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检索条件"任意字段=2018 IEEE/ACM Machine Learning in HPC Environments, MLHPC 2018"

共 58 条记录，以下是41-50 订阅

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Virtual Savant for the Heterogeneous Computing Scheduling Problem 16

Virtual Savant for the Heterogeneous Computing Scheduling Pr...

引用

International Conference on High Performance Computing and Simulation (hpcS)

作者： Massobrio, Renzo Dorronsoro, Bernabe Nesmachnow, Sergio Univ Republica Montevideo Uruguay Univ Cadiz Cadiz Spain

ISBN: (纸本)9781538678794

A key issue when using distributed computing environments is finding a planning strategy to execute tasks in order to use the computational resources efficiently. This article presents the application of Virtual Savant to solve the heterogeneous computing scheduling problem, a widely-studied problem with several real-world applications. Virtual Savant is a novel method that uses machine learning techniques to automatically generate programs that can be executed in parallel to solve a given problem. Experimental analysis is performed on a set of problem instances generated following methodologies from the related literature. Results show that Virtual Savant is able to outperform MinMin, a well-known heuristic for the studied problem, by up to 15% while showing good scalability properties when increasing the number of computing resources and the dimension of the problem instances.

关键词： virtual savant scheduling machine learning optimization automatic program generation

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learning Network Flow Based on Rough Set Flow Graphs and ACO Clustering in Distributed Cognitive environments 1

Learning Network Flow Based on Rough Set Flow Graphs and ACO...

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1st acm/ieee International Workshop on Software Engineering for Cognitive Services (SE4COG)

作者： Lewicki, Arkadiusz Eberbach, Eugene Univ Informat Technol & Management Rzeszow Poland Machine Learning & Robot Engn Seekonk MA USA

ISBN: (纸本)9781450357401

This paper presents the use of a modified collective behavior strategy of ant colonies to find approximate sets in the multi-objective optimization problem. The currently used methods search for non-dominated solutions, which takes place directly on the basis of definitions in the previously generated finite set of admissible ratings, searching in the space of goals by analyzing active constraints, solving optimization tasks in terms of all subsequent individual optimization criteria and adopting optimization criteria in order to form a substitute criterion of optimization in the form of a combination of linear criteria with appropriately selected weighting factors. However, these methods are ineffective in many cases. Therefore, the authors of the article propose a new approach based on the use of rough sets flow graphs to control the strategy of communicating artificial ants in distributed cognitive environments. The use of this approach allows to maximize the number of generated solutions and finding non-dominated solutions for the multiple objectives.

关键词： Cognitive systems services Network Flow algorithms Rough Sets flow graphs ACO clustering

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Accelerator-centric Deep learning Systems for Enhanced Scalability, Energy-Efficiency, and Programmability 18

Accelerator-centric Deep Learning Systems for Enhanced Scala...

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23rd Asia and South Pacific Design Automation Conference (ASP-DAC)

作者： Rhu, Minsoo POSTECH Pohang South Korea

ISBN: (纸本)9781509006021

Deep learning (DL) has been successfully deployed in various application domains ranging from computer vision, speech recognition, to natural language processing. As the network models and the datasets used to train these models scale, system architects are faced with new challenges in designing a scalable and energy-efficient high-performance computing (hpc) system for training DL algorithms. One of the key obstacles that DL researchers are facing is the memory capacity bottleneck, where the limited physical memory size of the PCIe-attached DL accelerator (whether it be a discrete GPU or ASIC accelerator like Google's Tensor Processing Unit) constrains the algorithm that can be studied. In this paper, and the associated invited special session talk, we first discuss recent research literature geared towards designing scalable hpc systems for DL. In this context, we then discuss the memory capacity wall problem and introduce the work on virtualized deep neural networks, a memory virtualization solution that systematically reduces the memory consumption of DNN training. We conclude by providing projections on future challenges DNN memory virtualization will encounter upon and suggest accelerator-centric DL system as a promising research direction for the development of a scalable and energy-efficient deep learning system architecture.

关键词： Training Memory management Computational modeling Throughput Acceleration Bandwidth machine learning

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Task Runtime Prediction in Scientific Workflows Using an Online Incremental learning Approach 11

Task Runtime Prediction in Scientific Workflows Using an Onl...

