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<div>[Call for Papers]</div>
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<div>PMAW: The Programming Models and Algorithms Workshop</div>
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<div>Held in Conjunction with IPDPS'18</div>
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<div>https://pmaw.github.io</div>
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<div>The purpose of this workshop is to bring together experts of different areas to</div>
<div>bridge the gap between programming models and algorithms in general. The</div>
<div>ultimate goal is to foster the interdisciplinary relationships among domain</div>
<div>scientists, and compiler, language and runtime (CLR) experts in order to</div>
<div>re-design and rethink the next generation of algorithms that will be used</div>
<div>during the forthcoming decades. Therefore, the intended audience and</div>
<div>participants of the proposed workshop involves experts, practitioners,</div>
<div>students, professors and researchers of the mentioned fields, of the broader</div>
<div>computer science, applied mathematics and computational science fields.</div>
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<div>In the shorter term, the objective of this workshop is to enable and retarget</div>
<div>core algorithmic building blocks that leverage cutting-edge data-flow, task and</div>
<div>data-parallel runtimes, and gradually promote the integration of performance,</div>
<div>energy and other execution properties that an algorithm should embody in order</div>
<div>to fully exploit future hardware and software computing infrastructure.</div>
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<div>We welcome ongoing and preliminary work, experience reports showing a negative</div>
<div>result or a gap between the two communities. In general, PMAW topics lie</div>
<div>between fields of domain scientists and CLR experts. For instance, domain</div>
<div>experts who are attempting to map their application to some specific platform</div>
<div>or runtime, as well as CLR experts wishing to identify and learn about strong</div>
<div>and impactful applications and benchmarks suitable for some new software</div>
<div>technology.</div>
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<div>All papers must be original and not simultaneously submitted to another journal</div>
<div>or conference. The following is a non-exhaustive list of topics of interest:</div>
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<div>- Implementation of algorithms using data-flow models and runtimes</div>
<div>- Domain specific languages, compilers and tools for data-flow, data and task parallelism</div>
<div>- New parallel runtime features that facilitate algorithmic description</div>
<div>- Rethinking of classical algorithms that incorporate parallelism, energy and</div>
<div> other performance properties</div>
<div>- Communication minimization schemes</div>
<div>- Network aware computing</div>
<div>- Locality and Communication abstractions for algorithmic specification</div>
<div>- Orchestrating runtimes and heterogeneous runtime scheduling</div>
<div>- Experiences in porting or implementing scientific frameworks over one or more</div>
<div> programming models</div>
<div>- A negative result demonstrating general poor support for some application or</div>
<div> class of computation and potential solutions or workaround</div>
<div>- Application and runtime demos</div>
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<div>[Important Dates]</div>
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<div>Paper submission start: November 6, 2017</div>
<div>Abstract due: February 9, 2018</div>
<div>Paper submission due : February 16, 2018</div>
<div>Notification of acceptance : April 2, 2018</div>
<div>Camera ready papers: April 23, 2018</div>
<div>Workshop date: May 25, 2018</div>
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<div>[Submission]</div>
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<div>Please submit your paper here: https://easychair.org/conferences/?conf=pmaw18</div>
<div>Papers must use the ACM standard LATEX or Word templates. As this workshop</div>
<div>encourages works-in-progress, papers should be 5-7 pages in length, including</div>
<div>references and appendices Proceedings</div>
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<div>PMAW will not have formal proceedings. However, accepted papers will be posted</div>
<div>online so as to allow authors to re-submit their work to other venues and using</div>
<div>the feedback provided in the workshop.</div>
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<div>[More Information]</div>
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<div>For more information regarding this workshop, please visit:</div>
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<div> https://pmaw.github.io</div>
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<div>or contact Martin Kong <mkong@bnl.gov></div>
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<div style="font-family:Tahoma; font-size:13px">Martin Kong
<div>Assistant Scientist</div>
<div>Computational Science Initiative (CSI)</div>
<div>Brookhaven National Laboratory</div>
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