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CUDA-For-Clusters: A System for Efficient Execution of CUDA Kernels on Multi-core Clusters

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18th international conference on Euro-Par parallel Processing

作者： Prabhakar, Raghu Govindarajan, R. thazhuthaveetil, Matthew J. Univ Calif Los Angeles Los Angeles CA 90095 USA Indian Inst Sci Supercomp Educ & Res Ctr Bangalore Karnataka India

ISBN: (纸本)9783642328206

Rapid advancements in multi-core processor architectures coupled with low-cost, low-latency, high-bandwidth interconnects have made clusters of multi-core machines a common computing resource. Unfortunately, writing good parallel programs that efficiently utilize all the resources in such a cluster is still a major challenge. Various programming languages have been proposed as a solution to this problem, but are yet to be adopted widely to run performance-critical code mainly due to the relatively immature software framework and the effort involved in re-writing existing code in the new language. In this paper, we motivate and describe our initial study in exploring CUDA as a programming language for a cluster of multi-cores. We develop CUDA-For-Clusters (CFC), a framework that transparently orchestrates execution of CUDA kernels on a cluster of multi-core machines. the well-structured nature of a CUDA kernel, the growing popularity, support and stability of the CUDA software stack collectively make CUDA a good candidate to be considered as a programming language for a cluster. CFC uses a mixture of source-to-source compiler transformations, a work distribution run-time and a light-weight software distributed shared memory to manage parallel executions. Initial results on running several standard CUDA benchmark programs achieve impressive speedups of up to 7.5X on a cluster with 8 nodes, thereby opening up an interesting direction of research for further investigation.

关键词： CUDA Multi-Cores distributed Programming distributed systems Clusters Software distributed Shared Memory

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Performance of the Modern parallel Programming Approaches: A Case Study 18

Performance of the Modern Parallel Programming Approaches: A...

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18th IEEE international conference on Computer Science and Information Technologies, CSIT 2023

作者： Fedynyak, Volodymyr Hryniv, Oleksa Vey, Bohdan Farenyuk, Oleg Ukrainian Catholic University Faculty of Applied Sciences L'viv Ukraine Institute for Condensed Matter Physics National Academy of Sciences of Ukraine L'viv Ukraine

ISBN: (纸本)9798350360462

this research is devoted to a quantitative comparison of the performance of several parallel programming approaches and compares their computational performance. Comparison is performed for the Computational Dynamics Problem solved by the MacCormack scheme. parallel computation properties of this sample problem task are well-understood. the parallel programming techniques were chosen considering the recent trends in high-performance computing. Both high-level framework-based implementations (using OneAPI DPC++ and ArrayFire) and low-level implementations (based on CUDA C++) are reviewed and their performance is compared. Additionally, single SMP systems with multiple CUDA-capable GPUs were studied using GPUDirect and Unified Memory technologies. Wall-time was used as a performance metric. the comparison was performed using Student's t-test for the Gauss-distributed experimental results and the non-parametric Wilcoxon signed-rank test - for other distributions. the results show that CUDA-based solutions outperform other approaches though development time considerations often can favor more high-level approaches. © 2023 IEEE.

关键词： Graphics processing unit

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Application of Edge-to-Cloud Methods Toward Deep Learning 18

Application of Edge-to-Cloud Methods Toward Deep Learning

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IEEE 18th international conference on E-Science (E-Science)

作者： Choudhary, Khushi Nersisyan, Nona Lin, Edward Chandrasekaran, Shobana Mayani, Rajiv Pottier, Loic Murillo, Angela P. Virdone, Nicole K. Kee, Kerk Deelman, Ewa Univ Southern Calif Informat Sci Inst Los Angeles CA 90089 USA Indiana Univ Sch Informat & Comp Bloomington IN 47405 USA Texas Tech Univ Lubbock TX 79409 USA

ISBN: (数字)9781665461245

ISBN: (纸本)9781665461245

Scientific workflows are important in modern computational science and are a convenient way to represent complex computations, which are often geographically distributed among several computers. In many scientific domains, scientists use sensors (e.g., edge devices) to gather data such as CO2 level or temperature, that are usually sent to a central processing facility (e.g., a cloud). However, these edge devices are often not powerful enough to perform basic computations or machine learning inference computations and thus applications need the power of cloud platforms to generate scientific results. this work explores the execution and deployment of a complex workflow on an edge-to-cloud architecture in a use case of the detection and classification of plankton. In the original application, images were captured by cameras attached to buoys floating in Lake Greifensee (Switzerland). We developed a workflow based on that application. the workflow aims to pre-process images locally on the edge devices (i.e., buoys) then transfer data from each edge device to a cloud platform. Here, we developed a Pegasus workflow that runs using HTCondor and leveraged the Chameleon cloud platform and its recent CHI@Edge feature to mimic such deployment and study its feasibility in terms of performance and deployment.

