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检索条件"主题词=Programming Model"

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DISC: A Domain-Interaction Based programming model With Support for Heterogeneous Execution 14

DISC: A Domain-Interaction Based Programming Model With Supp...

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International Conference on High Performance Computing, Networking, Storage and Analysis

作者： Kurt, Mehmet Can Agrawal, Gagan Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9781479955008

Several emerging trends are pointing to increasing heterogeneity among nodes and/or cores in HPC systems. Existing programming models, especially for distributed memory execution, typically have been designed to facilitate high performance on homogeneous systems. This paper describes a programming model and an associated runtime system we have developed to address the above need. The main concepts in the programming model are that of a domain and interactions between the domain elements. We explain how stencil computations, unstructured grid computations, and molecular dynamics applications can be expressed using these simple concepts. We show how inter-process communication can be handled efficiently at runtime just from the knowledge of domain interaction, for different types of applications. Subsequently, we develop techniques for the runtime system to automatically partition and re-partition the work among heterogeneous processors or nodes.

关键词： programming model heterogeneous support load balancing

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FPM: A Flexible programming model for MPSoC on FPGA

FPM: A Flexible Programming Model for MPSoC on FPGA

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26th IEEE International Parallel and Distributed Processing Symposium (IPDPS) / Workshop on High Performance Data Intensive Computing

作者： Wang, Chao Li, Xi Zhang, Junneng Chen, Peng Feng, Xiaojing Zhou, Xuehai Univ Sci & Technol China Sch Comp Sci Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Suzhou Inst Adv Study Suzhou Peoples R China

ISBN: (纸本)9780769546766

This paper proposes a flexible programming model (FPM), which addresses the automatic parallel execution for functional tasks on heterogeneous multiprocessors. Guided by the simply annotated source codes, a front-end source to source compiler is provided to identify the parallel regions and generate the sources codes. A runtime middleware analyzes the inter-task data dependencies and schedules the tasks with renaming techniques automatically. FPM has been verified by the prototype built on state-of-art FPGA. Examples demonstrate that our model can largely ease the burden of programmers as well as uncover the task level parallelism.

关键词： programming model MPSoC data dependencies out-of-order execution task level parallelism

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ZhuQue: A Neural Network programming model Based on Labeled Data Layout 13th

ZhuQue: A Neural Network Programming Model Based on Labeled ...

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13th International Symposium on Advanced Parallel Processing Technologies (APPT)

作者： Du, Weijian Wu, Linyang Chen, Xiaobing Zhuang, Yimin Zhi, Tian Chinese Acad Sci Inst Comp Technol SKL Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Cambricon Tech Ltd Shanghai Peoples R China

ISBN: (纸本)9783030296117;9783030296100

In the last five years, the research of neural network accelerators has made remarkable achievements and provided powerful hardware support for many deep learning algorithms. In order to improve the performance of the neural network accelerator, algorithm optimization and data layout in the neural network development kit (NDK) are indispensable. The rich data types in neural network algorithms determine the diversity of data layout information. How to add complex data layout information to the NDK, to guide the work of all aspects of the software, to avoid user perception and to provide a user-friendly API, has become a series of issues worth studying. This paper implements a neural network development kit based on labeled data layout to solve the above problems, and abstracts a neural network programming model. The programming model establishes a neural network computing graph at "creating time", "compiling time" sets the data label and "runtime" uses the label to control the data transfer. Compared with the existing NDK, the software has an average performance improvement of 4.76x. In addition, this paper also defines dynamic tags and static tags of neural network data, and proposes a neural network data classification method.

关键词： Neural network programming model Labeled Data layout SDK

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SLOPE: Structural Locality-Aware programming model for Composing Array Data Analysis 34th

SLOPE: Structural Locality-Aware Programming Model for Compo...

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34th International Conference on High Performance Computing (ISC High Performance)

作者： Dong, Bin Wu, Kesheng Byna, Suren Tang, Houjun Lawrence Berkeley Natl Lab 1 Cyclotron Rd Berkeley CA 94720 USA

ISBN: (纸本)9783030206567;9783030206550

MapReduce brought on the Big Data revolution. However, its impact on scientific data analyses has been limited because of fundamental limitations in its data and programming models. Scientific data is typically stored as multidimensional arrays, while MapReduce is based on key-value (KV) pairs. Applying MapReduce to analyze array-based scientific data requires a conversion of arrays to KV pairs. This conversion incurs a large storage overhead and loses structural information embedded in the array. For example, analysis operations, such as convolution, are defined on the neighbors of an array element. Accessing these neighbors is straightforward using array indexes, but requires complex and expensive operations like self-join in the KV data model. In this work, we introduce a novel `structural locality'-aware programming model (SLOPE) to compose data analysis directly on multidimensional arrays. We also develop a parallel execution engine for SLOPE to transparently partition the data, to cache intermediate results, to support in-place modification, and to recover from failures. Our evaluations with real applications show that SLOPE is over ninety thousand times faster than Apache Spark and is 38% faster than TensorFlow.

