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检索条件"任意字段=2024 International Symposium on Computer Architecture and High Performance Computing Workshops"

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uSystolic: Byte-Crawling Unary Systolic Array 28

uSystolic: Byte-Crawling Unary Systolic Array

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28th Annual IEEE international symposium on high-performance computer architecture (HPCA)

作者： Di Wu San Miguel, Joshua Univ Wisconsin Dept ECE Madison WI 53706 USA

ISBN: (纸本)9781665420273

General matrix multiply (GEMM) is an important operation in broad applications, especially the thriving deep neural networks. To achieve low power consumption for GEMM, researchers have already leveraged unary computing, which manipulates bitstreams with extremely simple logic. However, existing unary architectures are not well generalizable to varying GEMM configurations in versatile applications and incompatible to the binary computing stack, imposing challenges to execute unary GEMM effortlessly. In this work, we address the problem by architecting a hybrid unary-binary systolic array, uSystolic, to inherit the legacy-binary data scheduling with slow (thus power-efficient) data movement, i.e., data bytes are crawling out from memory to drive uSystolic. uSystolic exhibits tremendous area and power improvements as a joint effect of 1) low-power computing kernel, 2) spatial-temporal bitstream reuse, and 3) on-chip SRAM elimination. For the evaluated edge computing scenario, compared with the binary parallel design, the rated-coded uSystolic reduces the systolic array area and total on-chip area by 59.0% and 91.3%, with the on-chip energy and power efficiency improved by up to 112.2x and 44.8x for AlexNet.

关键词： Power demand Processor scheduling Neural networks Random access memory computer architecture Systolic arrays Hybrid power systems

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computing seismic attributes with deep-learning models 35

Computing seismic attributes with deep-learning models

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35th IEEE international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Hecker, Nicolas Napoli, Otavio O. Astudillo, Carlos A. Navarro, Joao Paulo Souza, Alan Miranda, Daniel Villas, Leandro A. Borin, Edson Univ Estadual Campinas UNICAMP Ctr Estudos Energia & Petr CEPETRO Campinas Brazil Univ Estadual Campinas UNICAMP Inst Comp IC Campinas Brazil NVIDIA Sao Paulo Brazil Petr Brasileiro SA PETROBRAS Rio De Janeiro Brazil

ISBN: (纸本)9798350381603

Seismic data contains valuable information about the Earth's subsurface, which is useful in oil and gas (O&G) exploration. Seismic attributes are derived from seismic data to highlight relevant data structures and properties, improving geological or geophysical data interpretation. However, when calculated on large datasets, quite common in the O&G industry, these attributes may be computationally expensive regarding computing power and memory capacity. Deep learning techniques can reduce these costs by avoiding direct attribute calculation. Some of these techniques may, however, be too complex, require large volumes of training data, and demand large computational capacity. This work shows that a conventional U-Net Convolutional Neural Network (CNN) model, with 31 million parameters, can be used to compute diverse seismic attributes directly from seismic data. The F3 dataset and attributes calculated on it were employed to train the models, each specialized in a specific attribute. The trained CNN models yield low prediction errors for most of the tested attributes. These results evince that simple CNN models are able to infer seismic attributes with high accuracy.

关键词： Convolutional neural networks Deep learning Regression model Seismic attribute computation

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Deriving a Methodology for Code Deployment on Multi-Core Platforms via Iterative Manual Optimizations

Deriving a Methodology for Code Deployment on Multi-Core Pla...

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26th IEEE international Parallel and Distributed Processing symposium (IPDPS) / Workshop on high performance Data Intensive computing

作者： McCool, Stuart Milligan, Peter Sage, Paul Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland

ISBN: (纸本)9780769546766

In recent years, there has been what can only be described as an explosion in the types of processing devices one can expect to find within a given computer system. These include the multi-core CPU, the General Purpose Graphics Processing Unit (GPGPU) and the Accelerated Processing Unit (APU), to name but a few. The widespread uptake of these systems presents would-be users with at least two problems. Firstly, each device exposes a complex underlying architecture which must be appreciated in order to attain optimal performance. This is coupled with the fact that a single system can support an arbitrary number of such devices. Consequently, fully leveraging the performance capabilities of such a system must come at a cost - increasingly prolonged development times. Adhering to a methodology will have the significant industrial impact of reducing these development times. This paper describes the continued formulation of such a novel methodology. Two real world scientific programs are optimized for execution on the CUDA platform. Double precision accuracy and optimized speedups (which include PCI-E transfer times) of 15x and 17x are achieved.

关键词： heterogeneous computing GPGPU methodology toolset artificial intelligence characterization Racah Slater

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Local discovery of system architecture - Application parameter sensitivity: An empirical technique for adaptive grid applications 11

Local discovery of system architecture - Application paramet...

