Introduction
Due to the heterogeneous data sets they process, data intensive applications employ a diverse set of methods and data structures. Consequently, they abound with irregular memory accesses, control flows, and communication patterns. Current supercomputing systems are organized around components optimized for data locality and bulk synchronous computations. Managing any form of irregularity on them demands substantial effort, and often leads to poor performance. Holistic solutions to address these challenges can emerge only by considering the problem from all perspectives: from micro- to system-architectures, from compilers to languages, from libraries to runtimes, from algorithm design to data characteristics. Strong collaborative efforts among researchers with different expertise, including domain experts and end users, could lead to significant breakthroughs. This workshop brings together scientists with these different backgrounds to discuss methods and technologies for efficiently supporting irregular applications on current and future architectures.
Call for paper
Submission Topics
Topics of interest, of both theoretical and practical significance, include but are not limited to:
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Micro- and System-architectures, including multi- and many-core designs, heterogeneous processors, accelerators (GPUs, vector processors, Automata processor), reconfigurable (coarse grained reconfigurable and FPGA designs) and custom processors
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Network architectures and interconnect (including high-radix networks, optical interconnects)
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Novel memory architectures and designs (including processors-in memory)
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Impact of new computing paradigms on irregular workloads (including neuromorphic processors and quantum computing)
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Modeling, simulation and evaluation of novel architectures with irregular workloads
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Innovative algorithmic techniques
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Combinatorial algorithms (graph algorithms, sparse linear algebra, etc.)
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Impact of irregularity on machine learning approaches
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Parallelization techniques and data structures for irregular workloads
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Data structures combining regular and irregular computations (e.g., attributed graphs)
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Approaches for managing massive unstructured datasets (including streaming data)
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Languages and programming models for irregular workloads
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Library and runtime support for irregular workloads
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Compiler and analysis techniques for irregular workloads
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High performance data analytics applications, including graph databases
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Important Date
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Nov 13
2017
Conference Date
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Nov 13 2017
Registration deadline
Sponsored By
IEEE 计算机学会
Contact Information
Previous Conferences
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2018-11-12 United States
2018 IEEE/ACM 8th Workshop on Irregular Applications: Architectures and Algorithms -
2016-11-13 United States Salt Lake City,USA
6th Workshop on Irregular Applications: Architectures and Algorithms -
2014-11-17 United States
Fourth Workshop on Irregular Applications: Architectures and Algorithms
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