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Emergency Backup for Scientific Applications

Emergency Backup for Scientific Applications

引用

Checkpointing for Supercomputing (SuperCheck), international symposium on

作者： Aniello Esposito Christopher Haine Ali Mohammed EMEA Research Lab Hewlett Packard Enterprise Basel Basel Switzerland

ISBN: (纸本)9781665475730

A framework for the efficient in-network data transfer between a parallel application and an independent storage server is proposed. The case of an unexpected and unrecoverable interruption of the application is considered, where the server takes the role of an emergency backup service preventing the unnecessary loss of valuable information. Workload managers such as SLURM provide a time buffer between the interruption and the termination of an application which can be optimally exploited by the framework making use of RDMA transport and redistribution of data by means of the Maestro middleware. An alternative could consist in a state-of-the-art checkpoint restart mechanism relying on a possibly shared storage hierarchy which suffers from variability and is not scalable in general or in-memory checkpointing which increases memory consumption considerably. Experiments are performed on a HPE/Cray EX system to construct a heuristics for amounts of data that can realistically be backed up during a given time buffer. The method proves to be faster than VELOC and plain MPI-IO using one server node already, for a number of user ranks up to a hundred, with the promise of also better scalability in the long run due to the in-network approach as opposed to filesystem transport.

关键词： Checkpointing Multithreading Scalability Memory management Layout Graphics processing units distributed databases

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FP4S: Fragment-based parallel State Recovery for Stateful Stream Applications 34

FP4S: Fragment-based Parallel State Recovery for Stateful St...

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

作者： Liu, Pinchao Xu, Hailu Da Silva, Dilma Wang, Qingyang Ahmed, Sarker Tanzir Hu, Liting Florida Int Univ Miami FL 33199 USA Texas A&M Univ College Stn TX 77843 USA Louisiana State Univ Baton Rouge LA 70803 USA

ISBN: (数字)9781728168760

ISBN: (纸本)9781728168760

Streaming computations are by nature long-running. They run in highly dynamic distributed environments where many stream operators may leave or fail at the same time. Most of them are stateful, in which stream operators need to store and maintain large-sized state in memory, resulting in expensive time and space costs to recover them. The state-of-the-art stream processing systems offer failure recovery mainly through three approaches: replication recovery, checkpointing recovery, and DStream-based lineage recovery, which are either slow, resource-expensive or fail to handle many simultaneous failures. We present FP4S, a novel fragment-based parallel state recovery mechanism that can handle many simultaneous failures for a large number of concurrently running stream applications. The novelty of FP4S is that we organize all the application's operators into a distributed hash table (DHT) based consistent ring to associate each operator with a unique set of neighbors. Then we divide each operator's in-memory state into many fragments and periodically save them in each node's neighbors, ensuring that different sets of available fragments can reconstruct lost state in parallel. This approach makes this failure recovery mechanism extremely scalable, and allows it to tolerate many simultaneous operator failures. We apply FP4S on Apache Storm and evaluate it using large-scale real-world experiments, which demonstrate its scalability, efficiency, and fast failure recovery features. When compared to the state-of-the-art solutions (Apache Storm), FP4S reduces 37.8% latency of state recovery and saves more than half of the hardware costs. It can scale to many simultaneous failures and successfully recover the states when up to 66.6% of states fail or get lost.

关键词： Storms Checkpointing Hardware Scalability Sparks Task analysis Bars

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Optimizing High-Performance Computing systems for Biomedical Workloads 34

Optimizing High-Performance Computing Systems for Biomedical...

