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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： William Fornaciari Politecnico di Milano � DEIB Italy

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The evolution of High Performance Computing (HPC) has to face with several obstacles, including power/thermal management of the cores and the presence of heterogeneous computing platforms. Within the Euro-HPC project TEXTAROSSA, started in spring 2021, several HPC applications exploiting AI and with the need of processing big chunks of data in a secure context, are properly accelerated by leveraging ad-hoc designed accelerators to be implemented in hardware. Such customized heterogeneity of execution has many benefits, but increases the problem of power management, since the accelerators, possibly generated through high-level-synthesis, are neither providing run-time information on their power consumption, nor allows to control the security of the information flow against implementation attacks. In such scenario, any global power manager / resource orchestrator, can operate only with a partial picture of the overall systems and not in real-time, with the risk of being trapped in poor power optimizations and unbalanced resource exploitation. The goal of the talk is to show how is possible to exploit popular ML techniques for a twofold purpose: •Automatically generate an on-line power monitor, to augment the hardware description of any piece of hardware, in particular that of cores and accelerators, capable to provide on-line power estimated in less that few milliseconds. •Select, in the space of all the possible power monitors, those that are not leaking information that can be used to mount side-channel attacks.

关键词： Hardware Monitoring Technology transfer Side-channel attacks Power system management distributed processing Conferences

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Modeling pre-Exascale AMR parallel I/O Workloads via Proxy Applications

Modeling pre-Exascale AMR Parallel I/O Workloads via Proxy A...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： William F Godoy Jenna Delozier Gregory R Watson Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge TN USA College of Computing Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The present work investigates the modeling of preexascale input/output (I/O) workloads of Adaptive Mesh Refinement (AMR) simulations through a simple proxy application. We collect data from the AMReX Castro framework running on the Summit supercomputer for a wide range of scales and mesh partitions for the hydrodynamic Sedov case as a baseline to provide sufficient coverage to the formulated proxy model. The non-linear analysis data production rates are quantified as a function of a set of input parameters such as output frequency, grid size, number of levels, and the Courant-Friedrichs-Lewy (CFL) condition number for each rank, mesh level and simulation time step. Linear regression is then applied to formulate a simple analytical model which allows to translate AMReX inputs into MACSio proxy I/O application parameters, resulting in a simple “kernel” approximation for data production at each time step. Results show that MACSio can simulate actual AMReX nonlinear “static” I/O workloads to a certain degree of confidence on the Summit supercomputer using the present methodology. The goal is to provide an initial level of understanding of AMR I/O workloads via lightweight proxy applications models to facilitate autotune data management strategies in anticipation of exascale systems.

关键词： Analytical models Adaptation models Time-frequency analysis Production Machine learning Predictive models Hydrodynamics

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AI for Datacenter Optimization (ADOPT'22)

AI for Datacenter Optimization (ADOPT'22)

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Stephanie Brink

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers and commercial cloud systems. This has led to a new focus on approaches to optimized resource usage, allocations and deployment of new AI frameworks. With these changes, there is a need to better understand HPC/cloud/datacenter operations with the goal of developing improved scheduling policies, identifying inefficiencies in resource utilization, energy/power consumption, failure prediction, as well as identifying policy violations. Simultaneously, there is a growing interest in addressing the increasing power requirements for AI training and inference operations in HPC/Cloud environments with the goal of minimizing the accompanying effects on climate change. There are several publicly available datasets such as the Argonne Leadership Computing Facility dataset, Blue Waters System Monitoring dataset, Philly traces by Microsoft Inc., the Google Cluster Usage traces and the Atlas cluster trace repository that can enable this research. In addition, the recently released MIT Supercloud Dataset includes monitoring logs from the MIT Supercloud system which include time series of CPU and GPU usage by jobs, memory usage, file system logs, and physical monitoring data. This workshop will focus on AI/ML approaches to datacenter operations, power/energy modeling, scheduling, optimization and monitoring tools. In addition to these topics, this workshop solicits papers that address any of the challenges in enabling large-scale AI in HPC/Cloud environments, including:

关键词： Artificial intelligence Conferences Monitoring Training Optimization distributed processing Resource management

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An Architecture Interface and Offload Model for Low-Overhead, Near-Data, distributed Accelerators 22

An Architecture Interface and Offload Model for Low-Overhead...

