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FANG: Fast and Efficient Successor-State Generation for Heuristic Optimization on GPUs 19th

FANG: Fast and Efficient Successor-State Generation for Heur...

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19th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Koester, Marcel Gross, Julian Krueger, Antonio Saarland Informat Campus Campus D3-2 D-66123 Saarbrucken Germany

ISBN: (纸本)9783030389918;9783030389901

Many optimization problems (especially nonsmooth ones) are typically solved by genetic, evolutionary, or metaheuristic-based algorithms. However, these genetic approaches and other related papers typically assume the existence of a neighborhood or successor-state function N(x), where x is a candidate state. the implementation of such a function can become arbitrarily complex in the field of combinatorial optimization. Many N(x) functions for a huge variety of different domainspecific problems have been developed in the past to solve this general problem. However, it has always been a great challenge to port or realize these functions on a massively-parallel architecture like a Graphics processing Unit (GPU). We present a GPU-based method called FANG that implements a generic and reusable N(x) for arbitrary domains in the field of combinatorial optimization. It can be customized to satisfy domainspecific requirements and leverages the underlying hardware in a fast and efficient way by construction. Moreover, our method has a high scalability with respect to the number of input states and the complexity of a single state. Measurements show significant performance improvements compared to traditional exploration approaches leveraging the CPU on our evaluation scenarios.

关键词： Heuristic search Combinatorial optimization Successor-state generation Neighborhood exploration Massively-parallel processing Graphics processing units GPUs

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Automated parallel Simulation of Heart Electrical Activity Using Finite Element Method 16th

Automated Parallel Simulation of Heart Electrical Activity U...

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16th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Sozykin, Andrey Epanchintsev, Timofei Zverev, Vladimir Khamzin, Svyatoslav Bersenev, Aleksandr Krasovskii Inst Math & Mech Ekaterinburg Russia Inst Immunol & Physiol UrB RAS Ekaterinburg Russia Ural Fed Univ Ekaterinburg Russia

ISBN: (纸本)9783319499567;9783319499550

In this paper we present an approach to the parallel simulation of the heart electrical activity using the finite element method with the help of the FEniCS automated scientific computing framework. FEniCS allows scientific software development using the near-mathematical notation and provides automatic parallelization on MPI clusters. We implemented the ten Tusscher-Panfilov (TP06) cell model of cardiac electrical activity. the scalability testing of the implementation was performed using up to 240 CPU cores and the 95 times speedup was achieved. We evaluated various combinations of the Krylov parallel linear solvers and the preconditioners available in FEniCS. the best performance was provided by the conjugate gradient method and the biconjugate gradient stabilized method solvers with the successive over-relaxation preconditioner. Since the FEniCS-based implementation of TP06 model uses notation close to the mathematical one, it can be utilized by computational mathematicians, biophysicists, and other researchers without extensive parallel computing skills.

关键词： Heart simulation Finite element method Scalability Krylov subspace methods FEniCS parallel computing

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parallel processing algorithms for the Vehicle Routing Problem and Its Variants: A Literature Review with a Look into the Future 20th

Parallel Processing Algorithms for the Vehicle Routing Probl...

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20th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Rabbouch, Bochra Rabbouch, Hana Saadaoui, Foued Zone Industrielle Manzel Hayet LEONI Wiring Syst Monastir 5033 Tunisia Univ Monastir Fac Sci Lab LR18ES15 Algebre Theorie Nombres & Anal Nonli Monastir 5019 Tunisia King Abdulaziz Univ Fac Sci Dept Stat POB 80203 Jeddah 21589 Saudi Arabia

ISBN: (纸本)9783030602451;9783030602444

Vehicle Routing Problems (VRPs) are well-know combinatorial optimization problems used to design an optimal route for a fleet of vehicles to service a set of customers under a number of constraints. Due to their NP-hard complexity, a number of purely computational techniques have been proposed in recent years in order to solve them. Among these techniques, nature-inspired algorithms have proven their effectiveness in terms of accuracy and convergence speed. Some of these methods are also designed in such a way to decompose the basic problem into a number of sub-problems which are subsequently solved in parallel computing environments. It is therefore the purpose of this paper to review the fresh corpus of the literature dealing with the main approaches proposed over the past few years to solve combinatorial optimization problems in general and, in particular, the VRP and its different variants. Bibliometric and review studies are conducted with a special attention paid to metaheuristic strategies involving procedures with parallel architectures. the obtained results show an expansion of the use of parallel algorithms for solving various VRPs. Nevertheless, the regression in the number of citations in this framework proves that the interest of the research community has declined somewhat in recent years. this decline may be explained by the lack of rigorous mathematical results and practical interfaces under famous calculation softwares.

