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IEEE international symposium on Information Theory (ISIT)

作者： Peng, Pei Soljanin, Emina Whiting, Philip Rutgers State Univ New Brunswick NJ 08901 USA Macquarie Univ Sydney NSW Australia

ISBN: (数字)9781728164328

ISBN: (纸本)9781728164328

distributed computing enables parallel execution of tasks that make up a large computing job. Random fluctuations in service times (inherent to computing environments) often cause a non-negligible number of straggling tasks with long completion time. Redundancy, in the form of task replication and erasure coding, has emerged as a potentially powerful way to curtail the variability in service time, as it provides diversity that allows a job to be completed when only a subset of redundant tasks gets executed. Thus both redundancy and parallelism reduce the execution time, but compete for resources of the system. In situations of constrained resources (here fixed number of parallel servers), increasing redundancy reduces the available level of parallelism. We characterize the diversity vs. parallelism tradeoff for three common models of task size dependent execution times. We find that different models operate optimally at different levels of redundancy, and thus may require very different code rates.

关键词： Redundancy

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parallel Approximations of the Tukey g-and-h Likelihoods and Predictions for Non-Gaussian Geostatistics

Parallel Approximations of the Tukey g-and-h Likelihoods and...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Sagnik Mondal Sameh Abdulah Hatem Ltaief Ying Sun Marc G. Genton David E. Keyes Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Kingdom of Saudi Arabia Statistics Program King Abdullah University of Science and Technology Extreme Computing Research Center King Abdullah University of Science and Technology

Maximum likelihood estimation is an essential tool in the procedure to impute missing data in climate/weather applications. By defining a particular statistical model, the maximum likelihood estimation can be used to understand the underlying structure of given geospatial data. The Gaussian random field has been widely used to describe geospatial data, as one of the most popular models under the hood of maximum likelihood estimation. Computation of Gaussian log-likelihood demands operations on a dense symmetric positive definite matrix, often parameterized by the Matérn correlation function. This computation of the log-likelihood requires $\mathcal{O}(n^{2})$ storage and $\mathcal{O}(n^{3})$ operations, which can be a huge task considering that the number of geographical locations, $n$ , now commonly reaches into the millions. However, despite its appealing theoretical properties, the assumptions of Gaussianity may be unrealistic since real data often show signs of skewness or have some extreme values. Herein, we consider the Tukey ${g-}$ and $-h$ (TGH) random field as an example of a non-Gaussian random field that shows more robustness in modeling geospatial data by including two more parameters to incorporate skewness and heavy tail features in the model. This work provides the first HPC implementation of the TGH random field's inference on parallel hardware architectures. Using task-based programming models associated with dynamic runtime systems, our implementation leverages the high concurrency of current parallel systems. This permits to run the exact log-likelihood evaluation of the Tukey g-and-h (TGH) random fields for a decent number of geospatial locations. To tackle large-scale problems, we provide additionally an implementation of the given model using two different low-rank approximations. We compress the aforementioned positive-definite symmetric matrix for computing the log-likelihood and rely on the Tile Low-Rank (TLR) and the Hierarchical Off-Diagon

关键词： Maximum likelihood estimation Symmetric matrices Runtime Computational modeling Linear algebra Tail Predictive models

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Asynchronous distributed-Memory Triangle Counting and LCC with RMA Caching

Asynchronous Distributed-Memory Triangle Counting and LCC wi...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Andrá s Strausz Flavio Vella Salvatore Di Girolamo Maciej Besta Torsten Hoefler Dept. of Computer Science ETH Zurich Zurich Switzerland Dept. of Engineering and Computer Science University of Trento Trento Italy

Triangle count and local clustering coefficient are two core metrics for graph analysis. They find broad application in analyses such as community detection and link recommen-dation. To cope with the computational and memory demands that stem from the size of today's graph datasets, distributed-memory algorithms have to be developed. Current state-of-the-art solutions suffer from synchronization overheads or expensive pre-computations needed to distribute the graph, achieving limited scaling capabilities. We propose a fully asynchronous implementation for triangle counting and local clustering coef-ficient based on 1D partitioning, using remote memory accesses for transferring data and avoid synchronization. Additionally, we show how these algorithms present data reuse on remote memory accesses and how the overall communication time can be improved by caching these accesses. Finally, we extend CLaMPI, a software-layer caching system for MPI RMA, to include application-specific scores for cached entries and influence the eviction procedure to improve caching efficiency. Our results show improvements on shared memory, and we achieve 14x speedup from 4 to 64 nodes for the LiveJoumal 1 graph on distributed memory. Moreover, we demonstrate how caching remote accesses reduces total running time by up to 73 % with respect to a non-cached version. Finally, we compare our implementation to TriC, the 2020 graph champion paper, and achieve up to 100x faster results for scale-free graphs.