关键词： Multidimensional array programming model Structural locality Composable data analysis User-defined function ArrayUDF Apache Spark TensorFlow MapReduce Array cache

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Lightning: Scaling the GPU programming model Beyond a Single GPU 36

Lightning: Scaling the GPU Programming Model Beyond a Single...

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36th IEEE International Parallel and Distributed Processing Symposium (IEEE IPDPS)

作者： Heldens, Stijn Hijma, Pieter Werkhoven, Ben Van Maassen, Jason van Nieuwpoort, Rob, V Netherlands eSci Ctr Amsterdam Netherlands Univ Amsterdam Amsterdam Netherlands Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781665481069

The GPU programming model is primarily aimed at the development of applications that run one GPU. However, this limits the scalability of GPU code to the capabilities of a single GPU in terms of compute power and memory capacity. To scale GPU applications further, a great engineering effort is typically required: work and data must be divided over multiple GPUs by hand, possibly in multiple nodes, and data must be manually spilled from GPU memory to higher-level memories. We present Lightning: a framework that follows the common GPU programming paradigm but enables scaling to large problems with ease. Lightning supports multi-GPU execution of GPU kernels, even across multiple nodes, and seamlessly spills data to higher-level memories (main memory and disk). Existing CUDA kernels can easily be adapted for use in Lightning, with data access annotations on these kernels allowing Lightning to infer their data requirements and the dependencies between subsequent kernel launches. Lightning efficiently distributes the work/data across GPUs and maximizes efficiency by overlapping scheduling, data movement, and kernel execution when possible. We present the design and implementation of Lightning, as well as experimental results on up to 32 GPUs for eight benchmarks and one real-world application. Evaluation shows excellent performance and scalability, such as a speedup of 57.2x over the CPU using Lighting with 16 GPUs over 4 nodes and 80 GB of data, far beyond the memory capacity of one GPU.

关键词： GPU distributed computing CUDA programming model

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DFA-G: A Unified programming model for Vertex-centric Parallel Graph Processing 16

DFA-G: A Unified Programming Model for Vertex-centric Parall...

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16th IEEE International Conference on Data Mining (ICDM)

作者： Suo, Bo Su, Jing Chen, Qun Li, Zhanhuai Pan, Wei Northwestern Polytech Univ Sch Comp Sci & Engn Xian Peoples R China

ISBN: (纸本)9781509059102

Many systems have been built for vertex-centric parallel graph processing. Based on the Bulk Synchronous Parallel (BSP) model, they execute user-defined operations at vertices iteratively and exchange information between vertices by messages. Even though the native BSP systems (e.g. Pregel and Giraph) execute the operations on vertices synchronously within an iteration, many other platforms (e.g. Grace, Blogel and GraphHP) have proposed asynchronous execution for improved efficiency. However, they also bring about an undesirable side effect: a program designed for synchronous platforms may not run properly on asynchronous platforms. In this demo, we present DFA-G (Deterministic Finite Automaton for Graph processing), a unified programming model for vertex-centric parallel graph processing. Built on DFA, DFA-G expresses the computation at a vertex as a process of message-driven state transition. A program modeled after DFA-G can run properly on both synchronous and asynchronous platforms. We demonstrate how to build DFA-G models using a graphical user interface and also how to automatically translate them into BSP programs.

关键词： Deterministic Finite Automaton Parallel Graph Processing programming model

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A Compilation Flow for Parametric Dataflow: programming model, Scheduling, and Application to Heterogeneous MPSoC

A Compilation Flow for Parametric Dataflow: Programming Mode...

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International Conference on Compilers, Architecture and Synthesis for Embedded Systems (CASES)

作者： Dardaillon, Mickael Marquet, Kevin Risset, Tanguy Martin, Jerome Charles, Henri-Pierre Univ Lyon Inria INSA Lyon CITI Inria F-69621 Villeurbanne France Univ Grenoble Alpes F-38000 Grenoble France CEA Grenoble LETI F-38054 Grenoble France

ISBN: (纸本)9781450330503

Efficient programming of signal processing applications on embedded systems is a complex problem. High level models such as Synchronous dataflow (SDF) have been privileged candidates for dealing with this complexity. These models permit to express inherent application parallelism, as well as analysis for both verification and optimization. Parametric dataflow models aim at providing sufficient dynamicity to model new applications, while at the same time maintaining the high level of analyzability needed for efficient real life implementations. This paper presents a new compilation flow that targets parametric dataflows. Built on the LLVM compiler infrastructure, it offers an actor based C++ programming model to describe parametric graphs, a compilation front-end providing graph analysis features, and a retargetable back-end to map the application on real hardware. This paper gives an overview of this flow, with a specific focus on scheduling. The crucial gap between dataflow models and real hardware on which actor firing is not atomic, as well as the consequences on FIFOs sizing and execution pipelining are taken into account. The experimental results illustrate our compilation flow applied to compilation of 3GPP LTE-Advanced demodulation on a heterogeneous MPSoC with distributed scheduling features. This achieves performances similar to time-consuming hand made optimizations.