关键词： Combinatorial optimization Vehicle Routing Problem Metaheuristics Nature-inspired algorithms parallel computing

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GPU-Based Heterogeneous Coding Architecture for HEVC 16th

GPU-Based Heterogeneous Coding Architecture for HEVC

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16th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Cebrian-Marquez, Gabriel Migallon, Hector Luis Martinez, Jose Lopez-Granado, Otoniel Pinol, Pablo Cuenca, Pedro Univ Castilla La Mancha Albacete Res Inst Informat I3A Plz Univ 2 Albacete 02071 Spain Miguel Hernandez Univ Dept Phys & Comp Architecture Elche 03202 Spain

ISBN: (纸本)9783319495835;9783319495828

the High Efficiency Video Coding (HEVC) standard has nearly doubled the compression efficiency of prior standards. Nonetheless, this increase in coding efficiency involves a notably higher computing complexity that should be overcome in order to achieve real-time encoding. For this reason, this paper focuses on applying parallel processing techniques to the HEVC encoder with the aim of reducing significantly its computational cost without affecting the compression performance. Firstly, we propose a coarse-grained slice-based parallelization technique that is executed in a multi-core CPU, and then, with finer level of parallelism, a GPU-based motion estimation algorithm. Both techniques define a heterogeneous parallel coding architecture for HEVC. Results show that speed-ups of up to 4.06x can be obtained on a quad-core platform with low impact in coding performance.

关键词： H.265 HEVC Heterogeneous parallel encoding GPU

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the Co-design of Astrophysical Code for Massively parallel Supercomputers 16th

The Co-design of Astrophysical Code for Massively Parallel S...

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16th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Glinsky, Boris Kulikov, Igor Chernykh, Igor Weins, Dmitry Snytnikov, Alexey Nenashev, Vladislav Andreev, Andrey Egunov, Vitaly Kharkov, Egor Inst Computat Math & Math Geophys SB RAS Lavrentjeva Ave 6 Novosibirsk 630090 Russia Novosibirsk State Tech Univ Novosibirsk 630073 Russia Volgograd State Tech Univ Volgograd 400005 Russia

ISBN: (纸本)9783319499567;9783319499550

the rapid growth of supercomputer technologies became a driver for the development of natural sciences. Most of the discoveries in astronomy, in physics of elementary particles, in the design of new materials in the DNA research are connected with numerical simulation and with supercomputers. Supercomputer simulation became an important tool for the processing of the great volume of the observation and experimental data accumulated by the mankind. Modern scientific challenges put the actuality of the works in computer systems and in the scientific software design to the highest level. the architecture of the future exascale systems is still being discussed. Nevertheless, it is necessary to develop the algorithms and software for such systems right now. It is necessary to develop software that is capable of using tens and hundreds of thousands of processors and of transmitting and storing of large volumes of data. In the present work the technology for the development of such algorithms and software is proposed. As an example of the use of the technology, the process of the software development is considered for some problems of astrophysics.

关键词： Exascale systems Co-design High performance computing Computational astrophysics Physics of plasmas

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international Workshops and Symposiums on algorithms and architectures for parallel processing, ica3pp 2015

International Workshops and Symposiums on Algorithms and Arc...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

ISBN: (纸本)9783319271606

the proceedings contain 83 papers. the special focus in this conference is on Trust, Security and Privacy for Big Data. the topics include: Nth-Order Multifunction Filter Employing Current Differencing Transconductance Amplifiers;An Efficient Spatial Query processing Algorithm in Multi-sink Directional Sensor Network;An Improved Method for Reversible Data Hiding in Encrypted Image;Study on Personalized Location Privacy Preservation algorithms Based on Road Networks;A Hierarchical Identity-Based Signature from Composite Order Bilinear Groups;Towards Returning Data Control to Cloud Users;A Distributed Algorithm for Graph Edge Partitioning;Scheduling Stochastic Tasks with Precedence Constrain on Cluster Systems with Heterogenous Communication Architecture;An Output-Oriented Approach of Test Data Generation Based on Genetic Algorithm;An Efficient Pre-filter to Accelerate Regular Expression Matching;A Hybrid Optimization Approach for Anonymizing Transactional Data;Program Obfuscator for Privacy-Carrying Unidirectional One-hop Re-encryption;Predicting Severity of Software Vulnerability Based on Grey System theory;Characterization of Android Applications with Root Exploit by Using Static Feature Analysis;A Lightweighted Fine-Grained Privacy-Preserving Profile Matching Mechanism for Mobile Social Networks in Proximity;Context-Aware QoS Assurance for Smart Grid Big Data processing with Elastic Cloud Resource Reconfiguration;Continuous User Identity Verification for Trusted Operators in Control Rooms;Leakage-Resilient Anonymous Identity-Based Broadcast Encryption in the Standard Model;Scheduling Resource of IaaS Clouds for Energy Saving Based on Predicting the Overloading Status of Physical Machines and Towards Mechanised Semantics of HPC.

