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Further results on convergence of asynchronous linear iterations

LINEAR ALGEBRA AND ITS APPLICATIONS 1998年第1-3期281卷 11-24页

作者： Su, YF Bhaya, A Kaszkurewicz, E Kozyakin, VS Univ Fed Rio de Janeiro Lab Parallel Computing Rio De Janeiro Brazil Russian Acad Sci Inst Informat Transmiss Problems Moscow Russia

This paper focuses on the convergence problem of asynchronous linear iterations. A stronger version of the necessity part of the classical Chazan-Miranker theorem is proved and new results for special classes of itera... 详细信息

关键词： convergence delays asynchronism iterative methods

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DP2: A Highly parallel Range Join for Genome Analysis on Distributed computing Platform

DP2: A Highly Parallel Range Join for Genome Analysis on Dis...

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2019 International Conference on High Performance computing and Simulation, HPCS 2019

作者： Sinha, Aman Lai, Bo-Cheng National Chiao Tung University Parallel Computing System Lab Hsinchu City Taiwan

ISBN: (纸本)9781728144849

Rapid growth of the sheer amount of genome data and intense computation become great challenges for downstream genome analytics. Efficient parallel processing and distributed computing are the two effective schemes to address the analysis of big data. Range join is a widely used, effective, yet time-consuming operation that finds the overlap between two different sets of genome features. The current widely adopted BEDTools [6] pipeline adopts single-node binary tree approach, while the distributed GenAp scheme fails to exploit the massive parallel computation on modern throughput processors, such as GPU (Graphic Processing Unit). This paper proposes a novel Distributed parallel P-ary search (DP2) that applies novel P-ary analysis to enable high parallelism at algorithmic level, and extensively utilize multiple GPUs at system and architecture level. Efficient computation allocation is implemented to leverage the distributed computing on clusters. The proposed framework can be well integrated with current BEDTools [6] pipeline, and achieves an average of 25x speedup for the actual range-join operation when compared with Binary tree approach of GenAp and a 13x end-to-end (total execution time) speedup in comparison to ADAM. © 2019 IEEE.

关键词： Pipelines

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Sparsifying synchronization for high-performance shared-memory sparse triangular solver

Sparsifying synchronization for high-performance shared-memo...

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29th International Supercomputing Conference, ISC 2014

作者： Park, Jongsoo Smelyanskiy, Mikhail Sundaram, Narayanan Dubey, Pradeep Parallel Computing Lab. Intel Corporation Santa Clara CA United States

ISBN: (纸本)9783319075174

The last decade has seen rapid growth of single-chip multiprocessors (CMPs), which have been leveraging Moore's law to deliver high concurrency via increases in the number of cores and vector width. Modern CMPs execute from several hundreds to several thousands concurrent operations per second, while their memory subsystem delivers from tens to hundreds Giga-bytes per second bandwidth. Taking advantage of these parallel resources requires highly tuned parallel implementations of key computational kernels, which form the back-bone of modern HPC. Sparse triangular solver is one such kernel and is the focus of this paper. It is widely used in several types of sparse linear solvers, and it is commonly considered challenging to parallelize and scale even on a moderate number of cores. This challenge is due to the fact that triangular solver typically has limited task-level parallelism and relies on fine-grain synchronization to exploit this parallelism, compared to data-parallel operations such as sparse matrix-vector multiplication. This paper presents synchronization sparsification technique that significantly reduces the overhead of synchronization in sparse triangular solver and improves its scalability. We discover that a majority of task dependencies are redundant in task dependency graphs which are used to model the flow of computation in sparse triangular solver. We propose a fast and approximate sparsification algorithm, which eliminates more than 90% of these dependencies, substantially reducing synchronization overhead. As a result, on a 12-core Intel® Xeon® processor, our approach improves the performance of sparse triangular solver by 1.6x, compared to the conventional level-scheduling with barrier synchronization. This, in turn, leads to a 1.4x speedup in a pre-conditioned conjugate gradient solver. © 2014 Springer International Publishing.

关键词： Synchronization

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A new energy efficient cluster-head and backup selection scheme in WSN

A new energy efficient cluster-head and backup selection sch...