关键词： data flow programming model heterogeneous MPSoC compiler scheduling

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Convergence of HPC and Big Data in extreme-scale data analysis through the DCEx programming model 34

Convergence of HPC and Big Data in extreme-scale data analys...

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34th IEEE International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Garcia-Blas, Javier Fernandez Munoz, Javier Carretero, Jesus Marozzo, Fabrizio Talia, Domenico Trunfio, Paolo Fernandez-Pena, Alberto Martin de Blas, Daniel Univ Carlos III Madrid Comp Sci & Engn Dept Leganes Spain Univ Calabria DIMES Dept Arcavacata Di Rende Italy Univ Carlos III Madrid Inst Invest Sanitaria Gregorio Maranon Dept Bioingn & Ingn Aerosp Madrid Spain

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

High-level programming models can help application developers to access and use resources without the need to manage low-level architectural entities, as a parallel programming model defines a set of programming abstractions that simplify the way by which a programmer structures and expresses her/his algorithm. Early proposals of Exascale programming tools are based on the adaptation of traditional parallel programming languages and hybrid solutions. This incremental approach is too conservative, often resulting in very complex code. This paper describes the design features, the programming constructs, and the runtime mechanisms of the Data Centric programming model for Exascale systems (DCEx). DCEx is based on structuring applications into data-parallel blocks. Blocks are units of shared-and distributed-memory parallel computation, communication, and migration in the memory/storage hierarchy. Blocks and their message queues are mapped onto processes and placed in memory/storage by the DCEx runtime. Those data-parallel blocks are orchestrated by using distributed parallel patterns that simplify the development cost. DCEx aims to reach the convergence of traditional HPC programming models, mainly based on MPI, with the emerging technologies based on the data intensive paradigms. To demonstrate the potential of DCEx, we carried out an experimental evaluation developing a real-world diffusion-weighted magnetic resonance imaging data processing application in a neuroimaging research context.

关键词： Big Data HPC convergence programming model PGAS parallel patterns

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Flexible Ray Traversal with an Extended programming model 19

Flexible Ray Traversal with an Extended Programming Model

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12th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia

作者： Lee, Won-Jong Liktor, Gabor Vaidyanathan, Karthik Intel Corp Mountain View CA 94040 USA

ISBN: (纸本)9781450369459

The availability of hardware-accelerated ray tracing in GPUs and standardized APIs has led to a rapid adoption of ray tracing in games. While these APIs allow programmable surface shading and intersections, most of the ray traversal is assumed to be fixed-function. As a result, the implementation of per-instance Level-of-Detail (LOD) techniques is very limited. In this paper, we propose an extended programming model for ray tracing which includes an additional programmable stage called the traversal shader that enables procedural selection of acceleration structures for instances. Using this programming model, we demonstrate multiple applications such as procedural multi-level instancing and stochastic LOD selection that can significantly reduce the bandwidth and memory footprint of ray tracing with no perceptible loss in image quality.

关键词： ray tracing programming model level-of-detail instancing

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Research on programming model for "Master-slave Distributed Tasks" of Insect Intelligent Building

Research on Programming Model for "Master-slave Distributed ...

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Chinese Automation Congress (CAC)

作者： Xue, Guangtong Yang, Qiliang Xing, Jianchun Zhao, Shuo Army Engn Univ PLA Coll Def Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728113128

Insect Intelligent Building is a flat and distributed new Building Intelligent System, whose tasks need be solved in parallel. How to develop parallel programs quickly and efficiently for it has become an important research content. However, the general parallel programming method lacks the domain characteristics of Insect Intelligent Building, and pay too much attention to the complex interaction between nodes. This leads to more difficulty in programming This paper focuses on the "master-slave distributed tasks" that is a kind of typical task in Insect Intelligent Building, and proposes a programming model. This programming model defines some new programming elements such as "Domain", "Master CPN", and "Slave CPN'', and hides underlying details of data propagation and aggregation between nodes. It effectively reduces the programming difficulty of "master-slave distributed tasks". In the case study, the programming model is applied to a tap water control system and the results show that it has high robustness and portability and be easy to use.

关键词： Insect Intelligent Building programming model master-slave distributed tasks MSpro

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