检索结果-内蒙古大学图书馆

1st International Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Johnston, Beau Miniskar, Narasinga Rao Young, Aaron Monil, Mohammad Alaul Haque Lee, Seyong Vetter, Jeffrey S. Oak Ridge Natl Lab Oak Ridge TN 37830 USA

ISBN: (纸本)9798350364613;9798350364606

High-Performance Computing is becoming increasingly heterogeneous, relying on a diverse mix of hardware to achieve good performance. Paradoxically, current drivers and frameworks for these devices typically require separate languages and implementations for each vendor. Furthermore, there are few tools and little support to schedule codes between these devices in a truly heterogeneous manner partly because of this fragmentation between vendors and the languages each supports. To overcome both limitations, the Intelligent Runtime System (IRIS) was developed. It allows a common task abstraction to automatically be shared among contemporary vendors and is run from a single host-side API. At runtime, IRIS queries the host system and registers which frameworks and drivers are available, these determine which kernels can be used by the scheduler-CPUs via OpenMP, Nvidia GPUs (CUDA), AMD CPUs (HIP), and Intel and Xilinx FPGAs with OpenCL. IRIS enables tasks to he scheduled to any heterogeneous device and resolves to the appropriate kernel binary at runtime it only uses the devices supported by the system on which it is run. IRIS supports single-task and graph-based expressions of dependencies of tasks. Additionally, IRIS features a range of dynamic scheduling policies, allowing complex chains of tasks and interactions to be executed, relieving the programmer/user from considering the system to assign tasks to devices optimally. This paper presents the peak performance attainable by IRIS over a range of systems each with different numbers and types of accelerator devices, it highlights the flexibility of IRIS since these devices are truly heterogeneous, relying on different back ends (drivers, frameworks, and languages) which historically required unique implementations to utilize them. We then use this peak performance as a baseline to compare increasingly complex chains of tasks (with increasingly complex task dependencies) and evaluate how IRIS copes. Finally, we consider

关键词： Execution Model Heterogeneous Computing Heterogeneous Systems High-Performance Computing Performance Portability programming models Runtime System Scheduling Policy Task Schedule

来源：评论

学校读者我要写书评

暂无评论

The TEXTAROSSA Project: Cool all the Way Down to the Hardware 27

The TEXTAROSSA Project: Cool all the Way Down to the Hardwar...

引用

27th Euromicro Conference on Digital System Design

作者： Filgueras, Antonio Agosta, Giovanni Aldinucci, Marco Alvarez, Carlos D'Ambra, Pasqua Bernaschi, Massimo Biagioni, Andrea Cattaneo, Daniele Celestini, Alessandro Celino, Massimo Chiarini, Carlotta Lo Cicero, Francesca Cretaro, Paolo Fornaciari, William Frezza, Ottorino Galimberti, Andrea Giacomini, Francesco de Haro Ruiz, Juan Miguel Iannone, Francesco Jaschke, Daniel Jimenez-Gonzalez, Daniel Kulczewski, Michal Leva, Alberto Lonardo, Alessandro Martinelli, Michele Martorell, Xavier Montangero, Simone Morais, Lucas Oleksiak, Ariel Palazzari, Paolo Pontisso, Luca Reghenzani, Federico Rossi, Cristian Saponara, Sergio Lodi, Carlo Saverio Simula, Francesco Terraneo, Federico Vicini, Piero Vidal, Miquel Zoni, Davide Zummo, Giuseppe Univ Politecn Cataluna Barcelona Spain Barcelona Supercomp Ctr Barcelona Spain Politecn Milan DEIB Milan Italy INFN Sez Roma Rome Italy Poznan Supercomp & Networking Ctr Poznan Poland ENEA Rome Italy Univ Pisa Pisa Italy Univ Padua Dipartimento Fis & Astron G Galilei Padua Italy INFN Sez Padova Padua Italy Ulm Univ Inst Complex Quantum Syst Ulm Germany Padua Quantum Technol Res Ctr Padua Italy Ctr Interuniv Nazl Informat CINI Rome Italy Natl Res Council CNR Pisa Italy Univ Sapienza Roma Rome Italy INFN CNAF Bologna Italy Univ Turin Turin Italy

ISBN: (纸本)9798350380392;9798350380385

The TEXTAROSSA project aims to bridge the technology gaps that exascale computing systems will face in the near future in order to overcome their performance and energy efficiency challenges. This project provides solutions for improved energy efficiency and thermal control, seamless integration of heterogeneous accelerators in HPC multi-node platforms, and new arithmetic methods. Challenges are tacked through a co-design approach to heterogeneous HPC solutions, supported by the integration and extension of HW and SW IPs, programming models, and tools derived from European research.