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Proceedings of the 55th Annual IEEE/ACM international symposium on Microarchitecture

作者： Saambhavi Baskaran Mahmut Taylan Kandemir Jack Sampson Pennsylvania State University

ISBN: (纸本)9781665462723

The performance and energy costs of coordinating and performing data movement have led to proposals adding compute units and/or specialized access units to the memory hierarchy. However, current on-chip offload models are restricted to fixed compute and access pattern types, which limits software-driven optimizations and the applicability of such an offload interface to heterogeneous accelerator resources. This paper presents a computation offload interface for multi-core systems augmented with distributed on-chip accelerators. With energy-efficiency as the primary goal, we define mechanisms to identify offload partitioning, create a low-overhead execution model to sequence these fine-grained operations, and evaluate a set of workloads to identify the complexity needed to achieve distributed near-data *** demonstrate that our model and interface, combining features of dataflow in parallel with near-data processing engines, can be profitably applied to memory hierarchies augmented with either specialized compute substrates or lightweight near-memory cores. We differentiate the benefits stemming from each of elevating data access semantics, near-data computation, inter-accelerator coordination, and compute/access logic specialization. Experimental results indicate a geometric mean (energy efficiency improvement; speedup; data movement reduction) of (3.3; 1.59; 2.4)×, (2.46; 1.43; 3.5)× and (1.46; 1.65; 1.48)× compared to an out-of-order processor, monolithic accelerator with centralized accesses and monolithic accelerator with decentralized accesses, respectively. Evaluating both lightweight core and CGRA fabric implementations highlights model flexibility and quantifies the benefits of compute specialization for energy efficiency and speedup at 1.23× and 1.43×, respectively.

关键词： distributed accelerator near-data offload energy efficiency heterogeneous architecture interface

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From global choreographies to verifiable efficient distributed implementations

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JOURNAL OF LOGICAL AND ALGEBRAIC METHODS IN PROGRAMMING 2020年 115卷

作者： Jaber, Mohamad Falcone, Ylies Attie, Paul Khalil, Al-Abbass Hallal, Rayan El-Hokayem, Antoine Amer Univ Beirut Comp Sci Dept Beirut Lebanon Univ Grenoble Alpes CNRS INRIA Grenoble INPLab Informat Grenoble Grenoble France Augusta Univ Sch Comp & Cyber Sci Augusta GA USA

We define a method to automatically synthesize efficient distributed implementations from high-level global choreographies. A global choreography describes the execution and communication logic between a set of provided processes which are described by their interfaces. At the choreography level, the operations include multiparty communications, choice, loop, and branching. A choreography is master triggered: it has one master to trigger its execution. This allows us to automatically generate conflict-free distributed implementations without controllers. The behavior of the synthesized implementations follows the behavior of choreographies. In addition, the absence of controllers ensures the efficiency of the implementation and reduces the communication needed at runtime. Moreover, we define a translation of the distributed implementations to equivalent Promela versions. The translation allows verifying the distributed system against behavioral properties. We implemented a Java prototype to validate the approach and applied it to automatically synthesize micro-service architectures. We also illustrate our method on the automatic synthesis of a verified distributed buying system. (C) 2020 Elsevier Inc. All rights reserved.

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A High-Performance distributed Relational Database System for Scalable OLAP Processing 33

A High-Performance Distributed Relational Database System fo...

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33rd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Arnold, Jason Glavic, Boris Raicu, Ioan IIT Chicago IL 60616 USA

ISBN: (纸本)9781728112466

The scalability of systems such as Hive and Spark SQL that are built on top of big data platforms have enabled query processing over very large data sets. However, the per-node performance of these systems is typically low compared to traditional relational databases. Conversely, Massively parallel Processing (MPP) databases do not scale as well as these systems. We present HRDBMS, a fully implemented distributed shared-nothing relational database developed with the goal of improving the scalability of OLAP queries. HRDBMS achieves high scalability through a principled combination of techniques from relational and big data systems with novel communication and work-distribution techniques. While we also support serializable transactions, the system has not been optimized for this use case. HRDBMS runs on a custom distributed and asynchronous execution engine that was built from the ground up to support highly parallelized operator implementations. Our experimental comparison with Hive, Spark SQL, and Greenplum confirms that HRDBMS's scalability is on par with Hive and Spark SQL (up to 96 nodes) while its per-node performance can compete with MPP databases like Greenplum.

关键词： SQL big data distributed query processing

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Pacon: Improving Scalability and Efficiency of Metadata Service through Partial Consistency 34

Pacon: Improving Scalability and Efficiency of Metadata Serv...

