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检索条件"任意字段=4th IEEE/ACM International Workshop on Extreme Scale Programming Models and Middleware, ESPM2 2018"

共 8 条记录，以下是1-10 订阅

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Proceedings of espm2 2021: 6th international ieee workshop on extreme scale programming models and middleware, Held in conjunction with SC 2021: the international Conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of ESPM2 2021: 6th International IEEE Workshop o...

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6th international ieee/acm workshop on extreme scale programming models and middleware, espm2 2021

ISBN: (纸本)9781665411400

the proceedings contain 6 papers. the topics discussed include: scalable parallel algorithm for fast computation of transitive closure of graphs on shared memory architectures;accelerating messages by avoiding copies in an asynchronous task-based programming model;parallel SIMD - a policy based solution for free speed-up using C++ data-parallel types;Taskflow-San: sanitizing erroneous control flow in taskflow graphs;performance evaluation of python parallel programming models: Charm4py and mpi4py;and evaluation of distributed tasks in stencil-based application on GPUs.

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Performance Evaluation of Python Parallel programming models: And mpi4py 6

Performance Evaluation of Python Parallel Programming Models...

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6th international ieee/acm workshop on extreme scale programming models and middleware, espm2 2021

作者： Fink, Zane Liu, Simeng Choi, Jaemin Diener, Matthias Kale, Laxmikant V. University of Illinois at Urbana-Champaign Department of Computer Science UrbanaIL United States

ISBN: (纸本)9781665411400

Python is rapidly becoming the lingua franca of machine learning and scientific computing. With the broad use of frameworks such as Numpy, SciPy, and TensorFlow, scientific computing and machine learning are seeing a productivity boost on systems without a requisite loss in performance. While high-performance libraries often provide adequate performance within a node, distributed computing is required to scale Python across nodes and make it genuinely competitive in large-scale high-performance computing. Many frameworks, such as Charm4Py, DaCe, Dask, Legate Numpy, mpi4py, and Ray, scale Python across nodes. However, little is known about these frameworks' relative strengths and weaknesses, leaving practitioners and scientists without enough information about which frameworks are suitable for their requirements. In this paper, we seek to narrow this knowledge gap by studying the relative performance of two such frameworks: Charm4Py and *** perform a comparative performance analysis of Charm4Py and mpi4py using CPU and GPU-based microbenchmarks other representative mini-apps for scientific computing. © 2021 ieee.

关键词： Python

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Proceedings of espm2 2018: 4th international workshop on extreme scale programming models and middleware, Held in conjunction with SC 2018: the international Conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of ESPM2 2018: 4th International Workshop on Ext...

引用

4th ieee/acm international workshop on extreme scale programming models and middleware, espm2 2018

ISBN: (纸本)9781728101781

the proceedings contain 8 papers. the topics discussed include: distributed memory futures for compile-time, deterministic-by-default concurrency in distributed C++ applications;design of data management for multi SPMD workflow programming model;integration of CUDA processing within the C++ library for parallelism and concurrency (HPX);automatic generation of high-order finite-difference code with temporal blocking for extreme-scale many-core systems;asynchronous execution of python code on task-based runtime systems;a unified runtime for PGAS and event-driven programming;portable and reusable deep learning infrastructure with containers to accelerate cancer studies;and analysis of explicit vs. implicit tasking in OpenMP using Kripke.

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Design of Data Management for Multi SPMD Workflow programming Model 4

Design of Data Management for Multi SPMD Workflow Programmin...

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ieee/acm 4th international workshop on extreme scale programming models and middleware (espm2)

作者： Dufaud, thomas Tsuji, Miwako Sato, Mitsuhisa Univ Paris Sud CEA CNRS Digiteo LabsUVSQINRIAMaison SimulatUSR 3441 Bat 565 F-91191 Gif Sur Yvette France UVSQ EA 7432 LI PaRAD 45 Ave Etats Unis F-78035 Versailles France RIKEN R CCS Ctr Computat Sci Chuo Ku 7-1-26 Minatojima Minami Machi Kobe Hyogo 6500047 Japan

ISBN: (纸本)9781728101781

As both the complexity of algorithms and architecture increase, development of scientific software becomes a challenge. In order to exploit future architecture, we consider a Multi-SPMD workflow programing model. then, data transfer between tasks during computation highly depends on the architecture and middleware used. In this study we design an adaptive system for data management in a parallel programming environment which can express two level of parallelism. We show how the consideration of multiple strategies based on I/O and direct message passing can improve performances and fault tolerance in the YML-XMP environment. On a real application with a sufficiently large amount of local data, speedup of 1.36 for a mixed strategy to 1.73 for a direct message passing method are obtained compared to our original design.