关键词： Scientific Workflows Workflow Management systems Edge computing Pegasus Zooplankton Machine Learning

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Analysis of hierarchical semi-markov processes with parallel regions 18th

Analysis of hierarchical semi-markov processes with parallel...

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18th international GI/ITG conference on Measurement, Modelling and Evaluation of computing systems and Dependability and Fault Tolerance, MMB and DFT 2016

作者： Homm, Daniel German, Reinhard Department of Computer Science 7 University Erlangen-Nuremberg Martensstr. 3 Erlangen91058 Germany

ISBN: (纸本)9783319315584

We consider state charts with generally distributed state sojourn times and with parallel regions in composite states. this corresponds to semi-Markov processes (SMPs) with parallel regions consisting again of SMPs. the concept of parallel regions significantly extends the modeling power: it allows for the specification of non-memoryless activities that take place in parallel on many nested hierarchy levels. parallel regions can be left either by final states or by exit states, corresponding to the maximum and the minimum of the sojourn times in the regions, respectively. therefore, concurrent activities with synchronization and competition can easily be modeled. An SMP with parallel regions cannot simply be analyzed by flattening the state space. We propose an analysis based on a steady-state analysis of an embedded Markov chain (EMC) at the top level and by a transient analysis at the composite state level with a limited computational effort. An expression for the asymptotic complexity of the analysis is also provided. An example SMP containing all modeling features with parallel regions is illustrated. We carry out experiments on basis of this model and confirm the results by simulations. © Springer international Publishing Switzerland 2016.

关键词： Transient analysis

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Striped Data Server for Scalable parallel Data Analysis 18

Striped Data Server for Scalable Parallel Data Analysis

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18th international Workshop on Advanced computing and Analysis Techniques in Physics Research (ACAT)

作者： Chang, Jin Gutsche, Oliver Mandrichenko, Igor Pivarski, James

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the FNAL LDRD Project FNAL-LDRD-2016-032, we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. the representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. the Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and

关键词： High energy physics

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Middleware 2017 - Proceedings of the 2017 international Middleware conference

Middleware 2017 - Proceedings of the 2017 International Midd...

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18th ACM/IFIP/USENIX Middleware conference, Middleware 2017

ISBN: (纸本)9781450347204

the proceedings contain 20 papers. the topics discussed include: HyperDrive: exploring hyperparameters with pop scheduling;sieve: actionable insights from monitored metrics in distributed systems;Rafiki: a middleware for parameter tuning of NoSQL datastores for dynamic metagenomics workloads;Rivulet: a fault-tolerant platform for smart-home applications;binary compatible graphics support in Android for running iOS apps;Sense-Aid: a framework for enabling network as a service for participatory sensing;ORCA: an ORChestration automata for configuring VNFs;improving spark application throughput via memory aware task co-location: a mixture of experts approach;Swayam: distributed autoscaling to meet SLAs of machine learning inference services with resource efficiency;data-driven serverless functions for object storage;a programming model for application-defined multipath TCP scheduling;POLM2: automatic profiling for object lifetime-aware memory management for hotspot big data applications;SPECTRE: supporting consumption policies in window-based parallel complex event processing;efficient covering for top-k filtering in content-based publish/subscribe systems;StreamApprox: approximate computing for stream analytics;X-Search: revisiting private web search using Intel SGX;rectify: black-box intrusion recovery in PaaS Clouds;scheduler activations for interference-resilient SMP virtual machine scheduling;DoubleDecker: a cooperative disk caching framework for derivative clouds;and Ginja: one-dollar cloud-based disaster recovery for databases.

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Real Time Key Frame Extraction through parallel Computation of Entropy Difference 18th

Real Time Key Frame Extraction Through Parallel Computation ...