关键词： High-performance computing heterogeneous computing FPGA GPU thermal management power management hardware accelerators programming models

来源：评论

学校读者我要写书评

暂无评论

Revet: A Language and Compiler for Dataflow Threads 30

Revet: A Language and Compiler for Dataflow Threads

引用

30th IEEE International Symposium on High-Performance Computer Architecture (HPCA)

作者： Rucker, Alexander C. Sundram, Shiv Smith, Coleman Vilim, Matthew Prabhakar, Raghu Kjolstad, Fredrik Olukotun, Kunle Stanford Univ Stanford CA 94305 USA SambaNova Syst Inc Palo Alto CA USA

ISBN: (纸本)9798350393132;9798350393149

Spatial dataflow architectures such as reconfigurable dataflow accelerators (RDA) can provide much higher performance and efficiency than CPUs and GPUs. In particular, vectorized reconfigurable dataflow accelerators (vRDA) in recent literature represent a design point that enhances the efficiency of dataflow architectures with vectorization. Today, vRDAs can be exploited using either hard-coded kernels or MapReduce languages like Spatial, which cannot vectorize data-dependent control flow. In contrast, CPUs and GPUs can be programmed using general-purpose threaded abstractions. The ideal combination would be the generality of a threaded programming model coupled with the efficient execution model of a vRDA. We introduce Revet: a programming model, compiler, and execution model that lets threaded applications run efficiently on vRDAs. The Revet programming language uses threads to support a broader range of applications than prior parallel-patterns approaches, and our MLIR-based compiler lowers this language to a generic dataflow backend that operates on streaming tensors. Finally, we show that mapping threads to dataflow out-performs GPUs, the current state-of-the-art for threaded accelerators, by 3.8x.

关键词： CGRA Dataflow Dataflow Threads Languages programming models RDA Reconfigurable Architectures vRDA

来源：评论

学校读者我要写书评

暂无评论

Synthesis of Approximate Parametric Circuits for Variational Quantum Algorithms 5

Synthesis of Approximate Parametric Circuits for Variational...

引用

2024 International Conference on Quantum Computing and Engineering

作者： Burgstahler, Blake Wilson, Ellis Pakin, Scott Mueller, Frank North Carolina State Univ Raleigh NC 27695 USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9798331541378

This work presents a novel approach to synthesize approximate circuits for the ansatze of variational quantum algorithms (VQA) and demonstrates its effectiveness in the context of solving integer linear programming (ILP) problems. Synthesis is generalized to produce parametric circuits in close approximation of the original circuit and to do so offline. This removes synthesis from the (online) critical path between repeated quantum circuit executions of VQA. We hypothesize that this approach will yield novel high fidelity results beyond those discovered by the baseline without synthesis. Simulation and real device experiments complement the baseline in finding correct results in many cases where the baseline fails to find any and do so with on average 32% fewer CNOTs in circuits.

关键词： circuit synthesis circuit-model quantum computing programming models QAOA VQA

来源：评论

学校读者我要写书评

暂无评论

programming Tools for High-Performance Data Analysis 24

Programming Tools for High-Performance Data Analysis

引用

33rd International Symposium on High-Performance Parallel and Distributed Computing (HPDC)

作者： Talia, Domenico Trunfio, Paolo Univ Calabria DIMES Arcavacata Di Rende CS Italy

ISBN: (纸本)9798400704130

In this paper we discuss and compare some of the most popular big data programming frameworks for high performance computing (HPC) systems, such as Hadoop, Spark, Storm, Airflow, MPI, Hive, Pig and UPC++. We highlight here their support to different classes of applications, such as batch, streaming, graph-based, and query-based applications. The main features of such frameworks according to their programming model, type of parallelism, level of abstraction, verbosity, and main classes of applications are summarized. We also discuss the main factors that can influence the choice of the most appropriate framework to process and analyze big data. These include the characteristics of input data, the class of application and the infrastructure requirements.

