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Scalable task-parallel SGD on Matrix Factorization in Multicore Architectures 29

Scalable Task-Parallel SGD on Matrix Factorization in Multic...

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29th IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Nishioka, Yusuke Taura, Kenjiro Univ Tokyo Grad Sch Informat Sci & Technol Tokyo Japan

ISBN: (纸本)9781467376846

Recommendation is an indispensable technique especially in e-commerce services such as Amazon or Netflix to provide more preferable items to users. Matrix factorization is a well-known algorithm for recommendation which estimates affinities between users and items solely based on ratings explicitly given by users. To handle the large amounts of data, stochastic gradient descent (SGD), which is an online loss minimization algorithm, can be applied to matrix factorization. SGD is an effective method in terms of both convergence speed and memory consumption, but is difficult to be parallelized due to its essential sequentiality. FPSGD by Zhuang et al. [15] is an existing parallel SGD method for matrix factorization by dividing the rating matrix into many small blocks. Threads work on blocks, so that they do not update the same rows or columns of the factor matrices. Because of this technique FPSGD achieves higher convergence speed than other existing methods. Still, as we demonstrate in this paper, FPSGD does not scale beyond 32 cores with 1.4GB Netflix dataset because assigning non-conflicting blocks to threads needs a lock operation. In this work, we propose an alternative approach of SGD for matrix factorization using task parallel programming model. As a result, we have successfully overcome the bottleneck of FPSGD and achieved higher scalability with 64 cores.

关键词： Recommender systems Matrix factorization Stochastic gradient descent task parallel model

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Fault Tolerant Implementation of Peer-to-Peer Distributed Iterative Algorithms

Fault Tolerant Implementation of Peer-to-Peer Distributed It...

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15th IEEE International Conference on Computational Science and Engineering (CSE) / 10th IEEE/IFIP International Conference on Embedded and Ubiquitous Computing (EUC)

作者： The Tung Nguyen El-Baz, Didier CNRS LAAS 7 Ave Colonel Roche F-31400 Toulouse France Univ Toulouse LAAS F-31400 Toulouse France

ISBN: (纸本)9781467351652;9780769549149

Fault tolerance issues related to the implementation of distributed iterative algorithms via the P2PDC peer-to-peer distributed computing environment are considered. P2PDC is a decentralized environment dedicated to task parallel applications. It has been designed more particularly for the solution of large scale numerical simulation problems via distributed iterative algorithms. The environment allows frequent and direct communications between peers i.e., machines. P2PDC is based on P2PSAP, a self-adaptive communication protocol. We present new functionalities of P2PDC aimed at making our environment more robust. An adaptive fault tolerance mechanism ensures the robustness of computation to cope with peer faults. We consider also fault tolerance from an algorithmic point of view: we concentrate in particular on distributed asynchronous iterative algorithms that can tolerate some message loss. A series of computational results is presented and analyzed for a numerical simulation problem.

关键词： distributed computing peer to peer computing fault tolerance task parallel model numerical simulation

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Distributed Iterative Solution of Numerical Simulation Problems on Infiniband and Ethernet Clusters via the P2PSAP Self-Adaptive Protocol

Distributed Iterative Solution of Numerical Simulation Probl...

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21st Euromicro International Conference on parallel, Distributed, and Network-Based Processing (PDP)

作者： Tembo, Serge Romaric The Tung Nguyen El-Baz, Didier CNRS LAAS 7 Ave Colonel Roche F-31400 Toulouse France Univ Toulouse 1 LAAS F-31400 Toulouse France

ISBN: (纸本)9780769549392;9781467353212

The distributed iterative solution of numerical simulation problems on Infiniband or Ethernet Clusters via the P2PDC environment is studied. The P2PDC decentralized environment is dedicated to task parallel applications. It has been designed for the solution of large scale numerical simulation problems via distributed iterative algorithms. The P2PDC environment is based on the P2PSAP self-adaptive communication protocol. New functionalities of the P2PSAP communication protocol aimed at using Infiniband clusters are presented. A series of computational results is presented and analyzed.

