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data-driven execution of the tile LU Decomposition 6

Data-driven execution of the tile LU Decomposition

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6th workshop on data-flow execution models for extreme scale computing, dfm 2016

作者： Matheou, George Kyriacou, Costas Evripidou, Paraskevas University of Cyprus Nicosia Cyprus Frederick University Nicosia Cyprus

ISBN: (纸本)9781450361996

the objective of this paper is to analyze, develop and evaluate the tile LU Decomposition using the FREDDO framework. FREDDO is a C++ framework, based on the DDM model of execution, that supports efficient data-driven execution on conventional processors. the performance evaluation shows that FREDDO scales well and tolerates scheduling overheads and memory latencies effectively. the LU implementation is evaluated in both single-node and distributed execution environments. In both cases our framework achieves very good speedups, especially in the larger problem sizes. Particularly, our framework achieves up to 97% of the maximum possible speedup on a single-node and up to 90% of the maximum possible speedup on a 4-node cluster with a total of 128 cores. © 2016 Association for computing Machinery.

关键词： Multitasking

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Challenges and opportunities for dataflow processing on exascale computers 6

Challenges and opportunities for dataflow processing on exas...

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6th workshop on data-flow execution models for extreme scale computing, dfm 2016

作者： Wozniak, Justin M. Wilde, Michael Foster, Ian Argonne National Laboratory LemontIL United States

ISBN: (纸本)9781450361996

In this paper we have outlined multiple potential applications of dataflow-based programming and runtime systems to the challenges of exascale computing. We described how a coarse-grained dataflow abstraction can capture the overarching patterns in exascale workflows, particularly in describing data-dependent analysis tasks attached to large-scale simulation ensembles. this in situ data transmission and analysis model enables a highly productive programming model, where components can be easily coupled and decoupled for varying computational experiments. We also described how this programming model allows the runtime system to automatically make computation and data placement decisions relevant to multiple exascale systems challenges. Our position is that exascale computing offers an exciting opportunity for dataflow processing to solve challenges conventionally addressed by systems designers and implementors. the Swift/T system is but one attempt to address these problems, and we invite discussion at the dfm workshop about this research and development area. © 2016 Association for computing Machinery.

关键词： data handling

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Tree-based read-only data chunks for NVRAM programming 6

Tree-based read-only data chunks for NVRAM programming

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6th workshop on data-flow execution models for extreme scale computing, dfm 2016

作者： Bhandari, Kumud Sarkar, Vivek Department of Computer Science HoustonTX United States

ISBN: (纸本)9781450361996

As the DRAM technology is fast reaching a scaling threshold, emerging non-volatile, byte-addressable memory (NVRAM) is expected to supplement and eventually replace DRAM. Future computing systems are anticipated to have a large amount of NVRAM, possibly spanning across more than one coherence domain. Furthermore, taking advantage of in-place persistence provided by the NVRAM in future systems requires a strategy to prevent tolerated failures (e.g. power failure) from leaving persistent data in an incoherent state. A fresh look at memory management approaches across the system stack is required to fully utilize future NVRAM. In this paper, we carefully assess the NVRAM-related memory access and management challenges, its implication to application level programming, and examine the suitability of tree-based read-only data chunks to NVRAM programming. © 2016 Association for computing Machinery.

关键词： Memory architecture

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CnC: A dependence programming model? 6

CnC: A dependence programming model?

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6th workshop on data-flow execution models for extreme scale computing, dfm 2016

作者： Budimlić, Zoran Knobe, Kathleen Rice University HoustonTX United States

ISBN: (纸本)9781450361996

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. © 2016 Association for computing Machinery.

关键词： data flow analysis

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