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检索条件"任意字段=24th International Conference on Parallel and Distributed Computing, Euro-Par 2018"

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24th international european conference on parallel and distributed computing, euro-par 2018

24th International European Conference on Parallel and Distr...

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24th international european conference on parallel and distributed computing, euro-par 2018

ISBN: (纸本)9783319969824

the proceedings contain 57 papers. the special focus in this conference is on parallel and distributed computing. the topics include: Combined Vertical and Horizontal Autoscaling through Model Predictive Control;Early Termination of Failed HPC Jobs through Machine and Deep Learning;peacock: Probe-Based Scheduling of Jobs by Rotating Between Elastic Queues;online Scheduling of Task Graphs on Hybrid Platforms;interference-Aware Scheduling Using Geometric Constraints;resource-Efficient Execution of Conditional parallel Real-Time Tasks;Improving GPU Cache Hierarchy Performance with a Fetch and Replacement Cache;abelian: A Compiler for Graph Analytics on distributed, Heterogeneous Platforms;Using Dynamic Compilation to Achieve Ninja Performance for CNN Training on Many-Core Processors;a Methodology for Performance Analysis of Applications Using Multi-layer I/O;privacy-Preserving Top-k Query Processing in distributed Systems;minimizing Network Traffic for distributed Joins Using Lightweight Locality-Aware Scheduling;VIoLET: A Large-Scale Virtual Environment for Internet of things;adaptive Bandwidth-Efficient Recovery Techniques in Erasure-Coded Cloud Storage;IT Optimization for Datacenters Under Renewable Power Constraint;GPU Provisioning: the 80 - 20 Rule;ECSched: Efficient Container Scheduling on Heterogeneous Clusters;combinatorial Auction Algorithm Selection for Cloud Resource Allocation Using Machine Learning;cloud Federation Formation in Oligopolistic Markets;improving Cloud Simulation Using the Monte-Carlo Method;Runtime Determinacy Race Detection for OpenMP Tasks;Nobody Cares if You Liked Star Wars: KNN Graph Construction on the Cheap;One-Sided Communications for More Efficient parallel State Space Exploration over RDMA Clusters;robust Decentralized Mean Estimation with Limited Communication;snapshot-Based Synchronization: A Fast Replacement for Hand-over-Hand Locking;measuring Multithreaded Message Matching Misery;high-Quality Shared-Memory Graph partitioning.

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parallel Auto-Scheduling of Counting Queries in Machine Learning Applications on HPC Systems

Parallel Auto-Scheduling of Counting Queries in Machine Lear...

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29th international conference on parallel and distributed computing (euro-par)

作者： Bratek, Pawel Szustak, Lukasz Zola, Jaroslaw Czestochowa Tech Univ Dabrowskiego 69 PL-42201 Czestochowa Poland Univ Buffalo Buffalo NY 14260 USA

ISBN: (纸本)9783031488023;9783031488030

We introduce a parallel mechanism for auto-scheduling data access queries in machine learning applications. Our solution combines the advantages of three individual strategies to reduce the time of query stream execution. Using bayesian network learning as a use case, we achieve several times speedup compared to the best possible strategy on two different computing servers.

关键词： Machine learning

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parallel Cholesky Factorization for Banded Matrices Using OpenMP Tasks 29th

Parallel Cholesky Factorization for Banded Matrices Using Op...

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29th international conference on parallel and distributed computing (euro-par)

作者： Liu, Felix Fredriksson, Albin Markidis, Stefano KTH Royal Inst Technol Stockholm Sweden RaySearch Labs Stockholm Sweden

ISBN: (纸本)9783031396977;9783031396984

Cholesky factorization is a method for solving linear systems involving symmetric, positive-definite matrices, and can be an attractive choice in applications where a high degree of numerical stability is needed. One such application is mathematical optimization, where direct methods for solving linear systems are widely used and often a significant performance bottleneck. An example where this is the case, and the specific type of optimization problem motivating this work, is radiation therapy treatment planning, where mathematical optimization is used to create individual treatment plans for patients. To address this bottleneck, we propose a task-based multi-threaded method for Cholesky factorization of banded matrices with medium-sized bands. We implement our algorithm using OpenMP tasks and compare our performance with state-of-the-art libraries such as Intel MKL. Our performance measurements show a performance that is on par or better than Intel MKL (up to similar to 26% on a single CPU socket) for a wide range of matrix bandwidths on two different Intel CPU systems.