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

作者： Kovatch, Patricia Gai, Lili Cho, Hyung Min Fluder, Eugene Jiang, Dansha Icahn Sch Med Mt Sinai Genet & Genom Sci New York NY 10029 USA Icahn Sch Med Mt Sinai Off Dean New York NY 10029 USA

ISBN: (纸本)9781728174457

The productivity of computational biologists is limited by the speed of their workflows and subsequent overall job throughput. Because most biomedical researchers are focused on better understanding scientific phenomena rather than developing and optimizing code, a computing and data system implemented in an adventitious and/or non-optimized manner can impede the progress of scientific discovery. In our experience, most computational, life-science applications do not generally leverage the full capabilities of high-performance computing, so tuning a system for these applications is especially critical. To optimize a system effectively, systems staff must understand the effects of the applications on the system. Effective stewardship of the system includes an analysis of the impact of the applications on the compute cores, file system, resource manager and queuing policies. The resulting improved system design, and enactment of a sustainability plan, help to enable a long-term resource for productive computational and data science. We present a case study of a typical biomedical computational workload at a leading academic medical center supporting over $100 million per year in computational biology research. Over the past eight years, our high-performance computing system has enabled over 900 biomedical publications in four major areas: genetics and population analysis, gene expression, machine learning, and structural and chemical biology. We have upgraded the system several times in response to trends, actual usage, and user feedback. Major components crucial to this evolution include scheduling structure and policies, memory size, compute type and speed, parallel file system capabilities, and deployment of cloud technologies. We evolved a 70 teraflop machine to a 1.4 petaflop machine in seven years and grew our user base nearly 10-fold. For long-term stability and sustainability, we established a chargeback fee structure. Our overarching guiding principle for eac

关键词： high performance computing computational biology genomics system optimization scheduling parallel file systems cloud technologies sustainability

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A distributed Cache Mechanism of HDFS to Improve Learning Performance for Deep Reinforcement Learning

A Distributed Cache Mechanism of HDFS to Improve Learning Pe...

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IEEE international Conference on Big Data and Cloud Computing (BdCloud)

作者： Yongqiang Gao Shunyi Feng Zhenkun Li College of Computer Science Inner Mongolia University Hohhot China

Hadoop distributed file system (HDFS) has been widely used in deep reinforcement learning due to its high reliability and high performance. However, the dataset for training deep reinforcement learning models is usually composed of a large number of small files, which will cause the severe performance degradation of HDFS. Although many methods have been proposed to address small files problem in HDFS, none of them considers correlations between small files for training deep reinforcement learning models. To improve the learning performance for deep reinforcement learning, this paper proposes a new HDFS architecture, which uses a distributed cache mechanism to improve data access efficiency for training deep reinforcement learning models. The experimental results show that the proposed HDFS with distributed cache mechanism can achieve better learning performance compared to the traditional HDFS without cache and with centralized caching mechanism.

关键词： Deep learning Training Degradation Energy consumption File systems Clustering algorithms distributed databases

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5th IEEE Workshop on parallel and distributed Processing for Computational Social systems (ParSocial 2021)

2021 IEEE International Parallel and Distributed Processing ...

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2021 IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2021 - In conjunction with IEEE IPDPS 2021 2021年 973-974页

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DASSA: parallel DAS Data Storage and Analysis for Subsurface Event Detection 34

DASSA: Parallel DAS Data Storage and Analysis for Subsurface...

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

作者： Dong, Bin Tribaldos, Veronica Rodriguez Xing, Xin Byna, Suren Ajo-Franklin, Jonathan Wu, Kesheng Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Georgia Inst Technol Atlanta GA 30332 USA Rice Univ Houston TX USA

