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检索条件"任意字段=27th International Conference on Parallel and Distributed Computing, Euro-Par 2021"

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Towards Flexible and Compiler-Friendly Layer Fusion for CNNs on Multicore CPUs 27th

Towards Flexible and Compiler-Friendly Layer Fusion for CNNs...

引用

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

作者： Lin, Zhongyi Georganas, Evangelos Owens, John D. Univ Calif Davis Davis CA 95616 USA Intel Corp Parallel Comp Lab Santa Clara CA USA

ISBN: (纸本)9783030856656;9783030856649

In deep learning pipelines, we demonstrate the performance benefits and tradeoffs of combining two convolution layers into a single layer on multicore CPUs. We analyze when and why fusion may result in runtime speedups, and study three types of layer fusion: (a) 3-by-3 depthwise convolution with 1-by-1 convolution, (b) 3-by-3 convolution with 1-by-1 convolution, and (c) two 3-by-3 convolutions. We show that whether fusion is beneficial is dependent on numerous factors, including arithmetic intensity, machine balance, memory footprints, memory access pattern, and the way the output tensor is tiled. We devise a schedule for all these fusion types to automatically generate fused kernels for multicore CPUs through auto-tuning. With more than 30 layers extracted from five CNNs, we achieve a 1.04x geomean with 1.44x max speedup against separate kernels from MKLDNN, and a 1.24x geomean with 2.73x max speed up against AutoTVM-tuned separate kernels in standalone kernel benchmarks. We also show a 1.09x geomean with 1.29x max speedup against TVM, and a 2.09x geomean with 3.35x max speedup against MKLDNN-backed PyTorch, in end-to-end inference tests.

关键词： Layer fusion CNN Multicore CPUs Auto-tuning

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G-Morph: Induced Subgraph Isomorphism Search of Labeled Graphs on a GPU 27th

G-Morph: Induced Subgraph Isomorphism Search of Labeled Grap...

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

作者： Rowe, Bryan Gupta, Rajiv Univ Calif Riverside CSE Dept Riverside CA 92521 USA

ISBN: (纸本)9783030856656;9783030856649

Induced subgraph isomorphism search finds the occurrences of embedded subgraphs within a single large data graph that are strictly isomorphic to a given query graph. Labeled graphs contain object types and are a primary input source to this core search problem, which applies to systems like graph databases for answering queries. In recent years, researchers have employed GPU parallel solutions to this problem to help accelerate runtimes by utilizing the filtering-and-joining framework, which first filters vertices that cannot be part of the solution then joins partial solutions with candidate edges until full isomorphisms are determined. However, the performance of current GPU-based solutions is hindered by limited filtering effectiveness and presence of extraneous computations. this paper presents G-Morph, a fast GPU-based induced subgraph isomorphism search system for labeled graphs. Our filtering-and-joining system parallelizes both phases by upfront eliminating irrelevant vertices via a novel space-efficient vertex signature hashing strategy and efficiently joining partial solutions through use of a novel sliding window algorithm (slide-join) that provides overflow protection for larger input. Together these techniques greatly reduce extraneous computations by reducing Cartesian products and improving edge verification while supporting large scan operations (split-scan). G-Morph outperforms the state-of-the-art GPU-based GSI and CPU-based VF3 systems on labeled real-world graphs achieving speedups of up to 15.78x and 43.56x respectively.

关键词： Graph databases Subgraph isomorphism Breadth-first search GPU programming parallel computing

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Scalable Random Forest with Data-parallel computing 29th

Scalable Random Forest with Data-Parallel Computing

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

作者： Vazquez-Novoa, Fernando Conejero, Javier Tatu, Cristian Badia, Rosa M. Barcelona Supercomp Ctr BSC CNS Barcelona Spain