关键词： Measurement distributed processing Costs Clustering algorithms Partitioning algorithms Peer-to-peer computing Synchronization

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parallel algorithms for finding connected components using linear algebra

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JOURNAL OF parallel AND distributed computing 2020年 144卷 14-27页

作者： Zhang, Yongzhe Azad, Ariful Buluc, Aydin Grad Univ Adv Studies SOKENDAI Dept Informat Hayama Kanagawa Japan Indiana Univ Dept Intelligent Syst Engn Bloomington IN 47405 USA Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA USA

Finding connected components is one of the most widely used operations on a graph. Optimal serial algorithms for the problem have been known for half a century, and many competing parallel algorithms have been proposed over the last several decades under various different models of parallel computation. This paper presents a class of parallel connected-component algorithms designed using linear-algebraic primitives. These algorithms are based on a PRAM algorithm by Shiloach and Vishkin and can be designed using standard GraphBLAS operations. We demonstrate two algorithms of this class, one named LACC for Linear Algebraic Connected Components, and the other named FastSV which can be regarded as LACC's simplification. With the support of the highly-scalable Combinatorial BLAS library, LACC and FastSV outperform the previous state-of-the-art algorithm by a factor of up to 12x for small to medium scale graphs. For large graphs with more than 50B edges, LACC and FastSV scale to 4K nodes (262K cores) of a Cray XC40 supercomputer and outperform previous algorithms by a significant margin. This remarkable performance is accomplished by (1) exploiting sparsity that was not present in the original PRAM algorithm formulation, (2) using high-performance primitives of Combinatorial BLAS, and (3) identifying hot spots and optimizing them away by exploiting algorithmic insights. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Connected Component distributed memory GraphBLAS

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HiCOMB 2022 Invited Speaker: Pandemic-scale Phylogenetics

HiCOMB 2022 Invited Speaker: Pandemic-scale Phylogenetics

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Yatish Turakhia University of California San Diego

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Phylogenetics has been central to the genomic surveillance, epidemiology and contact tracing efforts during the COVD-19 pandemic. But the massive scale of genomic sequencing has rendered the pre-pandemic tools quite inadequate for comprehensive phylogenetic analyses. In this talk, I will discuss a high-performance computing (HPC) phylogenetic package that we developed to address the needs imposed by this pandemic. Orders of magnitude gains were achieved by this package through several domain-specific optimization and parallelization techniques. The package comprises four programs: UShER, matOptimize, RIPPLES and matUtils. Using high-performance computing, UShER and matOptimize maintain and refine daily a massive mutation-annotated phylogenetic tree consisting of all (>9M currently) SARSCoV-2 sequences available on online repositories. With UShER and RIPPLES, individual labs - even with modest compute resources - incorporate newly-sequenced SARS-CoV-2 genomes on this phylogeny and discover evidence for recombination in real-time. With matUtils, they rapidly query and visualize massive SARS-CoV-2 phylogenies. This has empowered scientists worldwide to study the SARS-CoV-2 evolutionary and transmission dynamics at an unprecedented scale, resolution and speed. This has laid the groundwork for future genomic surveillance of MOST infectious pathogens.

关键词： Phylogeny Genomics Bioinformatics Coronaviruses Surveillance Pandemics High performance computing

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Machine Learning for CUDA+MPI Design Rules

Machine Learning for CUDA+MPI Design Rules

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Carl Pearson Aurya Javeed Karen Devine Sandia National Laboratories Albuquerque NM USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

We present a new strategy for automatically exploring the design space of key CUDA + MPI programs and providing design rules that discriminate slow from fast implementations. In such programs, the order of operations (e.g., G PU kernels, MPI communication) and assignment of operations to resources (e.g., G PU streams) makes the space of possible designs enormous. Systems experts have the task of redesigning and reoptimizing these programs to effectively utilize each new platform. This work provides a prototype tool to reduce that burden. In our approach, a directed acyclic graph of CUDA and MPI operations defines the design space for the program. Monte-Carlo tree search discovers regions of the design space that have large impact on the program's performance. A sequence-to-vector transformation defines features for each explored im-plementation, and each implementation is assigned a class label according to its relative performance. A decision tree is trained on the features and labels to produce design rules for each class; these rules can be used by systems experts to guide their implementations. We demonstrate our strategy using a key kernel from scientific computing - sparse-matrix vector multiplication - on a platform with multiple MPI ranks and GPU streams.

关键词： distributed processing Directed acyclic graph Monte Carlo methods Scientific computing Graphics processing units Prototypes Machine learning

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Real-time highly-parallel tracking processing with FPGA at LHCb

Real-time highly-parallel tracking processing with FPGA at L...