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A list scheduling algorithm for DAG-based parallel computing models 15th

A list scheduling algorithm for DAG-based parallel computing...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Fu, Hao Yu, Ce Sun, Jizhou Wang, Mengmeng Du, Jun School of Computer Science and Technology Tianjin University Tianjin300072 China

ISBN: (纸本)9783319271217

Task scheduling on multiprocessor system is a well-known problem in area of parallel computing. For this problem, many static scheduling algorithms have been reported. But in most static algorithms, only one attribute of tasks is considered when constructing a ready list, which consists of all ready tasks, and there is no evaluation for different task attributes. In this paper, a list scheduling algorithm for DAG-based parallel computing models is proposed. It is mainly designed for reducing the scheduling length of applications with regular DAG models. Eight task attributes in the DAG model are evaluated, and corresponding rules are presented, which will be used in constructing the ready list. And when scheduling tasks, its start time and communication cost on idle processors are taken into consideration. Experimental results show that the proposed algorithm can achieve a significant performance improvement, which is up to 142 %. © Springer international Publishing Switzerland 2015.

关键词： Scheduling algorithms

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Task parallel implementation of matrix multiplication on multi-socket multi-core architectures 15th

Task parallel implementation of matrix multiplication on mul...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Wang, Yizhuo Ji, Weixing Chen, Xu Hu, Sensen School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9783319271361

Matrix multiplication is a very important computation kernel in many science and engineering applications. this paper presents a parallel implementation framework for dense matrix multiplication on multi-socket multi-core architectures. Our framework first partitions the computation between the multi-core processors. then a hybrid matrix multiplication algorithm is used on each processor, which combines the Winograd algorithm and the classical algorithm. In addition, a hierarchical work-stealing scheme is applied to achieve dynamic load balancing and enforce data locality in our framework. Performance experiments on two platforms show that our implementation gets significant performance gains compared with the state-of-the-art implementations. © Springer international Publishing Switzerland 2015.

关键词： Matrix algebra

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TAMM: A New Topology-Aware Mapping Method for parallel Applications on the Tianhe-2A Supercomputer 18th

TAMM: A New Topology-Aware Mapping Method for Parallel Appli...

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18th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Chen, Xinhai Liu, Jie Li, Shengguo Xie, Peizhen Chi, Lihua Wang, Qinglin Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Hunan Inst Traff Engn Inst Adv Sci & Technol Hengyang 421001 Peoples R China

ISBN: (纸本)9783030050511;9783030050504

With the increasing size of high performance computing systems, the expensive communication overhead between processors has become a key factor leading to the performance bottleneck. However, default process-to-processor mapping strategies do not take into account the topology of the interconnection network, and thus the distance spanned by communication messages may be particularly far. In order to enhance the communication locality, we propose a new topology-aware mapping method called TAMM. By generating an accurate description of the communication pattern and network topology, TAMM employs a two-step optimization strategy to obtain an efficient mapping solution for various parallel applications. this strategy first extracts an appropriate subset of all idle computing resources on the underlying system and then constructs an optimized one-to-one mapping with a refined iterative algorithm. Experimental results demonstrate that TAMM can effectively improve the communication performance on the Tianhe-2A supercomputer.

关键词： High performance computing systems Topology-aware mapping Communication pattern Network topology

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More Effective Distributed Deep Learning Using Staleness Based Parameter Updating 18th

More Effective Distributed Deep Learning Using Staleness Bas...

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18th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Ye, Yan Chen, Mengqiang Yan, Zijie Wu, Weigang Xiao, Nong Sun Yet Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Guangdong Prov Key Lab Big Data Anal & Proc Guangzhou Peoples R China MoE Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9783030050542;9783030050535

Deep learning technology has been widely applied for various purposes, especially big data analysis. However, computation required for deep learning is getting more complex and larger. In order to accelerate the training of large-scale deep networks, various distributed parallel training protocols have been proposed. In this paper, we design a novel asynchronous training protocol, Weighted Asynchronous parallel (WASP), to update neural network parameters in a more effective way. the core of WASP is "gradient staleness", a parameter version number based metric to weight gradients and reduce the influence of the stale parameters. Moreover, by periodic forced synchronization of parameters, WASP combines the advantages of synchronous and asynchronous training models and can speed up training with a rapid convergence rate. We conduct experiments using two classical convolutional neural networks, LeNet-5 and ResNet-101, at the Tianhe-2 supercomputing system, and the results show that, WASP can achieve much higher acceleration than existing asynchronous parallel training protocols.

关键词： Distributed deep learning parallel computing Parameter server Asynchronous parallel Supercomputing system

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