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11th ieee/acm International Conference on Utility and Cloud Computing (UCC-Companion) / 5th ieee/acm International Conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Hilman, Muhammad Hafizhuddin Rodriguez, Maria A. Buyya, Rajkumar Univ Melbourne Sch Comp & Informat Syst Cloud Comp & Distributed Syst CLOUDS Lab Melbourne Vic Australia

ISBN: (纸本)9781538655047

Many algorithms in workflow scheduling and resource provisioning rely on the performance estimation of tasks to produce a scheduling plan. A profiler that is capable of modeling the execution of tasks and predicting their runtime accurately, therefore, becomes an essential part of any Workflow Management System (WMS). With the emergence of multi-tenant Workflow as a Service (WaaS) platforms that use clouds for deploying scientific workflows, task runtime prediction becomes more challenging because it requires the processing of a significant amount of data in a near real-time scenario while dealing with the performance variability of cloud resources. Hence, relying on methods such as profiling tasks' execution data using basic statistical description (e.g., mean, standard deviation) or batch offline regression techniques to estimate the runtime may not be suitable for such environments. In this paper, we propose an online incremental learning approach to predict the runtime of tasks in scientific workflows in clouds. To improve the performance of the predictions, we harness fine-grained resources monitoring data in the form of time-series records of CPU utilization, memory usage, and I/O activities that are reflecting the unique characteristics of a task's execution. We compare our solution to a state-of-the-art approach that exploits the resources monitoring data based on regression machine learning technique. From our experiments, the proposed strategy improves the performance, in terms of the error, up to 29.89%, compared to the state-of-the-art solutions.

关键词： task runtime prediction online incremental learning scientific workflow

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Proceedings - 2018 3rd acm/ieee Symposium on Edge Computing, SEC 2018

Proceedings - 2018 3rd ACM/IEEE Symposium on Edge Computing,...

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3rd acm/ieee Symposium on Edge Computing, SEC 2018

ISBN: (纸本)9781538694459

The proceedings contain 57 papers. The topics discussed include: Vigilia: securing smart home edge computing;learning from differentially private neural activations with edge computing;harpocrates: giving out your secrets and keeping them too;application-aware IoT camera virtualization for video analytics edge computing;edge-based discovery of training data for machine learning;bandwidth-efficient live video analytics for drones via edge computing;Aggio: a coupon safe for privacy-preserving smart retail environments;from cell towers to smart street lamps: placing cloudlets on existing urban infrastructures;scalable edge computing for low latency data dissemination in topic-based publish/subscribe;dependency mining for service resilience at the edge;cooperative-competitive task allocation in edge computing for delay-sensitive social sensing;portable energy-aware cluster-based edge computers;and scalability and performance evaluation of edge cloud systems for latency constrained applications.

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Robust Object Estimation using Generative-Discriminative Inference for Secure Robotics Applications 18

Robust Object Estimation using Generative-Discriminative Inf...

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37th ieee/acm International Conference on Computer-Aided Design (ICCAD)

作者： Liu, Yanqi Costantini, Alessandro Bahar, R. Iris Sui, Zhiqiang Ye, Zhefan Lu, Shiyang Jenkins, Odest Chadwicke Brown Univ Sch Engn Providence RI 02912 USA Univ Michigan Dept Comp Sci & Engn Ann Arbor MI 48109 USA

ISBN: (纸本)9781450359504

Convolutional neural networks (CNNs) are of increasing widespread use in robotics, especially for object recognition. However, such CNNs still lack several critical properties necessary for robots to properly perceive and function autonomously in uncertain, and potentially adversarial, environments. In this paper, we investigate factors for accurate, reliable, and resource -efficient object and pose recognition suitable for robotic manipulation in adversarial clutter. Our exploration is in the context of a three-stage pipeline of discriminative CNN -based recognition, generative probabilistic estimation, and robot manipulation. This pipeline proposes using a SAmpling Network Density filter, or SAND filter, to recover from potentially erroneous decisions produced by a CNN through generative probabilistic inference. We present experimental results from SAND filter perception for robotic manipulation in tabletop scenes with both benign and adversarial clutter. These experiments vary CNN model complexity for object recognition and evaluate levels of inaccuracy that can be recovered by generative pose inference. This scenario is extended to consider adversarial environmental modifications with varied lighting, occlusions, and surface modifications.

关键词： Robust machine learning Robot perception DNN adversarial attack

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Dynamic Bit-width Reconfiguration for Energy-Efficient Deep learning Hardware 18

Dynamic Bit-width Reconfiguration for Energy-Efficient Deep ...