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IEEE symposium on Nuclear Science (NSS/MIC)

作者： R. Fantechi W. Baldini G. Bassi A. Contu S. Kotriakhova F. Lazzari M. J. Morello F. Novissimo G. Punzi F. Terzuoli G. Tuci INFN - Istituto Nazionale di Fisica Nucleare Pisa Italy University of Pisa Pisa Italy INFN - Istituto Nazionale di Fisica Nucleare Ferrara Italy University of Ferrara Ferrara Italy Scuola Normale Superiore - Pisa Pisa Italy INFN - Istituto Nazionale di Fisica Nucleare Cagliari Italy University of Siena Siena Italy Physikalisches Institut Universitaet Heidelberg Heidelberg Germany

For the future high luminosity LHC runs, it is expected to reconstruct events more complex than the actual ones. in view of this need, the LHCb Collaboration has established a testbed for heterogeneous computing equipment to develop solutions for real-time event reconstruction integrated with the LHCb DAQ infrastructure. The most advanced one is a highly-parallelized tracking processor, based on the "Artificial Retina", which uses a set of FPGA, to which the data are distributed using a custom optical mesh with high speed links. The aim at the testbed is to implement a demonstrator for the tracking of pixel detectors, able to manage the data of a sector of a detector. The scalability to a full size processor will be discussed, as well as the DAQ integration issues for a system running on real data.

关键词：

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Modeling Power Consumption of Lossy Compressed I/O for Exascale HPC Systems

Modeling Power Consumption of Lossy Compressed I/O for Exasc...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Grant Wilkins Jon C. Calhoun Holcombe Department of Electrical and Computer Engineering Clemson University Clemson South Carolina

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Exascale computing enables unprecedented, detailed and coupled scientific simulations which generate data on the order of tens of petabytes. Due to large data volumes, lossy compressors become indispensable as they enable better compression ratios and runtime performance than lossless compressors. Moreover, as (high-performance computing) HPC systems grow larger, they draw power on the scale of tens of megawatts. Data motion is expensive in time and energy. Therefore, optimizing compressor and data I/O power usage is an important step in reducing energy consumption to meet sustainable computing goals and stay within limited power budgets. In this paper, we explore efficient power consumption gains for the SZ and ZFP lossy compressors and data writing on a cloud HPC system while varying the CPU frequency, scientific data sets, and system architecture. Using this power consumption data, we construct a power model for lossy compression and present a tuning methodology that reduces energy overhead of lossy compressors and data writing on HPC systems by 14.3% on average. We apply our model and find 6.5 kJ s, or 13 %, of savings on average for 512GB I/O. Therefore, utilizing our model results in more energy efficient lossy data compression and I/O.

关键词： Power demand Runtime Exascale computing Computational modeling Systems architecture Writing Compressors

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An Approach to Estimate Power Consumption of a CUDA Kernel 18

An Approach to Estimate Power Consumption of a CUDA Kernel

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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Alavani, Gargi Desai, Jineet Sarkar, Santonu BITS Pilani KK Birla Goa Campus Dept CS & IS Goa India

ISBN: (纸本)9781665414852

Graphics Processing Unit (GPU) has emerged as a popular computing device to achieve Exa-scale performance in High-Performance computing (HPC) applications. While the power-performance ratio is relatively high for a GPU, it still draws a significant amount of power during computation. In this paper, we propose a preliminary power prediction model which can be used by developers for building power-efficient GPU applications. Using this proposed work, developers can estimate the power consumption of a GPU application during implementation without having to execute it on actual hardware. Our model combines the information derived from static analysis of a CUDA program and a machine learning-based model. We have utilised decision tree technique to validate results across three different GPU architectures: Kepler, Maxwell and Volta. Observed R-2 score value using the decision tree model is 0.8973 for Volta architecture.

关键词： GPGPU High Performance computing Power Modelling Prediction Technique

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Exploring Chapel Productivity Using Some Graph Algorithms 34

Exploring Chapel Productivity Using Some Graph Algorithms

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Barrett, Richard F. Cook, Jeanine Olivier, Stephen L. Aaziz, Omar Jenkins, Christipher D. Vaughan, Courtenay T. Sandia Natl Labs Cyber Intelligence Res POB 5800 Albuquerque NM 87185 USA Sandia Natl Labs Ctr Comp Res POB 5800 Albuquerque NM 87185 USA Sandia Natl Labs Cyber Secur & Mission Comp Ctr POB 5800 Albuquerque NM 87185 USA Sandia Natl Labs Syst Secur Res POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9781728174457

A broad set of data science and engineering questions may be organized as graphs, providing a powerful means for describing relational data. Although experts now routinely compute graph algorithms on huge, unstructured graphs using high performance computing (HPC) or cloud resources, this practice hasn't yet broken into the mainstream. Such computations require great expertise, yet users often need rapid prototyping and development to quickly customize existing code. Toward that end, we are exploring the use of the Chapel programming language as a means of making some important graph analytics more accessible, examining the breadth of characteristics that would make for a productive programming environment, one that is expressive, performant, portable, and robust. In this talk we describe our early explorations of this space, based on miniTri [4], a miniapp from the Mantevo suite [1], and the mean hitting time algorithm [2], one of the analytics being explored within Grafiki1 [3], both of which are designed for use on distributed memory parallel processing environments. These implementations have been posed in terms of key linear algebra operations and algorithms, specifically sparse matrix-matrix multiplication, operating on integer datatypes, and the Conjugate Gradient method, based on a graph Laplacian matrix.

关键词： High performance computing parallel programming graph analytics

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