关键词： Cholesky factorization Task-Based parallelism Linear Solver

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ODIN: Overcoming Dynamic Interference in iNference Pipelines 29th

ODIN: Overcoming Dynamic Interference in iNference Pipelines

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29th international conference on parallel and distributed computing (euro-par)

作者： Soomro, Pirah Noor Papadopoulou, Nikela Pericas, Miquel Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9783031396977;9783031396984

As an increasing number of businesses becomes powered by machine-learning, inference becomes a core operation, with a growing trend to be offered as a service. In this context, the inference task must meet certain service-level objectives (SLOs), such as high throughput and low latency. However, these targets can be compromised by interference caused by long- or short-lived co-located tasks. Prior works focus on the generic problem of co-scheduling to mitigate the effect of interference on the performance-critical task. In this work, we focus on inference pipelines and propose ODIN, a technique to mitigate the effect of interference on the performance of the inference task, based on the online scheduling of the pipeline stages. Our technique detects interference online and automatically re-balances the pipeline stages to mitigate the performance degradation of the inference task. We demonstrate that ODIN successfully mitigates the effect of interference, sustaining the latency and throughput of CNN inference, and outperforms the least-loaded scheduling (LLS), a common technique for interference mitigation. Additionally, it is effective in maintaining service-level objectives for inference, and it is scalable to large network models executing on multiple processing elements.

关键词： CNN parallel pipelines Online tuning Design space exploration Interference mitigation Inference serving

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Automatic parallelization of Python Programs for distributed Heterogeneous computing 28th

Automatic Parallelization of Python Programs for Distributed...

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28th international conference on parallel and distributed computing (euro-par)

作者： Shirako, Jun Hayashi, Akihiro Paul, Sri Raj Tumanov, Alexey Sarkar, Vivek Georgia Inst Technol Atlanta GA 30332 USA Intel Corp Austin TX 78746 USA

ISBN: (纸本)9783031125973;9783031125966

this paper introduces a new approach to automatic ahead-of-time (AOT) parallelization and optimization of sequential Python programs for execution on distributed heterogeneous platforms. Our approach enables AOT source-to-source transformation of Python programs, driven by the inclusion of type hints for function parameters and return values. these hints can be supplied by the programmer or obtained by dynamic profiler tools;multi-version code generation guarantees the correctness of our AOT transformation in all cases. Our compilation framework performs automatic parallelization and sophisticated high-level code optimizations for the target distributed heterogeneous hardware platform. It introduces novel extensions to the polyhedral compilation framework that unify user-written loops and implicit loops present in matrix/tensor operators, as well as automated selection of CPU vs. GPU code variants. Finally, output parallelized code generated by our approach is deployed using the Ray runtime for scheduling distributed tasks across multiple heterogeneous nodes in a cluster, thereby enabling both intra-node and inter-node parallelism. Our empirical evaluation shows significant performance improvements relative to sequential Python in both single-node and multi-node experiments, with a performance improvement of over 20,000x when using 24 nodes and 144 GPUs in the OLCF Summit supercomputer for the Space-Time Adaptive Processing (STAP) radar application.

关键词： parallelizing compilers Python language parallel computing Heterogeneous computing distributed computing

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ReSLB: Load Balanced Workflow for distributed Deep Learning Mass Spectrometry Database 24

ReSLB: Load Balanced Workflow for Distributed Deep Learning ...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Mocheng, Li Yang, Liu Yang, Ou Zhiguang, Chen Nong, Xiao Tao, Chen College of Computer National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Changsha China College of Computer National University of Defense Technology Changsha China School of Computer Sun Yat-sen University Guangzhou China Beijing China

ISBN: (纸本)9798350319934

the proteomics data analysis pipeline based on the shotgun method requires efficient data processing methods. the parallel algorithm of mass spectrometry database search faces the problems of rapidly expanding database size but low scalability. the heterogeneous database search algorithm based on deep learning is an effective way to solve this problem. Still, the deep learning-based distributed parallel database search algorithm is lacking. this paper analyzes the database searching computational load using a neural network and designs ReSLB, a workflow for the restricted search of mass spectrometry data based on GPU cluster and neural network scoring algorithm. this work aims to ensure the high scalability of future deep learning-based mass spectrometry distributed databases. In the performance estimation of 256 GPUs, the load imbalance of less than 30% and the parallel efficiency of 60% are achieved. Compared with state-of-the-art, the time cost is reduced by 75%, and the parallel efficiency from 1 to 256 GPUs is 3.6x higher than that. © 2022 IEEE.

关键词： Mass spectrometry

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Exploring the Impact of Node Failures on the Resource Allocation for parallel Jobs 27th

Exploring the Impact of Node Failures on the Resource Alloca...