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23rd ieee/acm International Symposium on Low Power Electronics and Design (ISLPED)

作者： Pagliari, Daniele Jahier Macii, Enrico Poncino, Massimo Politecn Torino Turin Italy

ISBN: (纸本)9781450357043

Deep learning models have reached state of the art performance in many machine learning tasks. Benefits in terms of energy, bandwidth, latency, etc., can be obtained by evaluating these models directly within Internet of Things end nodes, rather than in the cloud. This calls for implementations of deep learning tasks that can run in resource limited environments with low energy footprints. Research and industry have recently investigated these aspects, coming up with specialized hardware accelerators for low power deep learning. One effective technique adopted in these devices consists in reducing the bit-width of calculations, exploiting the error resilience of deep learning. However, bit-widths are tipically set statically for a given model, regardless of input data. Unless models are retrained, this solution invariably sacrifices accuracy for energy efficiency. In this paper, we propose a new approach for implementing input-dependant dynamic bit-width reconfiguration in deep learning accelerators. Our method is based on a fully automatic characterization phase, and can be applied to popular models without retraining. Using the energy data from a real deep learning accelerator chip, we show that 50% energy reduction can be achieved with respect to a static bit-width selection, with less than 1% accuracy loss.

关键词： Energy-efficiency Deep learning Energy-quality tradeoff

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A Reinforcement learning-based Approach for the Risk Management of e-Health environments: A Case Study 14

A Reinforcement Learning-based Approach for the Risk Managem...

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14th International Conference on Signal Image Technology & Internet Based Systems (SITIS)

作者： Coronato, Antonio Paragliola, Giovanni Naeem, Muddasar de Pietro, Giuseppe CNR ICAR Naples Italy Univ Parthenope CNR ICAR Dept Engn Naples Italy

ISBN: (纸本)9781538693858

Modern medical software systems are often classified as medical devices and governed by regulations which require stringent risk safety activities to be implemented to minimize the occurrence of risky events. This paper proposes a Reinforcement learning (RL based approach for training a software agent for risk management of medical software systems. The goal of the RL agent is to avoid that a patient enters in dangerous and undesirable states. At the same time, the agent must be able to reach on a safe state or an exit in a minimum interval of time.

关键词： Reinforcement learning Risk Management Software Agent machine learning e-Health

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Achieving Performance and Programmability for MapReduce(-like) Frameworks 25

Achieving Performance and Programmability for MapReduce(-lik...

引用

25th ieee International Conference on High Performance Computing, Data and Analytics (HiPC)

作者： Guo, Jia Agrawal, Gagan Ohio State Univ Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781538683866

Programmability and performance are often considered alternatives in the context of hpc programming systems. For example, general purpose frameworks like MPI are associated with high performance, and though MapReduce and similar frameworks have demonstrated high programmability, it is also well accepted that they fall short in terms of performance. Providing abstractions that maintain high programmability and performance remains an open question. In this paper, we introduce two different variations of the original MapReduce API. We demonstrate efficient implementations of them together with the original MapReduce API as part of the same framework, focusing on how the API differences impact middleware implementation, and examine the resulting performance. Furthermore, to understand how application characteristics impact relative performance of the three systems, we develop and validate a performance model. Overall, we show that a MapReduce-like API that only requires small additional effort from programmers can provide high performance, outperforming Spark significantly.

关键词： Programming Data analysis Sparks Clustering algorithms Commutation Market research machine learning

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CALOREE: learning Control for Predictable Latency and Low Energy 18

CALOREE: Learning Control for Predictable Latency and Low En...

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23rd International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Mishra, Nikita Imes, Connor Laferty, John D. Hofmann, Henry Univ Chicago Chicago IL 60637 USA Yale Univ New Haven CT 06520 USA

ISBN: (纸本)9781450349116

Many modern computing systems must provide reliable latency with minimal energy. Two central challenges arise when allocating system resources to meet these conlicting goals: (1) complexity-modern hardware exposes diverse resources with complicated interactions-and (2) dynamics-latency must be maintained despite unpredictable changes in operating environment or input. machine learning accurately models the latency of complex, interacting resources, but does not address system dynamics;control theory adjusts to dynamic changes, but struggles with complex resource interaction. We therefore propose CALOREE, a resource manager that learns key control parameters to meet latency requirements with minimal energy in complex, dynamic environments. CALOREE breaks resource allocation into two sub-tasks: learning how interacting resources affect speedup, and controlling speedup to meet latency requirements with minimal energy. CALOREE defines a general control system-whose parameters are customized by a learning framework-while maintaining control-theoretic formal guarantees that the latency goal will be met. We test CALOREE's ability to deliver reliable latency on heterogeneous ARM big. LITTLE architectures in both single and multi-application scenarios. Compared to the best prior learning and control solutions, CALOREE reduces deadline misses by 60% and energy consumption by 13%.

关键词： machine learning control theory real-time systems energy heterogeneous architectures resource allocation

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