关键词： Big data programming models MapReduce Workflows Message passing SQL-like PGAS

来源：评论

学校读者我要写书评

暂无评论

Errors Classification and Static Detection Techniques for Dual-programming Model (OpenMP and OpenACC)

引用

IEEE ACCESS 2022年 10卷 117808-117826页

作者： Basloom, Huda Saleh Dahab, Mohamed Yehia Alghamdi, Ahmed Mohammed Eassa, Fathy Elbouraey Al-Ghamdi, Abdullah Saad Al-Malaise Haridi, Seif King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Jeddah 21514 Saudi Arabia Agr Res Ctr ARC Giza 12619 Egypt Univ Jeddah Coll Comp Sci & Engn Dept Software Engn Jeddah 21493 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah 21589 Saudi Arabia KTH Royal Inst Technol Dept Comp Sci S-11428 Stockholm Sweden

Recently, incorporating more than one programming model into a system designed for high performance computing (HPC) has become a popular solution to implementing parallel systems. Since traditional programming languages, such as C, C++, and Fortran, do not support parallelism at the level of multi-core processors and accelerators, many programmers add one or more programming models to achieve parallelism and accelerate computation efficiently. These models include Open Accelerators (OpenACC) and Open Multi-Processing (OpenMP), which have recently been used with various models, including Message Passing Interface (MPI) and Compute Unified Device Architecture (CUDA). Due to the difficulty of predicting the behavior of threads, runtime errors cannot be predicted. The compiler cannot identify runtime errors such as data races, race conditions, deadlocks, or livelocks. Many studies have been conducted on the development of testing tools to detect runtime errors when using programming models, such as the combinations of OpenACC with MPI models and OpenMP with MPI. Although more applications use OpenACC and OpenMP together, no testing tools have been developed to test these applications to date. This paper presents a testing tool for detecting runtime using a static testing technique. This tool can detect actual and potential runtime errors during the integration of the OpenACC and OpenMP models into systems developed in C++. This tool implement error dependency graphs, which are proposed in this paper. Additionally, a dependency graph of the errors is provided, along with a classification of runtime errors that result from combining the two programming models mentioned earlier.

关键词： Parallel programming high-performance computing OpenMP OpenACC deadlock runtime errors race condition static testing software testing testing tools classifications exascale systems programming models

来源：评论

学校读者我要写书评

暂无评论

Interaction-Oriented Software Engineering: programming abstractions for autonomy and decentralization

引用

AI COMMUNICATIONS 2022年第4期35卷 381-391页

作者： Chopra, Amit K. Univ Lancaster Sch Comp & Commun Lancaster England

We review the main ideas and elements of Interaction-Oriented Software Engineering (IOSE), a program of research that we have pursued for the last two decades, a span of time in which it has grown from philosophy to practical programming abstractions. What distinguishes IOSE from any other program of research is its emphasis on supporting autonomy by modeling the meaning of communication and using that as the basis for engineering decentralized sociotechnical systems. Meaning sounds esoteric but is the basis for practical decision making and a holy grail for the field of distributed systems. We describe our contributions so far, directions for research, and the potential for broad impact on computing.

关键词： Autonomy decentralization meaning sociotechnical systems multiagent systems protocols norms programming models causality

来源：评论

学校读者我要写书评

暂无评论

Hidet: Task-Mapping programming Paradigm for Deep Learning Tensor Programs 2023

Hidet: Task-Mapping Programming Paradigm for Deep Learning T...

引用

28th ACM International Conference on Architectural Support for programming Languages and Operating Systems (ASPLOS)

作者： Ding, Yaoyao Yu, Cody Hao Zheng, Bojian Liu, Yizhi Wang, Yida Pekhimenko, Gennady Univ Toronto Toronto ON Canada Amazon Web Serv Santa Clara CA USA Vector Inst Toronto ON Canada