ISBN: (纸本)9783031396977;9783031396984

In the last years, there has been a significant increment in the quantity of data available and computational resources. this leads scientific and industry communities to pursue more accurate and efficient Machine Learning (ML) models. Random Forest is a well-known algorithm in the ML field due to the good results obtained in a wide range of problems. Our objective is to create a parallel version of the algorithm that can generate a model using data distributed across different processors that computationally scales on available resources. this paper presents two novel proposals for this algorithm with a data-parallel approach. the first version is implemented using the PyCOMPSs framework and its failure management mechanism, while the second variant uses the new PyCOMPSs nesting paradigm where the parallel tasks can generate other tasks within them. Both approaches are compared between them and against MLlib Apache Spark Random Forest with strong and weak scaling tests. Our findings indicate that while the MLlib implementation is faster when executed in a small number of nodes, the scalability of both new variants is superior. We conclude that the proposed data-parallel approaches to the Random Forest algorithm can effectively generate accurate and efficient models in a distributed computing environment and offer improved scalability over existing methods.

关键词： Random Forest PyCOMPSs COMPSs parallelism distributed computing Dislib Machine Learning HPC

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Rafiki: Task-Level Capacity Planning in distributed Stream Processing Systems 27th

Rafiki: Task-Level Capacity Planning in Distributed Stream P...

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

作者： Pfister, Benjamin J. J. Lickefett, Wolf S. Nitschke, Jan Paul, Sumit Geldenhuys, Morgan K. Scheinert, Dominik Gontarska, Kordian thamsen, Lauritz Tech Univ Berlin Berlin Germany Univ Potsdam Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9783031061561;9783031061554

distributed Stream Processing is a valuable paradigm for reliably processing vast amounts of data at high throughput rates with low end-to-end latencies. Most systems of this type offer a fine-grained level of control to parallelize the computation of individual tasks within a streaming job. Adjusting the parallelism of tasks has a direct impact on the overall level of throughput a job can provide as well as the amount of resources required to provide an adequate level of service. However, finding optimal parallelism configurations that fall within the expected Quality of Service requirements is no small feat to accomplish. In this paper we present Rafiki, an approach to automatically determine optimal parallelism configurations for distributed Stream Processing jobs. Here we conduct a number of proactive profiling runs to gather information about the processing capacities of individual tasks, thereby making the selection of specific utilization targets possible. Understanding the capacity information enables users to adequately provision resources so that streaming jobs can deliver the desired level of service at a reduced operational cost with predictable recovery times. We implemented Rafiki prototypically together with Apache Flink where we demonstrate its usefulness experimentally.

关键词： distributed Stream Processing Capacity planning Resource optimization Quality of Service parallelization Profiling Performance modeling

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Experiments Using a Software-distributed Shared Memory, MPI and 0MQ over Heterogeneous computing Resources

Experiments Using a Software-Distributed Shared Memory, MPI ...

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26th international conference on parallel and distributed computing (euro-par)

作者： Cudennec, Loic Trabelsi, Kods CEA LIST PC 172 F-91191 Gif Sur Yvette France DGA MI Dept Artificial Intelligence BP 7 F-35998 Rennes Armees France

ISBN: (纸本)9783030715939;9783030715922

distributed heterogeneous computing systems escalate the problem of choosing the appropriate programming model. Programming models such as message passing are efficient but require low-level management of communications. Higher level of programming such as shared memory are convenient for the application design but they usually have performance issues. With the recent development of distributed heterogeneous systems and new protocols to access remote memories, there is an opportunity for distributed shared memory systems to offer a satisfying level of abstraction while not giving up on performance. In this paper a video processing application is written using MPI, 0MQ and an in-house software-distributed shared memory (S-DSM) backend and deployed over a set of heterogeneous computing boards. Results show that 0MQ implementation is the most efficient but at the price of writing the application with the targeted platform in mind. the S-DSM implementation runs up to 2 times faster than the pure OpenMPI implementation and competes with 0MQ when the data granularity is small.