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18th international conference on Computer Information systems and Industrial Management

作者： Gautam, Nandita Das, Debdoot Khatua, Sunirmal Saha, Banani Univ Calcutta Comp Sci Dept Kolkata 700098 India

ISBN: (纸本)9783030289560;9783030289577

the advancement of image processing in the field of Artificial Intelligence has created various research prospects in the area of object detection, pattern recognition etc. Capturing real time video stream for multiple cameras within a region of interest has become a common phenomenon for an Intelligent Situation Awareness System. Video processing is an important application which is rapidly developing nowadays as an area of extensive research. Content retrieval as well as information collection from a video requires both syntactic and semantic analysis. For a large video data, some set of frames are used to represent the video content. these are identified as key frames. Several algorithms have been defined to extract key frames from a stored video file. the existing algorithms that have been defined for key frame extraction are based on sequential mode. this paper looks into the extraction of key frames for any real time video stream. the experimental results show that there is an effective reduction in the execution time to a huge extent in the case of distributed processing as compared to the sequential processing of frames. In this paper, we propose a distributed framework to regulate the speed of key frame generation for heterogeneous speed of incoming video.

关键词： Real time streaming Key frames parallel computing Entropy

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An efficient parallel optimization for co-authorship network analysis 18

An efficient parallel optimization for co-authorship network...

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18th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2017

作者： Valencio, Carlos Roberto De Freitas, Jose Carlos Gratao De Souza, Rogeria Cristiane Neves, Leandro Alves Donega Zafalon, Geraldo Francisco Colombini, Angelo Cesar Tenorio, William Campus São José Do Rio Preto SP Brazil São Carlos SP Brazil

ISBN: (纸本)9781538631515

Co-authorship analysis in science and technology partnerships provides a vision of cooperation patterns between individuals and organizations and is still widely used to understand and assess scientific collaboration patterns. this analysis is conducted by means of bibliometry, which is the quantitative study of scientific production. However, with the evolution of database management systems, there was a significant increase in the volume of stored data, which could difficult the analysis. In this context, the developed work presents an efficient parallel optimization of bibliometric information, in order to allow this scientific analysis in a Big Data environment. Our results show that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the number of nodes tends to infinity;the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the number of nodes tends to infinity. Also, the results found present good values of speed up and efficiency. © 2017 IEEE.

关键词： Big data

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Measuring distributed Applications through MapReduce and Traffic Analysis

Measuring Distributed Applications through MapReduce and Tra...

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international conference on parallel and distributed systems (ICPADS)

作者： thiago Vieira Paulo Soares Marco Machado Rodrigo Assad Vinicius Garcia Federal University of Pernambuco (UFPE) Recife Pernambuco Brazil

Monitoring and evaluating distributed systems at runtime is a difficult effort. Network traffic analysis may be used to analyse distributed systems, but the capacity to analyse large amount of traffic is a challenge. this work proposes the use of Map Reduce for deeply inspecting distributed systems network traffic, evaluating the effectiveness and the processing capacity of Map Reduce for this inspection.

关键词： Peer to peer computing Inspection Scalability Monitoring Runtime Sockets Hardware

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Queuing local solutions in distributed constraint satisfaction systems

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18th conference of the Canadian Society for Computational Studies of Intelligence: Advances in Artificial Intelligence, Canadian AI 2005

作者： Mueller, Ronnie Havens, William S. Intelligent Systems Laboratory School of Computing Science Simon Fraser University Burnaby BC V5A 1S6 Canada

ISBN: (纸本)3540258647

When solving distributed Constraint Satisfaction Problems (DCSP), it is desirable that the search exploits asynchronism as much as possible so that the employed agents can perform much of the work in parallel. this allows to utilize the processing power available in a distributed environment. However, in many of todays DCSP algorithms, only a few agents are working at any given time and the others are idling. this is caused by the fact that once an agent is consistent with its neighbors, it becomes idling until it is forced by other agents to choose a different assignment for its local variables. In this paper we propose a method that utilizes the idling time of the agents to increase the efficiency of a distributed backtracking algorithm where agents have complex local problems and share variables among them. An agent computes solutions to its local problem in advance while it is waiting for incoming messages. this means that when an agent finds a solution to the local problem that is consistent with higher order agents, it not only informs lower order agents but continuous to search for further solutions which then are stored in a queue. When the current local solution becomes invalid due to a nogood received from a lower order agent, the agent does not have to search for a new local solution but can retrieve a precomputed one from the queue. this approach increases the amount of work the agents can perform in parallel since higher order agents search ahead for local solutions while lower order agents are trying to expand the current partial solution. Our experiments show that some increase in performance can be gained by queuing local solutions in distributed backtracking. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Constraint theory

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