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ObsCon: Integrated Monitoring and Control for parallel, Real-Time applications

ObsCon: Integrated Monitoring and Control for Parallel, Real...

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IEEE international conference on Cluster Computing

作者： Alan Nussbaum Shwetha Mathangi Chandra Choodamani Karsten Schwan College of Computing Georgia Institute of Technology Atlanta GA

A large class of emerging compute-intensive applications demand real-time or near real-time processing guarantees on streaming data. Sensor processing in particular, has stringent latency requirements for carrying out its digital processing for rapidly incoming radar data streams. The consequent demands on the cluster middleware used to run such codes include (i) efficient online observation of current application performance, coupled with (ii) highly responsive controllers able to dynamically adjust the application's input-and data-dependent runtime behavior. We present the Obs(erver)Con(troller) software for online monitoring and control, which based on specifications of acceptable application states and tunable knobs within the execution environment, ensures that application performance falls within acceptable limits. ObsCon topologies are dynamic, making possible the runtime association of ObsCon methods with arbitrary DAG-structured, distributed/parallel stream processing applications running on high end cluster machines. This paper describes the ObsCon software and its 'grey box' use with a high performance cluster code that exports to ObsCon select 'hooks' for online monitoring and control -- Adaptive Digital Beamforming for a phase-array radar system.

关键词： Monitoring Peer-to-peer computing Runtime Topology Real-time systems Middleware Radar

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High level synthesis of RDF queries for graph analytics 15

High level synthesis of RDF queries for graph analytics

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IEEE international conference on Computer-Aided Design

作者： Vito Giovanni Castellana Marco Minutoli Alessandro Morari Antonino Tumeo Marco Lattuada Fabrizio Ferrandi Pacific Northwest National Laboratory Richland WA USA Politecnico di Milano - DEIB Milano Italy

ISBN: (纸本)9781467383899

In this paper we present a set of techniques that enable the synthesis of efficient custom accelerators for memory intensive, irregular applications. To address the challenges of irregular applications (large memory footprint, unpredictable fine-grained data accesses, and high synchronization intensity), and exploit their opportunities (thread level parallelism, memory level parallelism), we propose a novel accelerator design that employs an adaptive and distributed Controller (DC) architecture, and a Memory Interface Controller (MIC) that supports concurrent and atomic memory operations on a multi-ported/multi-banked shared memory. Among the multitude of algorithms that may benefit from our solution, we focus on the acceleration of graph analytics applications and, in particular, on the synthesis of SPARQL queries on Resource Description Framework (RDF) databases. We achieve this objective by incorporating the synthesis techniques into Bambu, an Open Source high-level synthesis tools, and interfacing it with GEMS, the Graph database Engine for Multithreaded Systems. The GEMS' front-end generates optimized C implementations of the input queries, modeled as graph pattern matching algorithms, which are then automatically synthesized by Bambu. We validate our approach by synthesizing several SPARQL queries from the Lehigh University Benchmark (LUBM).

关键词： parallel processing Databases Field programmable gate arrays Acceleration Program processors Resource description framework Computer architecture

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Experimental Evaluation of Memory Optimizations on an Embedded GPU Platform

Experimental Evaluation of Memory Optimizations on an Embedd...

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international conference on P2P, parallel, Grid, Cloud and Internet Computing (3PGCIC)

作者： Innocenzo Mungiello University of Naples Federico II

ISBN: (纸本)9781467394741

This paper presents the experimental evaluation of data mapping techniques in the shared memory of an embedded GPU. The evaluated technique, previously presented in the literature in other contexts, aims at partitioning an array across the shared memory physical banks, so as to increase parallel accesses, resulting in appreciable gains in terms of both performance and energy efficiency. The paper presents the experimental setup used for characterizing physically the behaviour of the platform, allowing a validation and a closer understanding of the evaluated memory mapping technique.

关键词： Graphics processing units Instruction sets distributed databases Arrays Optimization Field programmable gate arrays

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Tardis: Time Traveling Coherence Algorithm for distributed Shared Memory

Tardis: Time Traveling Coherence Algorithm for Distributed S...

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international conference on parallel Architecture and Compilation techniques (PACT)

作者： Xiangyao Yu Srinivas Devadas CSAIL MIT Cambridge MA USA

A new memory coherence protocol, Tardis, is proposed. Tardis uses timestamp counters representing logical time as well as physical time to order memory operations and enforce sequential consistency in any type of shared memory system. Tardis is unique in that as compared to the widely-adopted directory coherence protocol, and its variants, it completely avoids multicasting and only requires O(log N) storage per cache block for an N-core system rather than O(N) sharer information. Tardis is simpler and easier to reason about, yet achieves similar performance to directory protocols on a wide range of benchmarks run on 16, 64 and 256 cores.

