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275
An Extensible MetaLearning Approach for Scalable and Accurate Inductive Learning
, 1996
"... Much of the research in inductive learning concentrates on problems with relatively small amounts of data. With the coming age of ubiquitous network computing, it is likely that orders of magnitude more data in databases will be available for various learning problems of real world importance. Som ..."
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Cited by 48 (8 self)
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Much of the research in inductive learning concentrates on problems with relatively small amounts of data. With the coming age of ubiquitous network computing, it is likely that orders of magnitude more data in databases will be available for various learning problems of real world importance. Some learning algorithms assume that the entire data set fits into main memory, which is not feasible for massive amounts of data, especially for applications in data mining. One approach to handling a large data set is to partition the data set into subsets, run the learning algorithm on each of the subsets, and combine the results. Moreover, data can be inherently distributed across multiple sites on the network and merging all the data in one location can be expensive or prohibitive. In this thesis we propose, investigate, and evaluate a metalearning approach to integrating the results of mul...
Unstructured Tree Search on SIMD Parallel Computers
 IEEE Transactions on Parallel and Distributed Systems
, 1994
"... In this paper, we present new methods for load balancing of unstructured tree computations on largescale SIMD machines, and analyze the scalability of these and other existing schemes. An efficient formulation of tree search on a SIMD machine comprises of two major components: (i) a triggering mech ..."
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Cited by 38 (15 self)
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In this paper, we present new methods for load balancing of unstructured tree computations on largescale SIMD machines, and analyze the scalability of these and other existing schemes. An efficient formulation of tree search on a SIMD machine comprises of two major components: (i) a triggering mechanism, which determines when the search space redistribution must occur to balance search space over processors; and (ii) a scheme to redistribute the search space. We have devised a new redistribution mechanism and a new triggering mechanism. Either of these can be used in conjunction with triggering and redistribution mechanisms developed by other researchers. We analyze the scalability of these mechanisms, and verify the results experimentally. The analysis and experiments show that our new load balancing methods are highly scalable on SIMD architectures. Their scalability is shown to be no worse than that of the best load balancing schemes on MIMD architectures. We verify our theoretical...
Scalability of parallel algorithms for the allpairs shortest path problem
 in the Proceedings of the International Conference on Parallel Processing
, 1991
"... Abstract This paper uses the isoefficiency metric to analyze the scalability of several parallel algorithms for finding shortest paths between all pairs of nodes in a densely connected graph. Parallel algorithms analyzed in this paper have either been previously presented elsewhere or are small vari ..."
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Cited by 36 (14 self)
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Abstract This paper uses the isoefficiency metric to analyze the scalability of several parallel algorithms for finding shortest paths between all pairs of nodes in a densely connected graph. Parallel algorithms analyzed in this paper have either been previously presented elsewhere or are small variations of them. Scalability is analyzed with respect to mesh, hypercube and sharedmemory architectures. We demonstrate that isoefficiency functions are a compact and useful predictor of performance. In fact, previous comparative predictions of some of the algorithms based on experimental results are shown to be incorrect whereas isoefficiency functions predict correctly. We find the classic tradeoffs of hardware cost vs. time and memory vs. time to be represented here as tradeoffs of hardware cost vs. scalability and memory vs. scalability.
Performance and scalability of preconditioned conjugate gradient methods on parallel computers
 Department of Computer Science, University of Minnesota
, 1995
"... ..."
Parallel program performance prediction using deterministic task graph analysis
 ACM Trans. Comput. Syst
, 2004
"... In this paper, we consider analytical techniques for predicting detailed performance characteristics of a single shared memory parallel program for a particular input. Analytical models for parallel programs have been successful at providing simple qualitative insights and bounds on program scalabil ..."
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Cited by 27 (3 self)
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In this paper, we consider analytical techniques for predicting detailed performance characteristics of a single shared memory parallel program for a particular input. Analytical models for parallel programs have been successful at providing simple qualitative insights and bounds on program scalability, but have been less successful in practice for providing detailed insights and metrics for program performance (leaving these to measurement or simulation). We develop a conceptually simple modeling technique called deterministic task graph analysis that provides detailed performance prediction for sharedmemory programs with arbitrary task graphs, a wide variety of task scheduling policies, and significant communication and resource contention. Unlike many previous models that are stochastic models, our model assumes deterministic task execution times (while retaining the use of stochastic models for communication and resource contention). This assumption is supported by a previous study of the influence of nondeterministic delays in parallel programs. We evaluate our model in three ways. First, an experimental evaluation shows that our analysis technique is accurate and efficient for a variety of sharedmemory programs, including programs with large and/or complex task graphs, sophisticated task scheduling, highly nonuniform task
Scalability Issues Affecting the Design of a Dense Linear Algebra Library
 JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING
, 1994
"... This paper discusses the scalability of Cholesky, LU, and QR factorization routines on MIMD distributed memory concurrent computers. These routines form part of the ScaLAPACK mathematical software library that extends the widelyused LAPACK library to run efficiently on scalable concurrent computers ..."