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11th IEEE international symposium on high performance Distributed computing (HPDC-11)

作者： Corey, IR Johnson, JR Vetter, JS Lawrence Livermore Natl Lab Comp Applicat & Res Dept Livermore CA USA

ISBN: (纸本)0769516866

This study presents a technique that can significantly improve the performance of a distributed application by allowing the application to locally adapt to architectural characteristics of distinct resources in a distributed system. Application performance is sensitive to system architecture-application parameter pairings. In a distributed or Grid enabled application, a single parameter configuration for the whole application will not always be optimal for every participating resource. In particular, some configurations can significantly degrade performance. Furthermore, the behavior of a system may change during the course of the run. The technique described here provides an automated mechanism for run-time adaptation of application parameters to the local system architecture. Using a scaled-down simulation of a Monte Carlo physics code, we demonstrate that this technique can conservatively achieve speedups up to 65% on individual resources and may even provide order of magnitude speedup in the extreme case.

关键词： Application software computer applications computer architecture Degradation Distributed computing Laboratories Monte Carlo methods performance evaluation Processor scheduling Runtime

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Monte-Carlo Tree Search and Reinforcement Learning for Reconfiguring Data Stream Processing on Edge computing 31

Monte-Carlo Tree Search and Reinforcement Learning for Recon...

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31st international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Veith, Alexandre da Silva de Assuncao, Marcos Dias Lefevre, Laurent Claude Bernard Univ Lyon 1 Univ Lyon ENS Lyon CNRSInriaParallel Comp Lab LIP F-69342 Lyon 07 France

ISBN: (纸本)9781728141947

Distributed Stream Processing (DSP) applications are increasingly used in new pervasive services that process enormous amounts of data in a seamless and near real-time fashion. Edge computing has emerged as a means to minimise the time to handle events by enabling processing (i.e., operators) to be offloaded from the Cloud to the edges of the Internet, where the data is often generated. Deciding where to execute such operations (i.e., edge or cloud) during application deployment or at runtime is not a trivial problem. In this work, we employ Reinforcement Learning (RL) and Monte-Carlo Tree Search (MCTS) to reassign operators during application runtime. Experimental results show that RL and MCTS algorithms perform better than traditional placement techniques. We also introduce an optimisation to a MCTS algorithm, called MCTS-Best-UCT, that achieves similar latency with fewer operator migrations and faster execution time. In certain scenarios, the time needed by MCTS-Best-UCT to find the best end-to-end latency is at least 33% smaller than the time required by the other algorithms.

关键词： Reinforcement learning

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Retrospective: A Look Back at 20+Years of Experience in Parallel computing Education 34

Retrospective: A Look Back at 20+Years of Experience in Para...

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34th IEEE international Parallel and Distributed Processing symposium (IPDPS)

作者： Adams, Joel C. Calvin Univ Dept Comp Sci Grand Rapids MI 49546 USA

ISBN: (纸本)9781728174457

The author has been teaching parallel computing since the late 1990s. This paper provides a brief history of some of the key experiences in his journey as a parallel computing educator, including formative faculty development workshops, technology shifts, NSF-sponsored projects, and courses taught. By reading this history, the reader may see how much has changed over the past two decades, and so gain insight into how to prepare for changes to parallel computing education in the future.

关键词： beowulf cluster computer science education high performance history multicore parallel software supercomputing

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RAID-x: A new distributed disk array for I/O-centric cluster computing 9

RAID-x: A new distributed disk array for I/O-centric cluster...

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9th IEEE international symposium on high performance Distributed computing

作者： Hwang, K Jin, H Ho, R Univ So Calif Los Angeles CA 90089 USA

ISBN: (纸本)0769507832;0769507840

A new RAID-x (redundant array of inexpensive disks at level x) architecture is presented for distributed I/O processing on a serverless cluster of computers. The RAID-x architecture is based on a new concept of orthogonal striping and mirroring (OSM) across all distributed disks in the cluster. The primary advantages of this OSM approach lie in: (1) a significant improvement in parallel I/O bandwidth, (2) hiding disk mirroring overhead in the background, and (3) greatly enhanced scalability and reliability in cluster computing applications. All claimed advantages are substantiated with benchmark performance results on the Trojans cluster built at USC in 1999. Throughout the paper, we discuss the issues of scalable I/O performance, enhanced system reliability, and striped checkpointing on distributed RAID-x in serverless cluster environment.

关键词： Bandwidth Checkpointing computer applications computer architecture Concurrent computing Distributed computing Linux Packaging Reliability Scalability

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A speculative trace reuse architecture with reduced hardware requirements

A speculative trace reuse architecture with reduced hardware...