关键词： Protocols Coherence Program processors Clocks Physiology Radiation detectors Multicore processing

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Novel Data-Distribution Technique for Hadoop in Heterogeneous Cloud Environments

Novel Data-Distribution Technique for Hadoop in Heterogeneou...

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international conference on Complex, Intelligent and Software Intensive Systems, CISIS

作者： Vrushali Ubarhande Alina-Madalina Popescu Horacio González-Vélez National College of Ireland Cloud Competency Centre Dublin Ireland

The Hadoop framework has been developed to effectively process data-intensive MapReduce applications. Hadoop users specify the application computation logic in terms of a map and a reduce function, which are often termed MapReduce applications. The Hadoop distributed file system is used to store the MapReduce application data on the Hadoop cluster nodes called Data nodes, whereas Name node is a control point for all Data nodes. While its resilience is increased, its current data-distribution methodologies are not necessarily efficient for heterogeneous distributed environments such as public clouds. This work contends that existing data distribution techniques are not necessarily suitable, since the performance of Hadoop typically degrades in heterogeneous environments whenever data-distribution is not determined as per the computing capability of the nodes. The concept of data-locality and its impact on the performance of Hadoop are key factors, since they affect the performance in the Map phase when scheduling tasks. The task scheduling techniques in Hadoop should arguably consider data locality to enhance performance. Various task scheduling techniques have been analysed to understand their data-locality awareness while scheduling applications. Other system factors also play a major role while achieving high performance in Hadoop data processing. The main contribution of this work is a novel methodology for data placement for Hadoop Data nodes based on their computing ratio. Two standard MapReduce applications, Word Count and Grep, have been executed and a significant performance improvement has been observed based on our proposed data distribution technique.

关键词： Time factors distributed databases Processor scheduling Bandwidth Random access memory Cloud computing Data processing

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Optimization of Resource Allocation and Energy Efficiency in Heterogeneous Cloud Data Centers

Optimization of Resource Allocation and Energy Efficiency in...

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international conference on parallel processing (ICPP)

作者： Amer Qouneh Ming Liu Tao Li Department of Electrical and Computer Engineering University of Florida Gainesville Florida USA

Performance and energy efficiency are major concerns in cloud computing data centers. More often, they carry conflicting requirements making optimization a challenge. Further complications arise when heterogeneous hardware and data center management technologies are combined. For example, heterogeneous hardware such as General Purpose Graphics processing Units (GPGPUs) improve performance at the cost of greater power consumption while virtualization technologies improve resource management and utilization at the cost of degraded performance. In this paper, we focus on exploiting heterogeneity introduced by GPUs to reduce power budget requirements for servers while maintaining performance. To maintain or improve overall server performance at reduced power budget, we propose two enhancements: (a) We borrow power from co-located multithreaded virtual machines (VMs) and reallocate it to GPU VMs. (b) To compensate multi-threaded VMs and re-boost their performance, we propose to borrow virtual computing resources from GPU VMs and reallocate them to CPU VMs. Combining the two techniques minimizes server power budget while maintaining overall server performance. Our results show that server power budget can be reduced by almost 18% at the average cost of 13% performance degradation per virtual machine. In addition, reallocating virtual resources improves the performance of multi-threaded applications by 30% without affecting GPU applications. Combining both techniques reduces server energy consumption by 47 % with minimum performance degradation.

关键词： Graphics processing units Servers Cloud computing Power demand Hardware Virtualization Degradation

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Increasing performance of parallel and distributed systems in high performance computing using weight based approach

Increasing performance of parallel and distributed systems i...

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international conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： I. Arul Jothi P. Indumathy Dept of Computer Science and Engg Jerusalem College of Engineering Anna University Chennai Chennai India

ISBN: (纸本)9781479970766

High performance computing (HPC), large scale instruments and continuously increasing simulation tools are generating data at a huge rate that are difficult to be effectively managed and analyzed. Implementation of MapReduce model provides a way for processing huge volumes of data through the use of large number of commodity computers. MapReduce and Hadoop have been initially used for processing web data. But recently they have been used for processing more complex scientific applications. The proposed system helps to understand the impact of file system, network and programming modes on performance. The performance an application can obtain is largely work load dependent. Design of every MapReduce system has to include the Key factors like High speed, Quick Response, Accurate result. The proposed work is to improve the scheduling and management functionality of parallel and distributed Computing. The proposed technique Weight based Approach improves the performance by improving job scheduling strategy.