关键词： Heterogeneous computing distributed computing distributed shared memory Message passing

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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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DS-Means: distributed Data Stream Clustering

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18th international conference on euro-par parallel Processing

作者： Guerrieri, Alessio Montresor, Alberto Univ Trento Trento Italy

ISBN: (纸本)9783642328206

this paper proposes DS-means, a novel algorithm for clustering distributed data streams. Given a network of computing nodes, each of them receiving its share of a distributed data stream, our goal is to obtain a common clustering under the following restrictions (i) the number of clusters is not known in advance and (ii) nodes are not allowed to share single points of their datasets, but only aggregate information. A motivating example for DS-means is the decentralized detection of botnets, where a collection of independent ISPs may want to detect common threats, but are unwilling to share their precious users' data. In DS-means, nodes execute a distributed version of K-means on each chunk of data they receive to provide a compact representation of the data of the entire network. Later, X-means is executed on this representation to obtain an estimate of the number of clusters. A number of experiments on both synthetic and real-life datasets show that our algorithm is precise, efficient and robust.

关键词： Clustering algorithms

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euro-par 2021: parallel Processing Workshops 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Ricardo Chaves Aleksandar Ilic Dora B. Heras Didem Unat Rosa M. Badia Andrea Bracciali Patrick Diehl Anshu Dubey Oh Sangyoon Stephen L. Scott Laura Ricci

this book constitutes revised selected papers from the workshops held at the 27th international conference on parallel and distributed computing, euro-par 2021, which took place in Portugal, in August 2021. the worksh... 详细信息

ISBN: (数字)9783031061561

ISBN: (纸本)9783031061554

关键词： Computer Systems Organization and Communication Networks Information Systems and Communication Service Operating Systems

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Towards GPGPU Assisted computing in Virtualized Environments

Towards GPGPU Assisted Computing in Virtualized Environments

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16th international euro-par conference on parallel Processing

作者： Schmitt, thilo Weggerle, Alexander Himpel, Christian Schulthess, Peter Univ Ulm Inst Distributed Syst James Franck Ring O-27-3210 D-89069 Ulm Germany

ISBN: (纸本)9783642218781;9783642218774

General Purpose Computation on Graphics Processing Units (GPGPU) makes it possible to use the massive computing power of modern graphics cards for generic high-performance computing. However, the new virtualization technologies will typically not support high-performance graphics cards and as a consequence GPGPU resources can not be used in typical virtualization setups. In this paper we present an approach to introduce accelerated 3D graphics support as well as GPGPU facilities into virtualized environments. We present our proof-of-concept "VirtGL" and discuss architectural considerations and a lean, straight-forward way of implementation. We then give an outlook on how this approach can easily be adopted for virtualizing GPGPU APIs like CUDA, Stream, OpenCL and alike.

关键词： GPGPU virtualization virtualized 3D acceleration High Performance computing

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High Performance Portable Solver for Tridiagonal Toeplitz Systems of Linear Equations

High Performance Portable Solver for Tridiagonal Toeplitz Sy...

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26th international conference on parallel and distributed computing (euro-par)

作者： Dmitruk, Beata Stpiczynski, Przemyslaw Marie Curie Sklodowska Univ Inst Comp Sci Ul Akad 9 PL-20033 Lublin Poland

ISBN: (纸本)9783030715939;9783030715922

We show that recently developed divide and conquer parallel algorithm for solving tridiagonal Toeplitz systems of linear equations can be easily and efficiently implemented for a variety of modern multicore and GPU architectures, as well as hybrid systems. Our new portable implementation that uses OpenACC can be executed on both CPU-based and GPU-accelerated systems. More sophisticated variants of the implementation are suitable for systems with multiple GPUs and it can use CPU and GPU cores. We consider the use of both columnwise and row wise storage formats for two dimensional double precision arrays and show how to efficiently convert between these two formats using cache memory. Numerical experiments performed on Intel CPUs and Nvidia GPUs show that our new implementation achieves relatively good performance.

关键词： Tridiagonal Toeplitz systems parallel algorithms Vectorization Portability OpenACC Hybrid systems

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