关键词： Task analysis Parallel processing Distributed databases programming Computational modeling Computer architecture Data models

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Portable and Reusable Deep Learning Infrastructure with Containers to Accelerate Cancer Studies 4

Portable and Reusable Deep Learning Infrastructure with Cont...

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ieee/acm 4th international workshop on extreme scale programming models and middleware (espm2)

作者： Zaki, George F. Wozniak, Justin M. Ozik, Jonathan Collier, Nicholson Brettin, thomas Stevens, Rick Frederick Natl Lab Canc Res Frederick MD 21701 USA Argonne Natl Lab Data Sci & Learning 9700 S Cass Ave Argonne IL 60439 USA Univ Chicago Chicago IL 60637 USA Argonne Natl Lab Decis & Infrastruct Sci 9700 S Cass Ave Argonne IL 60439 USA Argonne Natl Lab Comp Environm & Life Sci 9700 S Cass Ave Argonne IL 60439 USA

ISBN: (纸本)9781728101781

Advanced programming models, domain specific languages, and scripting toolkits have the potential to greatly accelerate the adoption of high performance computing. these complex software systems, however, are often difficult to install and maintain, especially on exotic high-end systems. We consider deep learning workflows used on petascale systems and redeployment on research clusters using containers. Containers are used to deploy the MPI-based infrastructure, but challenges in efficiency, usability, and complexity must be overcome. In this work, we address these challenges through enhancements to a unified workflow system that manages interaction with the container abstraction, the cluster scheduler, and the programming tools. We also report results from running the application on our system, harnessing 298 TFLOPS (single precision).

关键词： Containers Optimization Cancer Python programming Deep learning Computational modeling

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A Unified Runtime for PGAS and Event-Driven programming 4

A Unified Runtime for PGAS and Event-Driven Programming

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ieee/acm 4th international workshop on extreme scale programming models and middleware (espm2)

作者： Paul, Sri Raj Chen, Kun Hayashi, Akihiro Grossman, Max Sarkar, Vivek DeVinney, Jason Carlson, Bill Rice Univ Houston TX 77005 USA Georgia Inst Technol Atlanta GA 30332 USA CCS Bowie MD USA

ISBN: (纸本)9781728101781

A well-recognized characteristic of extreme scale systems is that their computation bandwidths far exceed their communication bandwidths. PGAS runtimes have proven to be effective in enabling efficient use of communication bandwidth for many applications, due to their efficient support for short nonblocking one-sided messages. However, they were not designed for exploiting the massive levels of intra-node parallelism found in extreme scale systems. thus, a key question in the use of PGAS runtimes on extreme scale platforms is: what kind of node-level runtime system should be integrated with PGAS runtimes? In this paper, we explore the premise that event-driven intranode runtimes could be promising candidates for integration with PGAS runtimes, due to their ability to overlap useful computation with background long-latency operations. For this exploration, we use OpenSHMEM as an exemplar of PGAS runtimes, and *** as an exemplar of event-driven runtimes. While *** may seem an unusual choice for high-performance computing, its prominent role as an event-driven runtime for server-side Javascript is a good match for what we need for optimizing the use of communication bandwidth. At the same time, the availability of excess computation bandwidth in extreme scale systems can help mitigate the local computation overheads of Javascript relative to native languages such as C++. Further, since the *** environment is single-threaded, we get an automatic guarantee that no data races will occur on Javascript objects, a guarantee that we cannot get with C++. Our integration of OpenSHMEM and *** makes it possible to expose nonblocking PGAS operations as JavaScript constructs that can seamlessly be used with the JavaScript asynchronous mechanisms, such as those based on promises. We believe that the exploration and preliminary results in this paper offer a new direction for future research on building high productivity runtimes for extreme scale systems.