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2013 IEEE 14th International Conference on Information Reuse and Integration, IEEE IRI 2013

作者： Izadi, Davood Abawajy, Jemal Ghanavati, Sara Parallel and Distributed Computing Lab School of Information Technology Deakin University Geelong Australia

ISBN: (纸本)9781479910502

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Proper organization of nodes (clustering) is one of the major techniques to expand the lifespan of the whole network through aggregating data at the cluster head. The cluster head is the backbone of the entire cluster. That means if a cluster head fails to accomplish its function, the received and collected data by cluster head can be lost. Moreover, the energy consumption following direct communications from sources to base stations will be increased. In this paper, we propose a type-2 fuzzy based self-configurable cluster head selection (SCCH) approach to not only consider the selection criterion of the cluster head but also present the cluster backup approach. Thus, in case of cluster failure, the system still works in an efficient way. The novelty of this protocol is the ability of handling communication uncertainty, which is an inherent operational aspect of sensor networks. The experiment results indicate SCCH performs better than other recently developed methods. © 2013 IEEE.

关键词： Wireless sensor networks

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Study of a code comment decision method based on structural features

Study of a code comment decision method based on structural ...

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2019 International Conference on Intelligent computing, Automation and Systems, ICICAS 2019

作者： Wang, Renmin Wang, Tao Wang, Huaimin Key Lab. of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (纸本)9781728161068

Code comment is one of the most effective ways to help programmers to understand the source code. High-quality comment decisions can not only cover the core code snippets in the software system but also avoid generating redundant code comments. However, in actual development, there is no uniform comment specification, and most of the comment decisions depend on personal experience and domain knowledge. This paper has learned a common comment decision specification from a large number of code comment examples to assist programmers in making appropriate comment decisions during code development. This paper proposes a method to extract the code structure from the context code of the current code line, and use the machine learning algorithm to determine the possibility that the current code needs to add comments. The proposed method was evaluated on 8 well-known open-source Python projects in GitHub and the experimental results show the feasibility and effectiveness of the method in some code types. © 2019 IEEE.

关键词： Machine learning

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Lattice QCD on Intel® Xeon Phi™ coprocessors

Lattice QCD on Intel® Xeon Phi™ coprocessors

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28th International Supercomputing Conference on Supercomputing, ISC 2013

作者： Joó, Bálint Kalamkar, Dhiraj D. Vaidyanathan, Karthikeyan Smelyanskiy, Mikhail Pamnany, Kiran Lee, Victor W. Dubey, Pradeep Watson III, William Thomas Jefferson National Accelerator Facility Newport News VA United States Parallel Computing Lab. Intel Corporation Bangalore India Parallel Computing Lab. Intel Corporation Santa Clara CA United States

ISBN: (纸本)9783642387494

Lattice Quantum Chromodynamics (LQCD) is currently the only known model independent, non perturbative computational method for calculations in the theory of the strong interactions, and is of importance in studies of nuclear and high energy physics. LQCD codes use large fractions of supercomputing cycles worldwide and are often amongst the first to be ported to new high performance computing architectures. The recently released Intel Xeon Phi architecture from Intel Corporation features parallelism at the level of many x86-based cores, multiple threads per core, and vector processing units. In this contribution, we describe our experiences with optimizing a key LQCD kernel for the Xeon Phi architecture. On a single node, using single precision, our Dslash kernel sustains a performance of up to 320 GFLOPS, while our Conjugate Gradients solver sustains up to 237 GFLOPS. Furthermore we demonstrate a fully 'native' multi-node LQCD implementation running entirely on KNC nodes with minimum involvement of the host CPU. Our multi-node implementation of the solver has been strong scaled to 3.9 TFLOPS on 32 KNCs. © 2013 Springer-Verlag.

关键词： Quantum theory

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Cache line aware optimizations for ccnuma systems 15

Cache line aware optimizations for ccnuma systems

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24th ACM Symposium on High-Performance parallel and Distributed computing, HPDC 2015

作者： Ramos, Sabela Hoefler, Torsten Computer Architecture Group University of A Coruña Spain Scalable Parallel Computing Lab ETH Zurich Switzerland

ISBN: (纸本)9781450335508

Current shared memory systems utilize complex memory hi-erarchies to maintain scalability when increasing the num-ber of processing units. Although hardware designers aim to hide this complexity from the programmer, ignoring the detailed architectural characteristics can harm performance significantly. We propose to expose the block-based design of caches in parallel computers to middleware designers to allow semi-automatic performance tuning with the system-atic translation from algorithms to an analytic performance model. For this, we design a simple interface for cache line aware (CLa) optimization, a translation methodology, and a full performance model for cache line transfers in ccNUMA systems. Algorithms developed using CLa design perform up to 14x better than vendor and open-source libraries, and 2x better than existing ccNUMA optimizations. © 2015 ACM.