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Cited by 25 (12 self)
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This paper discusses the scalability of Cholesky, LU, and QR factorization routines on MIMD distributed memory concurrent computers. These routines form part of the ScaLAPACK mathematical software library that extends the widelyused LAPACK library to run efficiently on scalable concurrent computers. To ensure good scalability and performance, the ScaLAPACK routines are based on blockpartitioned algorithms that reduce the frequency of data movement between different levels of the memory hierarchy, and particularly between processors. The block cyclic data distribution, that is used in all three factorization algorithms, is described. An outline of the sequential and parallel blockpartitioned algorithms is given. Approximate models of algorithms' performance are presented to indicate which factors in the design of the algorithm have an impact upon scalability. These models are compared with timings results on a 128node Intel iPSC/860 hypercube. It is shown that the routines are highl...
Memory Usage in the LANL CM5 Workload
 In Job Scheduling Strategies for Parallel Processing
, 1997
"... . It is generally agreed that memory requirements should be taken into account in the scheduling of parallel jobs. However, so far the work on combined processor and memory scheduling has not been based on detailed information and measurements. To rectify this problem, we present an analysis of ..."
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Cited by 25 (7 self)
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. It is generally agreed that memory requirements should be taken into account in the scheduling of parallel jobs. However, so far the work on combined processor and memory scheduling has not been based on detailed information and measurements. To rectify this problem, we present an analysis of memory usage by a production workload on a large parallel machine, the 1024node CM5 installed at Los Alamos National Lab. Our main observations are  The distribution of memory requests has strong discrete components, i.e. some sizes are much more popular than others.  Many jobs use a relatively small fraction of the memory available on each node, so there is some room for time slicing among several memoryresident jobs.  Larger jobs (using more nodes) tend to use more memory, but it is difficult to characterize the scaling of perprocessor memory usage. 1 Introduction Resource management includes a number of distinct topics, such as scheduling and memory management. Howeve...
Performance Properties of Large Scale Parallel Systems
 Department of Computer Science, University of Minnesota
, 1993
"... There are several metrics that characterize the performance of a parallel system, such as, parallel execution time, speedup and efficiency. A number of properties of these metrics have been studied. For example, it is a well known fact that given a parallel architecture and a problem of a fixed size ..."
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Cited by 23 (7 self)
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There are several metrics that characterize the performance of a parallel system, such as, parallel execution time, speedup and efficiency. A number of properties of these metrics have been studied. For example, it is a well known fact that given a parallel architecture and a problem of a fixed size, the speedup of a parallel algorithm does not continue to increase with increasing number of processors. It usually tends to saturate or peak at a certain limit. Thus it may not be useful to employ more than an optimal number of processors for solving a problem on a parallel computer. This optimal number of processors depends on the problem size, the parallel algorithm and the parallel architecture. In this paper we study the impact of parallel processing overheads and the degree of concurrency of a parallel algorithm on the optimal number of processors to be used when the criterion for optimality is minimizing the parallel execution time. We then study a more general criterion of optimalit...
Selected problems of scheduling tasks in multiprocessor computing systems
 PHD THESIS, INSTYTUT INFORMATYKI POLITECHNIKA POZNANSKA
, 1997
"... ..."
Optimal distributed online prediction using minibatches
, 2010
"... Online prediction methods are typically presented as serial algorithms running on a single processor. However, in the age of webscale prediction problems, it is increasingly common to encounter situations where a single processor cannot keep up with the high rate at which inputs arrive. In this wor ..."
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Cited by 21 (2 self)
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Online prediction methods are typically presented as serial algorithms running on a single processor. However, in the age of webscale prediction problems, it is increasingly common to encounter situations where a single processor cannot keep up with the high rate at which inputs arrive. In this work, we present the distributed minibatch algorithm, a method of converting many serial gradientbased online prediction algorithms into distributed algorithms. We prove a regret bound for this method that is asymptotically optimal for smooth convex loss functions and stochastic inputs. Moreover, our analysis explicitly takes into account communication latencies between nodes in the distributed environment. We show how our method can be used to solve the closelyrelated distributed stochastic optimization problem, achieving an asymptotically linear speedup over multiple processors. Finally, we demonstrate the merits of our approach on a webscale online prediction problem.