关键词： Node.js JavaScript PGAS OpenSHMEM Asynchronous Many-Task (AMT) Habanero Library

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Distributed memory futures for compile-time, deterministic-by-default concurrency in distributed C plus plus applications 4

Distributed memory futures for compile-time, deterministic-b...

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ieee/acm 4th international workshop on extreme scale programming models and middleware (espm2)

作者： Wilke, Jeremiah J. Hollman, David S. Lewis, Cannada Markosyan, Aram Morals, Nicolas Sandia Natl Labs Scalable Modeling & Anal Livermore CA 94550 USA

ISBN: (纸本)9781728101781

Futures are a widely-used abstraction for enabling deferred execution in imperative programs. Deferred execution enqueues tasks rather than explicitly blocking and waiting for them to execute. Many task-based programming models with some form of deferred execution rely on explicit parallelism that is the responsibility of the programmer. Deterministic-by-default (implicitly parallel) models instead use data effects to derive concurrency automatically, alleviating the burden of concurrency management. Both implicitly and explicitly parallel models are particularly challenging for imperative object-oriented programming. Fine-granularity parallelism across member functions or amongst data members may exist, but is often ignored. In this work, we define a general permissions model that leverages the C++ type system and move semantics to define an asynchronous programming model embedded in the C++ type system. Although a default distributed memory semantic is provided, the concurrent semantics are entirely configurable through C++ constexpr integers. Correct use of the defined semantic is verified at compile-time, allowing deterministic-by-default concurrency to be safely added to applications. Here we demonstrate the use of these "extended futures" for distributed memory asynchronous communication and load balancing. An MPI particle-in-cell application is modified with the wrapper class using this task model, with results presented for a Haswell system up to 64 nodes.

关键词： task-based runtimes template metaprogramming parallel computing programming models execution models

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Automatic Generation of High-Order Finite-Difference Code with Temporal Blocking For extreme-scale Many-Core Systems 4

Automatic Generation of High-Order Finite-Difference Code wi...

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ieee/acm 4th international workshop on extreme scale programming models and middleware (espm2)

作者： Tanaka, Hideyuki Ishihara, Youhei Sakamoto, Ryo Nakamura, Takashi Kimura, Yasuyuki Nitadori, Keigo Tsubouchi, Miyuki Makino, Jun ExaScaler Inc Chiyoda Ku 2-1 Ogawa Machi Tokyo 1010052 Japan Kyoto Univ Yukawa Inst Theoret Phys Sakyo Ku Kyoto 6068502 Japan RIKEN R CCS Chuo Ku 7-1-26 Minatojima Minami Machi Kobe Hyogo 6500047 Japan PEZY Comp KK Chiyoda Ku 1-11 Ogawa Machi Tokyo 1010052 Japan Kobe Univ Nada Ku 1-1 Rokkodai Cho Kobe Hyogo 6578501 Japan

ISBN: (纸本)9781728101781

In this paper we describe the basic idea, implementation and achieved performance of our DSL for stencil computation, Formura, on systems based on PEZY-SC2 many-core processor. Formura generates, from high-level description of the differential equation and simple description of finite-difference stencil, the entire simulation code with MPI parallelization with overlapped communication and calculation, advanced temporal blocking and parallelization for many-core processors. Achieved performance is 4.78 PF, or 21.5% of the theoretical peak performance for an explicit scheme for compressive CFD, with the accuracy of fourth-order in space and third-order in time. For a slightly modified implementation of the same scheme, efficiency was slightly lower (17.5%) but actual calculation time per one timestep was faster by 25%. Temporal blocking improved the performance by up to 70%. Even though the B/F number of PEZY-SC2 is low, around 0.02, we have achieved the efficiency comparable to those of highly optimized CFD codes on machines with much higher memory bandwidth such as K computer. We have demonstrated that automatic generation of the code with temporal blocking is a quite effective way to make use of very large-scale machines with low memory bandwidth for large-scale CFD calculations.

关键词： Bandwidth Mathematical model Finite difference methods DSL Computational modeling Memory management Upper bound

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