关键词： Middleware

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Blackout: Speeding up recurrent neural network language models with very large vocabularies 4

Blackout: Speeding up recurrent neural network language mode...

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4th International Conference on Learning Representations, ICLR 2016

作者： Ji, Shihao Vishwanathan, S.V.N. Satish, Nadathur Anderson, Michael J. Dubey, Pradeep Parallel Computing Lab. Intel India Univ. of California Santa Cruz United States

We propose BlackOut, an approximation algorithm to efficiently train massive recurrent neural network language models (RNNLMs) with million word vocabularies. BlackOut is motivated by using a discriminative loss, and we describe a weighted sampling strategy which significantly reduces computation while improving stability, sample efficiency, and rate of convergence. One way to understand BlackOut is to view it as an extension of the DropOut strategy to the output layer, wherein we use a discriminative training loss and a weighted sampling scheme. We also establish close connections between BlackOut, importance sampling, and noise contrastive estimation (NCE). Our experiments, on the recently released one billion word language modeling benchmark, demonstrate scalability and accuracy of BlackOut;we outperform the state-of-the art, and achieve the lowest perplexity scores on this dataset. Moreover, unlike other established methods which typically require GPUs or CPU clusters, we show that a carefully implemented version of BlackOut requires only 1-10 days on a single machine to train a RNNLM with a million word vocabulary and billions of parameters on one billion words. Although we describe BlackOut in the context of RNNLM training, it can be used to any networks with large softmax output layers. © ICLR 2016: San Juan, Puerto Rico. All Rights Reserved.

关键词： Approximation algorithms

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parallelizing Julia with a Non-Invasive DSL 31

Parallelizing Julia with a Non-Invasive DSL

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31st European Conference on Object-Oriented Programming, ECOOP 2017

作者： Anderson, Todd A. Liu, Hai Kuper, Lindsey Totoni, Ehsan Vitek, Jan Shpeisman, Tatiana Parallel Computing Lab Intel Labs Chile Northeastern University Czech Technical University Prague Czech Republic

ISBN: (纸本)9783959770354

Computational scientists often prototype software using productivity languages that offer highlevel programming abstractions. When higher performance is needed, they are obliged to rewrite their code in a lower-level efficiency language. Different solutions have been proposed to address this trade-off between productivity and efficiency. One promising approach is to create embedded domain-specific languages that sacrifice generality for productivity and performance, but practical experience with DSLs points to some road blocks preventing widespread adoption. This paper proposes a non-invasive domain-specific language that makes as few visible changes to the host programming model as possible. We present parallelAccelerator, a library and compiler for highlevel, high-performance scientific computing in Julia. parallelAccelerator's programming model is aligned with existing Julia programming idioms. Our compiler exposes the implicit parallelism in high-level array-style programs and compiles them to fast, parallel native code. Programs can also run in "library-only" mode, letting users benefit from the full Julia environment and libraries. Our results show encouraging performance improvements with very few changes to source code required. In particular, few to no additional type annotations are necessary.

关键词： Economic and social effects

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Streaming tensor factorization for infinite data sources

Streaming tensor factorization for infinite data sources

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2018 SIAM International Conference on Data Mining, SDM 2018

作者： Smith, Shaden Huang, Kejun Sidiropoulos, Nicholas D. Karypis, George Intel Parallel Computing Lab United States University of Minnesota United States University of Virginia United States

Sparse tensor factorization is a popular tool in multi-way data analysis and is used in applications such as cybersecurity, recommender systems, and social network analysis. In many of these applications, the tensor is not known a priori and instead arrives in a streaming fashion for a potentially unbounded amount of time. Existing approaches for streaming sparse tensors are not practical for unbounded streaming because they rely on maintaining the full factorization of the data, which grows linearly with time. In this work, we present CP-stream, an algorithm for streaming factorization in the model of the canonical polyadic decomposition which does not grow linearly in time or space, and is thus practical for long-term streaming. Additionally, CP-stream incorporates user-specified constraints such as non-negativity which aid in the stability and interpretability of the factorization. An evaluation of CP-stream demonstrates that it converges faster than state-of-the-art streaming algorithms while achieving lower reconstruction error by an order of magnitude. We also evaluate it on real-world sparse datasets and demonstrate its usability in both network traffic analysis and discussion tracking. Our evaluation uses exclusively public datasets and our source code is released to the public as part of SPLATT, an open source high-performance tensor factorization toolkit. © 2018 by SIAM.

关键词： Factorization

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