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27th international european conference on parallel and distributed computing (euro-par)

作者： Vardas, Ioannis Ploumidis, Manolis Marazakis, Manolis Fdn Res & Technol Hellas FORTH Inst Comp Sci ICS 100 N Plastira Av Iraklion 70013 Greece

ISBN: (纸本)9783031061561;9783031061554

Increasing the size and complexity of modern HPC systems also increases the probability of various types of failures. Failures may disrupt application execution and waste valuable system resources due to failed executions. In this work, we explore the effect of node failures on the completion times of MPI parallel jobs. We introduce a simulation environment that generates synthetic traces of node failures, assuming that the times between failures for each node are independently distributed, following the same distribution but with different parameters. To highlight the importance of failure-awareness for resource allocation, we compare two failure-oblivious resource allocation approaches with one that considers node failure probabilities before assigning a partition to a job: a heuristic that randomly selects the partition for a job, and Slurm's linear resource allocation policy. We present results for a case study that assumes a 4D-torus topology and a Weibull distribution for each node's time between failures, and considers several different traces of node failures, capturing different failure patterns. For the synthetic traces explored, the benefit is more prominent for longer jobs, up to 82% depending on the trace, when compared with Slurm and a failure-oblivious heuristic. For shorter jobs, benefits are noticeable for systems with more frequent failures.

关键词： Impact of node failures on MPI parallel jobs Fault-aware resource allocation Synthetic node failure trace generation

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OmpSs-2@Cluster: distributed Memory Execution of Nested OpenMP-style Tasks 28th

OmpSs-2@Cluster: Distributed Memory Execution of Nested Open...

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28th international conference on parallel and distributed computing (euro-par)

作者： Aguilar Mena, Jimmy Shaaban, Omar Beltran, Vicenc Carpenter, Paul Ayguade, Eduard Labarta Mancho, Jesus Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9783031125973;9783031125966

State-of-the-art programming approaches generally have a strict division between intra-node shared memory parallelism and inter-node MPI communication. Tasking with dependencies offers a clean, dependable abstraction for a wide range of hardware and situations within a node, but research on task offloading between nodes is still relatively immature. this paper presents a flexible task offloading extension of the OmpSs-2 programming model, which inherits task ordering from a sequential version of the code and uses a common address space to avoid address translation and simplify the use of data structures with pointers. It uses weak dependencies to enable work to be created concurrently. the program is executed in distributed dataflow fashion, and the runtime system overlaps the construction of the distributed dependency graph, enforces dependencies, transfers data, and schedules tasks for execution. Asynchronous task parallelism avoids synchronization that is often required in MPI+OpenMP tasks. Task scheduling is flexible, and data location is tracked through the dependencies. We wish to enable future work in resiliency, scalability, load balancing and malleability, and therefore release all source code and examples open source.

关键词： Open source software

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Accelerating Brain Simulations with the Fast Multipole Method 28th

Accelerating Brain Simulations with the Fast Multipole Metho...

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28th international conference on parallel and distributed computing (euro-par)

作者： Noettgen, Hannah Czappa, Fabian Wolf, Felix Tech Univ Darmstadt Lab Parallel Programming Darmstadt Germany

ISBN: (纸本)9783031125973;9783031125966

the brain is probably the most complex organ in the human body. To understand processes such as learning or healing after brain lesions, we need suitable tools for brain simulations. the Model of Structural Plasticity offers a solution to that problem. It provides a way to model the brain bottom-up by specifying the behavior of the neurons and using structural plasticity to form the synapses. However, its original formulation involves a pairwise evaluation of attraction kernels, which drastically limits scalability. While this complexity has recently been decreased to O(***(2)n) after reformulating the task as a variant of an nbody problem and solving it using an adapted version of the Barnes-Hut approximation, we propose an even faster approximation based on the fast multipole method (FMM). the fast multipole method was initially introduced to solve pairwise interactions in linear time. Our adaptation achieves this time complexity, and it is also faster in practice than the previous approximation.

关键词： Fast Multipole Method Brain Simulation Structural Plasticity Scalability

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parallelizing Automatic Model Management System for AIOps on Microservice Platforms 27th

Parallelizing Automatic Model Management System for AIOps on...

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27th international european conference on parallel and distributed computing (euro-par)

作者： Chen, Ruibo Wu, Wenjun Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China

ISBN: (纸本)9783031061561;9783031061554

With the gradual increase in the scale of applications based on microservice architecture, the complexity of system operation and maintenance is also significantly increasing. the emergence of AIOps makes it possible to automatically detect the state, allocate the resources, warn and detect the anomaly of the system through some machine learning models. Given dynamic online workloads, the running state of a production microservice system is constantly in flux. therefore, it is necessary to continuously train, encapsulate and deploy models based on the current system status, so that the AIOps model can dynamically adapt to the system environment. To address this problem, this paper proposes a model management pipeline framework for AIOps on microservice platforms, and implements a prototype system based on Kubernetes to verify the framework. the system consists of three components: model training, model packaging and model deploying. parallelization and parameter search are introduced in the model training process to support rapid training of multiple models and automated model hyperparameter tuning. Rapid deployment of models is supported by the model packaging and deploying components. Experiments were performed to verify the prototype system, and the experimental results illustrate the feasibility of the proposed framework. this work provides a valuable reference for the construction of an integrated and streamlined AIOps model management system.

关键词： Model management pipeline AIOps parallel model training Microservice MLOps

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