ISBN: (纸本)9781728168760

Recently developed distributed acoustic sensing (DAS) technologies convert fiber-optic cables into large arrays of subsurface sensors, enabling a variety of applications including earthquake detection and environmental characterization. However, DAS systems produce voluminous datasets sampled at high spatial-temporal resolution and consequently, discovering useful geophysical knowledge within these large-scale data becomes a nearly impossible task for geophysicists. It is appealing to use supercomputers for DAS data analysis, as modern supercomputers are capable of performing over a hundred quadrillion FLOPS operations and have access to exabytes of storage space. Unfortunately, the majority of geophysical data processing libraries are not geared towards these supercomputer environments. This paper introduces a parallel DAS Data Storage and Analysis (DASSA) framework to enable easy-to-use and parallel DAS data analysis on modern supercomputers. DASSA uses a hybrid (i.e., MPI and OpenMP) data analysis execution engine that supports a user-defined function (UDF) interface for various operations and automatically parallelizes them for supercomputer execution. DASSA also provides novel data storage and access strategies, such as communication-avoiding parallel I/O, to reduce the cost of retrieving large DAS data for analysis. Compared with existing data analysis pipelines used by the geophysical community, DASSA is 16x faster and can efficiently scale up to 1456 computing nodes with 11648 CPU cores.

关键词： Supercomputers

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Exploiting High-Bandwidth Memory for FPGA-Acceleration of Inference on Sum-Product Networks

Exploiting High-Bandwidth Memory for FPGA-Acceleration of In...

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

作者： Lukas Weber Johannes Wirth Lukas Sommer Andreas Koch Embedded Systems and Applications Group TU Darmstadt Germany

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Due to the memory wall becoming increasingly problematic in high-performance computing, there is a steady push to improve memory architectures, mainly focusing on better bandwidth as well as latency. One of the results of this push is the development of High-Bandwidth Memory (HBM) which is an alternative to the regular DRAM typically used by accelerator-cards. This work adapts an existing accelerator architecture for inference on Sum-Product Networks (SPN) to exploit the HBM present on more recent high-performance FPGA-accelerator cards. The evaluation shows that the use of HBM enables almost linear scaling of the performance due to the embarrassingly parallel nature of batch-wise SPN inference. It is also shown that the only hindrance to this scaling is the limited bandwidth available for data-transfers between host and FPGA. Even with this bottleneck, the prior FPGA-based implementation is outperformed by up to 1.50x (geo.-mean 1.29x). Similarly, the CPU and GPU baselines are outperformed by up to 2.4x (geo.-mean 1.6x) and 8.4x (geo.-mean 6.9x) respectively. Based on the evaluation, the scaling potential of HBM-based FPGA-accelerators is explored to give an outlook on what is to come with future generations of PCIe-based interfaces.

关键词： Memory architecture Graphics processing units Accelerator architectures Random access memory Bandwidth parallel processing Routing

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Cross Online Assignment of Hybrid Task in Spatial Crowdsourcing

Cross Online Assignment of Hybrid Task in Spatial Crowdsourc...

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international Conference on Data Engineering

作者： Zhao Liu Guoqing Xiao Xu Zhou Yunchuan Qin Yunjun Gao Kenli Li College of Computer Science and Electronic Engineering Hunan University China College of Computer Science and Technology Zhejiang University China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Task assignment is a fundamental problem in spatial crowdsourcing. In many spatial crowdsourcing platforms, such as Didi, AMAP, and Uber, there are hybrid tasks, including real-time and reservation-type tasks, which are with different constraints and unevenly distributed in spatial and temporal. For these hybrid tasks, most existing studies suffer from low task completion rate and low profit for two reasons: firstly, they focus on homogeneous tasks with uniform constraints, and assign hybrid tasks separately; secondly, they cannot effectively address the uneven distribution of hybrid tasks. Inspired by this, we delve into the problem of online hybrid task assignment (HyTAO) with the goal of maximizing total revenue by simultaneously assigning both real-time and reservation-type tasks online for the first time. We prove the NP-hardness of the offline version of the HyTAO problem. To solve HyTAO effectively, we utilize a cross-platform cooperation model to tackle the challenge of non-uniform distribution. Following this, we design a binary tree-based search algorithm, namely BTS, which is capable of uniformly processing various types of tasks and quickly searching for available workers. Additionally, we discuss the parallel optimization strategies of BTS. To further enhance performance, we develop TBTS, which identifies tasks with high increased revenue based on a threshold. Finally, we conduct a comprehensive analysis of the complexity and competitive ratio of both BTS and TBTS. Extensive experiments are performed to demonstrate the efficiency of our approaches.