关键词： distributed computing cluster computing Infiniband micro-protocols task parallel model asynchronous algorithms

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Scalable Performance Predictions of Distributed Peer-to-Peer Applications

Scalable Performance Predictions of Distributed Peer-to-Peer...

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14th IEEE International Conference on High Performance Computing and Communications (HPCC) / IEEE 9th International Conference on Embedded Software and Systems (ICESS)

作者： Cornea, Bogdan Florin Bourgeois, Julien The Tung Nguyen El-Baz, Didier Univ Franche Comte FEMTO ST Inst DISC 1 Cours Leprince Ringuet F-25201 Montbeliard France CNRS LAAS F-31400 Toulouse France Univ De Toulouse LAAS F-31400 Toulouse France

ISBN: (纸本)9780769547497

Recently, a new environment for high performance peer-to-peer distributed computing was proposed. This environment, named P2PDC, addresses stable or volatile systems communicating in a decentralized manner using the self-adaptive protocol P2PSAP. P2PDC is devoted to task parallel applications like numerical simulation problems or optimization problems solved via parallel or distributed iterative algorithms. For distributed applications meant to run with P2PDC, a performance prediction tool named dPerf was proposed. dPerf combines static and dynamic analysis with trace-based simulation to provide scientist with information about the execution of their large scale numerical simulation applications. dPerf addresses real parallel and distributed numerical simulation and optimisation applications written in C, C++ or Fortran for P2PDC. This paper introduces an enhancement of the dPerf tool which provides scalable performance prediction results. Scaling is done with respect to (i) network configuration and (ii) number of peers. Scaling predictions based on network configuration is achieved through trace-based simulation, where various architectures can be studied. Scaling predictions based on the number of peers implies analyzing the communication topology and modifying trace files prior to simulation. We present experimental results obtained for the obstacle problem, a C/P2PDC implementation of the code used in mechanics and finance. Prediction for this application is computed under real conditions, with a reduced slowdown and by providing user with scalable results.

关键词： Scalability performance prediction static analysis dynamic analysis peer-to-peer computing high performance computing task parallel model numerical simulation

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Runtime systems for load balancing and fault tolerance on distributed systems

Runtime systems for load balancing and fault tolerance on di...

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作者： Arafat, Md Humayun The Ohio State University

学位级别：Ph.D.

Exascale computing creates many challenges for scientific applications in both hardware and software. There is a continuous need for adaption to new architectures. Load balancing and data distribution are major issues in increasingly large machines. In addition, fault tolerance has to be considered in every aspect of the system. In this dissertation, we make contributions to advance parallel computing, load balancing and fault tolerance in the context of scientific applications. The dynamical nucleation theory Monte Carlo (DNTMC) application from the NWChem computational chemistry suite utilizes a Markov chain Monte Carlo, two-level parallel structure, with periodic synchronization points that assemble the results of independent finer-grained calculations. Like many such applications, the existing code employs a static partitioning of processes into groups and assigns each group a piece of the finer-grained parallel calculation. A significant cause of performance degradation is load imbalance among groups since the time requirements of the inner-parallel calculation varies widely with the input problem and as a result of the Monte Carlo simulation. We present a novel approach to load balancing such calculations with minimal changes to the application. We introduce the concept of a resource sharing barrier (RSB) -- a barrier that allows process groups waiting on other processes' work to actively contribute to their completion. The next work presents an approach to accelerate task parallel computations using GPUs in the context of the Global Arrays parallel programming model. task parallelism is an efficient technique for expressing parallelism in irregular programs. We extend the Scioto task parallel scheduling framework to efficiently offload task execution to GPU accelerators. The execution of Scioto tasks on a GPU requires movement of data through three layers: the global address space, host memory, and device memory. We propose an automated, pipeline-based approa

关键词： Computer Science task parallel model CPU-GPU Fault Tolerance LoadBalancing

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