Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing最新文献

Challenges and Opportunities for Dataflow Processing on Exascale Computers 百亿亿次计算机上数据流处理的挑战与机遇

Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing Pub Date : 2016-09-15 DOI: 10.1145/3292533.3292537

J. Wozniak, M. Wilde, Ian T Foster

{"title":"Challenges and Opportunities for Dataflow Processing on Exascale Computers","authors":"J. Wozniak, M. Wilde, Ian T Foster","doi":"10.1145/3292533.3292537","DOIUrl":"https://doi.org/10.1145/3292533.3292537","url":null,"abstract":"Computational applications critical to society in areas such as materials design, climate modeling, and energy production and distribution must be developed quickly and correctly. Such studies are typically done via computational experiments, in which large numbers of simulation and analysis tasks are strung together into a workflow, formally or informally. Many innovative programming models to handle workflow specifications are based on an implicitly parallel dataflow language to support this model. Here, we posit that the dataflow model has the added benefit that it can exploit features of exascale computers, expected in ∼2023 [21]. Workflows and other outermost patterns such as parameter sweeps, searches, and optimizations can easily be expressed with dataflow languages. In this model, either statically or at runtime a dataflow structure is available to the runtime system, presenting the opportunity for many types of automated decisions for scheduling and resource management. Workflow applications on these systems will also introduce new requirements, such as high-performance data movement methods for in situ data analysis, and new challenges, such as varying reliability characterstics. In this paper, we describe three key exascale feature areas that will be exposed to users at the application level. First, a more complex storage hierarchy is expected. New cache types such as scratchpad memory may be available, and heterogeneous RAM systems may have differing performance and reliability characteristics. Node-local storage may be available. These systems are expected to be available to the application or middleware via advanced operating system and runtime features. Second, tighter power budgets and programmer-controlled power scaling will likely be available. These will require the application level to make decisions about performance/power tradeoffs. Third, a more complex","PeriodicalId":195082,"journal":{"name":"Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114718340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

CnC: A Dependence Programming Model CnC:一个依赖编程模型

Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing Pub Date : 2016-09-15 DOI: 10.1145/3292533.3292536

Zoran Budimlic, K. Knobe

{"title":"CnC: A Dependence Programming Model","authors":"Zoran Budimlic, K. Knobe","doi":"10.1145/3292533.3292536","DOIUrl":"https://doi.org/10.1145/3292533.3292536","url":null,"abstract":"Application tuning is the one of the major hurdles on the road to exascale computing. Tuning is often directed at a specific architecture or towards some specific tuning goal. As currently practiced, the tuning activity requires serious expertise in the application domain, target architecture and tuning goals. Keeping all these (sometimes conflicting) concerns in mind at the same time while developing a program is very difficult and error prone. Dependence programming is a class of dataflow programming in which both data and control flow are explicit, are distinguished and are given equal weight. This paper gives an overview of CnC, a dataflow dependence programming model, from the perspective of the needs of exascale computing and shows how CnC addresses those needs through a separation of concerns. The goal of CnC is to enable a process that is easier, less error-prone and more effective, by separating these concerns into independent activities. Rather than proposing yet another approach to tuning, CnC provides a way to specify the application in a way that a) hides details not relevant to tuning, b) includes as much detail as possible to support the analysis and tuning process, and c) does not assume any specific architecture, style of tuning or tuning goals. This results in a specification of the application that can be applied to any existing or future architectures and can use any existing or future tuning approach. At the same time it can be more efficient in human time spent on creating the application and more effective in finding the best tuning.","PeriodicalId":195082,"journal":{"name":"Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128349417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data-Driven execution of the Tile LU Decomposition Tile LU分解的数据驱动执行

Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing Pub Date : 2016-09-15 DOI: 10.1145/3292533.3292534

George Matheou, C. Kyriacou, P. Evripidou

引用次数: 0

Tree-based Read-only Data Chunks for NVRAM Programming 基于树的只读数据块NVRAM编程

Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing Pub Date : 2016-09-15 DOI: 10.1145/3292533.3292535

Kumud Bhandari, Vivek Sarkar

引用次数: 0

Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing 第六届极端规模计算数据流执行模型研讨会论文集

Proceedings of the Sixth Workshop on Data-Flow Execution Models for Extreme Scale Computing Pub Date : 1900-01-01 DOI: 10.1145/3292533

引用次数: 0