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34th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Liu, Yubo Lu, Yutong Chen, Zhiguang Zhao, Ming Sun Yat Sen Univ Guangzhou Peoples R China Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9781728168760

Traditional distributed file systems (DFS) use centralized service to manage metadata. Many studies based on this centralized architecture enhanced metadata processing capability by scaling the metadata server cluster, which is however still difficult to keep up with the growing number of clients and the increasingly metadata-intensive applications. Some solutions abandoned the centralized metadata service and improved scalability by embedding a private metadata service in an HPC application, but these solutions are suitable for only some specific applications and the absence of global namespace makes data sharing and management difficult. This paper addresses the shortcomings of existing studies by optimizing the consistency model of client-side metadata cache for the HPC scenario using a novel partial consistency model. It provides the application with strong consistency guarantee for only its workspace, thus improving metadata scalability without adding hardware or sacrificing the versatility and manageability of DFSes. In addition, the paper proposes batch permission management to reduce path traversal overhead, thereby improving metadata processing efficiency. The result is a library (Pacon) that allows existing DFSes to achieve partial consistency for scalable and efficient metadata management. The paper also presents a comprehensive evaluation using intensive benchmarks and representative application. For example, in file creation, Pacon improves the performance of BeeGFS by more than 76.4 times, and outperforms the state-of-the-art metadata management solution (IndexFS) by more than 4.6 times.

关键词： metadata scalability efficiency distributed file system consistency

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3rd international symposium on Signal and Image Processing, ISSIP 2020

3rd International Symposium on Signal and Image Processing, ...

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3rd international symposium on Signal and Image Processing, ISSIP 2020

ISBN: (纸本)9789813369658

The proceedings contain 16 papers. The special focus in this conference is on Signal and Image Processing. The topics include: Deep Convolutional Neural Network-Based Diagnosis of Invasive Ductal Carcinoma;speaker Identification in Spoken Language Mismatch Condition: An Experimental Study;Ultrasound Image Classification Using ACGAN with Small Training Dataset;preface;Chaotic Ions Motion Optimization (CIMO) for Biological Sequences Local Alignment: COVID-19 as a Case Study;assessment of Eyeball Movement and Head Movement Detection Based on Reading;using Hadoop Ecosystem and Python to Explore Climate Change;a Brief Review of Intelligent Rule Extraction Techniques;the Effect of Different Feature Selection Methods for Classification of Melanoma;intelligent Hybrid Technique to Secure Bluetooth Communications;parallel Algorithm to find Integer k where a given Well-distributed Graph is k-Metric Dimensional;a Fog-Based Retrieval of Real-Time Data for Health Applications;differential Evolution-Based Shot Boundary Detection Algorithm for Content-Based Video Retrieval;qutrit-Based Genetic Algorithm for Hyperspectral Image Thresholding.

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Maintenance Optimization of Multi-State systems with Single Maintenance Capacity and Arbitrarily distributed Maintenance Time

Maintenance Optimization of Multi-State Systems with Single ...

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international symposium on Advanced Reliability and Maintenance Modeling (APARM)

作者： Chen, Yiming Jiang, Tao Liu, Yu Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu Peoples R China

ISBN: (纸本)9781728171029

In engineering practices, a system can continuously operate while some components are being repaired. On the other hand, due to limited maintenance capacity, such as manpower and/or repair facility, maintenance actions can only be executed in serial fashion rather than in parallel. In this study, a new maintenance optimization problem for multi-state systems with single maintenance capacity is studied. In contrast to the exponential assumption for the distribution of maintenance time in most reported works, the time for each maintenance task can be arbitrarily distributed in our study. An optimization problem is formulated by treating the expected performance capacity of a system as an objective function. An illustrative example is given to demonstrate the effectiveness of the proposed method.

关键词： multi-state systems limited maintenance capacity arbitrarily distributed maintenance time embedded Markov chain

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Bandwidth Cost of Code Conversions in the Split Regime

Bandwidth Cost of Code Conversions in the Split Regime

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IEEE international symposium on Information Theory

作者： Francisco Maturana K. V. Rashmi Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

distributed storage systems must store large amounts of data over long periods of time. To avoid data loss due to device failures, an [n, k] erasure code is used to encode k data symbols into a codeword of n symbols that are stored across different devices. However, device failure rates change throughout the life of the data, and tuning n and k according to these changes has been shown to save significant storage space. Code conversion is the process of converting multiple codewords of an initial [n I , k I ] code into codewords of a final [n F , k F ] code that decode to the same set of data symbols. In this paper, we study conversion bandwidth, defined as the total amount of data transferred between nodes during conversion. In particular, we consider the case where the initial and final codes are MDS and a single initial codeword is split into several final codewords (k I = λ F k F for integer λ F ≥ 2), called the split regime. We derive lower bounds on the conversion bandwidth in the split regime and propose constructions that significantly reduce conversion bandwidth and are optimal for certain *** extended version of this paper is available at [1].

关键词： Codes Costs Symbols distributed databases Bandwidth Tuning

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