关键词： File systems Computers Circuit faults High performance computing Processor scheduling Throughput

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Elastic Stream processing with Latency Guarantees

Elastic Stream Processing with Latency Guarantees

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international conference on distributed Computing Systems

作者： Björn Lohrmann Peter Janacik Odej Kao Technische Universität Berlin

Many Big Data applications in science and industry have arisen, that require large amounts of streamed or event data to be analyzed with low latency. This paper presents a reactive strategy to enforce latency guarantees in data flows running on scalable Stream processing Engines (SPEs), while minimizing resource consumption. We introduce a model for estimating the latency of a data flow, when the degrees of parallelism of the tasks within are changed. We describe how to continuously measure the necessary performance metrics for the model, and how it can be used to enforce latency guarantees, by determining appropriate scaling actions at runtime. Therefore, it leverages the elasticity inherent to common cloud technology and cluster resource management systems. We have implemented our strategy as part of the Nephele SPE. To showcase the effectiveness of our approach, we provide an experimental evaluation on a large commodity cluster, using both a synthetic workload as well as an application performing real-time sentiment analysis on real-world social media data.

关键词： Throughput Runtime Quality of service parallel processing Engines Real-time systems Storms

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Shorter On-Line Warmup for Sampled Simulation of Multi-threaded applications

Shorter On-Line Warmup for Sampled Simulation of Multi-threa...

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international conference on parallel processing (ICPP)

作者： Chuntao Jiang Zhibin Yu Hai Jin Xiaofei Liao Lieven Eeckhout Yonggang Zeng Chengzhong Xu Service Computing Technology and System Lab Huazhong University of Science and Technology China Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Department of Electronics and Information Systems Ghent University Belgium Wayne State University USA

Warm up is a crucial issue in sampled micro architectural simulation to avoid performance bias by constructing accurate states for micro-architectural structures before each sampling unit. Not until very recently have researchers proposed Time-Based Sampling (TBS) for the sampled simulation of multi-threaded applications. However, warm up in TBS is challenging and complicated, because (i) full functional warm up in TBS causes very high overhead, limiting overall simulation speed, (ii) traditional adaptive functional warm up for sampling single-threaded applications cannot be readily applied to TBS, and (iii) check pointing is inflexible (even invalid) due to the huge storage requirements and the variations across different runs for multi-threaded applications. In this work, we propose Shorter On-Line (SOL) warm up, which employs a two-stage strategy, using 'prime' warm up in the first stage, and an extended 'No-State-Loss (NSL)' method in the second stage. SOL is a single-pass, on-line warm up technique that addresses the warm up challenges posed in TBS in parallel simulators. SOL is highly accurate and efficient, providing a good trade-off between simulation accuracy and speed, and is easily deployed to different TBS techniques. For the PARSEC benchmarks on a simulated 8-core system, two state-of-the-art TBS techniques with SOL warm up provide a 7.2× and 37× simulation speedup over detailed simulation, respectively, compared to 3.1× and 4.5× under full warm up. SOL sacrifices only 0.3% in absolute execution time prediction accuracy on average.

关键词： Adaptation models Computational modeling Checkpointing Hardware Benchmark testing Acceleration Measurement

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Revealing Potential Performance Improvements by Utilizing Hybrid Work-Sharing for Resource-Intensive Seismic applications

Revealing Potential Performance Improvements by Utilizing Hy...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Patrick Siegl Rainer Buchty Mladen Berekovic Chair for Chip Design for Embedded Computing (C3E) Technische Universität Carolo-Wilhelminazu Braunschweig

Heterogeneous system architectures are becoming more and more of a commodity in the scientific community. While it remains challenging to fully exploit such architectures, the benefits in performance and hybrid speed-up, by using a host processor and accelerators in parallel in a non-monolithic matter, are significant. Hereby, the energy efficiency is becoming an increasingly critical challenge for future high-performance computing (HPC) systems, which do want to exceed the Exascale barrier with several competing architecture concepts ranging from high-performance CPUs, combined with GPUs acting as floating-point accelerators, to computationally weak CPUs, paired with dedicated and highly-perform ant FPGA-based accelerators. In this paper, we realize and evaluate a hybrid computing approach based on a two-dimensional seismic streaming algorithm with several heterogeneous system architectures, including conventional HPC approaches based on powerful CPUs and GPUs. Furthermore, we elaborate the effort on an embedded system platform claiming to be a "mini supercomputer" [1]. Several CPU and accelerator combinations are utilized in a manual work-sharing manner with the aim of achieving significant performance speed-ups and a detailed energy-efficiency study. Based on roofline models and experimental evaluations, the paper provides an insight into the fact that hybrid computing is mostly unconditionally beneficial for balanced systems regarding the performance as well as the energy efficiency, aiding the programmer in the decision whether or not costly, manually tuned, homogeneous implementations are worthwhile.

关键词： Graphics processing units Computer architecture Partitioning algorithms Benchmark testing Instruction sets Computational modeling Performance evaluation

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