关键词： Crowdsourcing Deep learning Prediction algorithms Data engineering Real-time systems Spatial databases Complexity theory

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Benchmarking the Linear Algebra Awareness of TensorFlow and PyTorch

Benchmarking the Linear Algebra Awareness of TensorFlow and ...

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

作者： Aravind Sankaran Navid Akbari Alashti Christos Psarras Paolo Bientinesi RWTH Aachen University Germany Ume&#x00E5 Universitet Sweden

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Linear algebra operations, which are ubiquitous in machine learning, form major performance bottlenecks. The High-Performance Computing community invests significant effort in the development of architecture-specific optimized kernels, such as those provided by the BLAS and LAPACK libraries, to speed up linear algebra operations. However, end users are progressively less likely to go through the error prone and time-consuming process of directly using said kernels; instead, frameworks such as TensorFlow (TF) and PyTorch (PyT), which facilitate the development of machine learning applications, are becoming more and more popular. Although such frameworks link to BLAS and LAPACK, it is not clear whether or not they make use of linear algebra knowledge to speed up computations. For this reason, in this paper we develop benchmarks to investigate the linear algebra optimization capabilities of TF and PyT. Our analyses reveal that a number of linear algebra optimizations are still missing; for instance, reducing the number of scalar operations by applying the distributive law, and automatically identifying the optimal parenthesization of a matrix chain. In this work, we focus on linear algebra computations in TF and PyT; we both expose opportunities for performance enhancement to the benefit of the developers of the frameworks and provide end users with guidelines on how to achieve performance gains.

关键词： Linear systems Linear algebra Machine learning Benchmark testing Performance gain parallel processing Matrices

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Communication-Efficient Jaccard similarity for High-Performance distributed Genome Comparisons 34

Communication-Efficient Jaccard similarity for High-Performa...

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

作者： Besta, Maciej Kanakagiri, Raghavendra Mustafa, Harun Karasikov, Mikhail Raetsch, Gunnar Hoefler, Torsten Solomonik, Edgar Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Univ Illinois Dept Comp Sci Urbana IL USA Univ Hosp Zurich Biomed Informat Res Zurich Switzerland SIB Swiss Inst Bioinformat Lausanne Switzerland Indian Inst Technol Tirupati Dept Comp Sci Tirupati Andhra Pradesh India

ISBN: (纸本)9781728168760

The Jaccard similarity index is an important measure of the overlap of two sets, widely used in machine learning, computational genomics, information retrieval, and many other areas. We design and implement SimilarityAtScale, the first communication-efficient distributed algorithm for computing the Jaccard similarity among pairs of large datasets. Our algorithm provides an efficient encoding of this problem into a multiplication of sparse matrices. Both the encoding and sparse matrix product are performed in a way that minimizes data movement in terms of communication and synchronization costs. We apply our algorithm to obtain similarity among all pairs of a set of large samples of genomes. This task is a key part of modern metagenomics analysis and an evergrowing need due to the increasing availability of high-throughput DNA sequencing data. The resulting scheme is the first to enable accurate Jaccard distance derivations for massive datasets, using large-scale distributed-memory systems. We package our routines in a tool, called GenomeAtScale, that combines the proposed algorithm with tools for processing input sequences. Our evaluation on real data illustrates that one can use GenomeAtScale to effectively employ tens of thousands of processors to reach new frontiers in large-scale genomic and metagenomic analysis. While GenomeAtScale can be used to foster DNA research, the more general underlying SimilarityAtScale algorithm may be used for high-performance distributed similarity computations in other data analytics application domains.

关键词： distributed Jaccard Distance distributed Jaccard similarity Genome Sequence Distance Metagenome Sequence Distance High-Performance Genome Processing k-Mers Matrix-Matrix Multiplication Cyclops Tensor Framework

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