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18th international symposium on computer architecture and high performance computing

作者： Pilla, Mauricio L. Childers, Bruce R. da Costa, Amarildo T. Franca, Felipe M. G. Navaux, Philippe O. A. Univ Catolica Pelotas Sch Comp Sci Pelotas Brazil Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA IME Rio De Janeiro Brazil COPPE UFRJ Rio De Janeiro Brazil Univ Fed Rio Grande do Sul Inst Comp Sci Porto Alegre RS Brazil

ISBN: (纸本)0769527043

Trace reuse is an effective way of improving the performance of superscalar processors by skipping the execution of a sequence of instructions with known input and output values. However, the extra hardware complexity is of special concern when implementing such mechanisms. In this paper, we describe ways to reduce these requirements for Reuse through Speculation on Traces (RST). RST combines instruction and trace reuse with value prediction in an integrated mechanism to provide missing trace inputs when execution reaches the beginning of a trace. Speculatively reused traces do not consume resources in the execution pipeline, as they are not executed. In this paper we study the effects of constraining reuse tables to effectively reduce the number of reuse candidates and comparisons. We compare our approach to instruction reuse, trace reuse and value prediction. We show that RST reuses more instructions and has better performance than traditional trace reuse, with an average speedup over a baseline without reuse of 1.21.

关键词： computer software reusability

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Evaluating GPU Passthrough in Xen for high performance Cloud computing 28

Evaluating GPU Passthrough in Xen for High Performance Cloud...

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28th IEEE international Parallel & Distributed Processing symposium workshops (IPDPSW)

作者： Younge, Andrew J. Walters, John Paul Crago, Stephen Fox, Geoffrey C. Indiana Univ Pervas Technol Inst 2719 E 10th St Bloomington IN 47408 USA Univ Southern Calif Informat Sci Inst Arlington VA 22203 USA

ISBN: (纸本)9781479941162

With the advent of virtualization and Infrastructure-as-a-Service (IaaS), the broader scientific computing community is considering the use of clouds for their technical computing needs. This is due to the relative scalability, ease of use, advanced user environment customization abilities clouds provide, as well as many novel computing paradigms available for data-intensive applications. However, there is concern about a performance gap that exists between the performance of IaaS when compared to typical high performance computing (HPC) resources, which could limit the applicability of IaaS for many potential scientific users. Most recently, general-purpose graphics processing units (GPGPUs or GPUs) have become commonplace within high performance computing. We look to bridge the gap between supercomputing and clouds by providing GPU-enabled virtual machines (VMs) and investigating their feasibility for advanced scientific computation. Specifically, the Xen hypervisor is utilized to leverage specialized hardware-assisted I/O virtualization and PCI passthrough in order to provide advanced HPC-centric Nvidia GPUs directly in guest VMs. This methodology is evaluated by measuring the performance of two Nvidia Tesla GPUs within Xen VMs and comparing to bare-metal hardware. Results show PCI passthrough of GPUs within virtual machines is a viable use case for many scientific computing workflows, and could help support high performance cloud infrastructure in the near future.

关键词： Cloud computing GPUs HPC IaaS Scientific computing

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Enabling microservices management for Deep Learning applications across the Edge-Cloud Continuum 33

Enabling microservices management for Deep Learning applicat...

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33rd IEEE international symposium on computer architecture and high performance computing (SBAC-PAD)

作者： Houmani, Zeina Balouek-Thomert, Daniel Caron, Eddy Parashar, Manish Univ Lyon Inria Avalon Team LIP Lab UMR CNRS ENS Lyon Lyon France Univ Utah Sci Comp Imaging SCI Inst Salt Lake City UT 84112 USA

ISBN: (纸本)9781665443012

Deep Learning has shifted the focus of traditional batch workflows to data-driven feature engineering on streaming data. In particular, the execution of Deep Learning workflows presents expectations of near-real-time results with user-defined acceptable accuracy. Meeting the objectives of such applications across heterogeneous resources located at the edge of the network, the core, and in-between requires managing trade-offs between the accuracy and the urgency of the results. However, current data analysis rarely manages the entire Deep Learning pipeline along the data path, making it complex for developers to implement strategies in real-world deployments. Driven by an object detection use case, this paper presents an architecture for time-critical Deep Learning workflows by providing a data-driven scheduling approach to distribute the pipeline across Edge to Cloud resources. Furthermore, it adopts a data management strategy that reduces the resolution of incoming data when potential trade-off optimizations are available. We illustrate the system's viability through a performance evaluation of the object detection use case on the Grid'5000 testbed. We demonstrate that in a multi-user scenario, with a standard frame rate of 25 frames per second, the system speed-up data analysis up to 54.4% compared to a Cloud-only-based scenario with an analysis accuracy higher than a fixed threshold.

关键词： Cloud computing Edge computing Microservices Task allocation Real-time processing computing Continuum Deep Learning

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