ISBN: (纸本)9781450399166

As deep learning models nowadays are widely adopted by both cloud services and edge devices, reducing the latency of deep learning model inferences becomes crucial to provide efficient model serving. However, it is challenging to develop efficient tensor programs for deep learning operators due to the high complexity of modern accelerators (e.g., NVIDIA GPUs and Google TPUs) and the rapidly growing number of operators. Deep learning compilers, such as Apache TVM, adopt declarative scheduling primitives to lower the bar of developing tensor programs. However, we show that this approach is insufficient to cover state-of-the-art tensor program optimizations (e.g., double buffering). In this paper, we propose to embed the scheduling process into tensor programs and use dedicated mappings, called task mappings, to define the computation assignment and ordering directly in the tensor programs. This new approach greatly enriches the expressible optimizations by allowing developers to manipulate tensor programs at amuch finer granularity (e.g., allowing program-statement-level optimizations). We call the proposed method the task-mapping programming paradigm. In addition, we propose a new post-scheduling fusion optimization that allows developers to focus on scheduling every single operator and automates the fusion after scheduling. It greatly reduces the engineering efforts for operator fusion. Our proposed paradigm also constructs an efficient hardware-centric schedule space, which is agnostic to the program input size and greatly reduces the tuning time. With the proposed paradigm, we implement a deep learning compiler - Hidet. Extensive experiments on modern convolution and transformer models show that Hidet outperforms state-of-the-art DNN inference framework, ONNX Runtime, and compiler, TVM equippedwith scheduler AutoTVMand Ansor, by up to 1.48x (1.22x on average). It also reduces the tuning time by 20x and 11x compared with AutoTVM and Ansor, respectively. We open-sou

关键词： deep learning systems systems for machine learning programming models compilation tensor computation

来源：评论

学校读者我要写书评

暂无评论

Exascale programing models for extreme data processing 23

Exascale programing models for extreme data processing

引用

20th ACM International Conference on Computing Frontiers (CF)

作者： Garcia-Blas, Javier Carretero, Jesus Univ Carlos III Madrid Madrid Spain

ISBN: (纸本)9798400701405

Extreme Data is an incarnation of Big Data concept distinguished by the massive amounts of data that must be queried, communicated and analysed in (near) real-time by using a very large number of memory/storage elements and Exascale computing systems. Immediate examples are the scientific data produced at a rate of hundreds of gigabits-per-second that must be stored, filtered and analysed, the millions of images per day that must be mined/analyzed in parallel, the one billion of social data posts queried in real-time on an in-memory components database. Traditional disks or commercial storage cannot handle nowadays the extreme scale of such application data. Following the need of improvement of current concepts and technologies, ASPIDE's activities focus on data-intensive applications running on systems composed of up to millions of computing elements (Exascale systems). Practical results will include the methodology and software prototypes that will be designed and used to implement Exascale applications. The ASPIDE project contributed with the definition of a new programming paradigms, APIs, runtime tools and methodologies for expressing data-intensive tasks on Exascale systems, which can pave the way for the exploitation of massive parallelism over a simplified model of the system architecture, promoting high performance and efficiency, and offering powerful operations and mechanisms for processing extreme data sources at high speed and/or real-time. This work is continued now in the ADMIRE EuroHPC project.

关键词： High Performance Data Analysis programming models Monitoring Auto-tuning

来源：评论

学校读者我要写书评

暂无评论

A survey of software implementations used by application codes in the Exascale Computing Project

引用

INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2022年第1期36卷 5-12页

作者： Evans, Thomas M. Siegel, Andrew Draeger, Erik W. Deslippe, Jack Francois, Marianne M. Germann, Timothy C. Hart, William E. Martin, Daniel F. Oak Ridge Natl Lab Oak Ridge TN USA Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA Lawrence Livermore Natl Lab Livermore CA USA Lawrence Berkeley Natl Lab Berkeley CA USA Los Alamos Natl Lab Washington DC USA Sandia Natl Labs Livermore CA USA

The US Department of Energy Office of Science and the National Nuclear Security Administration initiated the Exascale Computing Project (ECP) in 2016 to prepare mission-relevant applications and scientific software for the delivery of the exascale computers starting in 2023. The ECP currently supports 24 efforts directed at specific applications and six supporting co-design projects. These 24 application projects contain 62 application codes that are implemented in three high-level languages-C, C++, and Fortran-and use 22 combinations of graphical processing unit programming models. The most common implementation language is C++, which is used in 53 different application codes. The most common programming models across ECP applications are CUDA and Kokkos, which are employed in 15 and 14 applications, respectively. This article provides a survey of the programming languages and models used in the ECP applications codebase that will be used to achieve performance on the future exascale hardware platforms.

关键词： Exascale Computing Project graphical processing unit computational physics applications programming models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：