant impact on performance. At the largest scales, intra-node parallelism and hybrid decentralized load balancing is shown to have a crucial importance in order to alleviate locking issues among shared memory threads and to scale the distributed resources while optimizing communication costs and minimizing idle time

A Lightweight Continuous Jobs Mechanism for MapReduce Frameworks

International audienceMapReduce is a programming model which allows the processing of vast amounts of data in parallel, on a large number of machines. It is particularly well suited to static or slow changing set of data since the execution time of a job is usually high. However, in practice data-centers collect data at fast rates which makes it very difficult to maintain up-to-date results. To address this challenge, we propose in this paper a generic mechanism for dealing with dynamic data in MapReduce frameworks. Long-standing MapReduce jobs, called continuous Jobs, are automatically re-executed to process new incoming data at a minimum cost. We present a simple and clean API which integrates nicely with the standard MapReduce model. Furthermore, we describe cHadoop, an implementation of our approach based on Hadoop which does not require modifications to the source code of the original framework. Thus, cHadoop can quickly be ported to any new version of Hadoop. We evaluate our proposal with two standard MapReduce applications (WordCount and WordCount-N-Count), and one real world application (RDF Query) on real datasets. Our evaluations on clusters ranging from 5 to 40 nodes demonstrate the benefit of our approach in terms of execution time and ease of use

Adaptive Dynamic Load Balancing in Heterogenous Multiple GPUs-CPUs Distributed Setting: Case Study of B&B Tree Search

International audienceThe emergence of new hybrid and heterogenous multi-GPU multi-CPU large scale platforms offers new opportunities and pauses new challenges when solving difficult optimization problems. This paper targets irregular tree search algorithms in which workload is unpredictable. We propose an adaptive distributed approach allowing to distribute the load dynamically at runtime while taking into account the computing abilities of either GPUs or CPUs. Using Branch-and-Bound and Flowshop as a case study, we deployed our approach using up to 20 GPUs jointly to up to 128 CPUs. Through extensive experiments in different system configurations, we report near optimal speedups, thus providing new insights into how to take full advantage of both GPUs and CPUs power in modern computing platforms

Overlay-Centric Load Balancing: Applications to UTS and B&B

International audienceTo deal with dynamic load balancing in large scale distributed systems, we propose to organize computing resources following a logical peer-to-peer overlay and to distribute the load according to the so-defined overlay. We use a tree as a logical structure connecting distributed nodes and we balance the load according to the size of induced subtrees. We conduct extensive experiments involving up to 1000 computing cores and provide a throughout analysis of different properties of our generic approach for two different applications, namely, the standard Unbalanced Tree Search and the more challenging parallel Branch-and-Bound algorithm. Substantial improvements are reported in comparison with the classical random work stealing and two finely tuned application specific strategies taken from the literature

A Combination of Artificial Neural Network and Artificial Immune System for Virus Detection

In this paper, we propose an Artificial Neural Immune Network (ANIN) for virus detection. ANIN is a combination of Artificial Neural Network (ANN) and Artificial Immune Network (AiNet). In ANIN, each ANN is considered as a detector. A pool of initial detectors then undergoes a mature process, called AiNet, to improve its recognizing ability. Thus, more than one ANN objects can cooperate to detect malicious code. The experimental results show that ANIN can achieve a detection rate of 87.98% on average with an acceptable false positive rate

Experiment and FEM Modelling of Bond Behaviors between Pre-stressing Strands and Ultra–High–Performance Concrete

The objective of this paper is to investigate the bond properties of prestressing strands embedded in Ultra–High–Performance Concrete (UHPC).The UHPC was made in laboratory using local materials in Vietnam.Its mixture contains: silica aggregates, portland cement PC40, fly ash, silica fume, polycarboxylate superplasticizer and the micro steel fibers.The experimental process is realized on a pull-out test. The volume fraction of micro steel fibers in UHPC was 2%. The prestressing strand with diameters of 15.2mm was considered. The interface shear strength between strand and UHPC is identified based on the results of force and displacement obtained during the pull-out test. The Cohesive Zone Model (CZM) is implemented in finite element model to study this interface behavior. This model described by a piecewise linear elastic law. The CZM’s parameters are identified based on experimental results of pull-out test.The numerical studies are used the CZM in ANSYS software. Two numerical tests are realized and compared with experimental results: pull-out test and other test to verify the deflection of I girder due to prestressing force

Experiment and FEM Modelling of Bond Behaviors between Pre-stressing Strands and Ultra–High–Performance Concrete

The objective of this paper is to investigate the bond properties of prestressing strands embedded in Ultra–High–Performance Concrete (UHPC).The UHPC was made in laboratory using local materials in Vietnam.Its mixture contains: silica aggregates, portland cement PC40, fly ash, silica fume, polycarboxylate superplasticizer and the micro steel fibers.The experimental process is realized on a pull-out test. The volume fraction of micro steel fibers in UHPC was 2%. The prestressing strand with diameters of 15.2mm was considered. The interface shear strength between strand and UHPC is identified based on the results of force and displacement obtained during the pull-out test. The Cohesive Zone Model (CZM) is implemented in finite element model to study this interface behavior. This model described by a piecewise linear elastic law. The CZM’s parameters are identified based on experimental results of pull-out test.The numerical studies are used the CZM in ANSYS software. Two numerical tests are realized and compared with experimental results: pull-out test and other test to verify the deflection of I girder due to prestressing force