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Runtime Aware Architectures

Published: 15 May 2017 Publication History

Abstract

In the last years the traditional ways to keep the increase of hardware performance to the rate predicted by the Moore's Law vanished. When uni-cores were the norm, hardware design was decoupled from the software stack thanks to a well defined Instruction Set Architecture (ISA). This simple interface allowed developing applications without worrying too much about the underlying hardware, while computer architects proposed techniques to aggressively exploit Instruction-Level Parallelism (ILP) in superscalar processors. Current multi-cores are designed as simple symmetric multiprocessors on a chip. While these designs are able to compensate the clock frequency stagnation, they face multiple problems in terms of power consumption, programmability, resilience or memory. The solution is to give more responsibility to the runtime system and to let it tightly collaborate with the hardware. The runtime has to drive the design of future multi-cores architectures. In this talk, we introduce an approach towards a Runtime-Aware Architecture (RAA), a massively parallel architecture designed from the runtime's perspective.

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cover image ACM Conferences
CF'17: Proceedings of the Computing Frontiers Conference
May 2017
450 pages
ISBN:9781450344876
DOI:10.1145/3075564
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 May 2017

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  • Invited-talk
  • Research
  • Refereed limited

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CF '17
Sponsor:
CF '17: Computing Frontiers Conference
May 15 - 17, 2017
Siena, Italy

Acceptance Rates

CF'17 Paper Acceptance Rate 43 of 87 submissions, 49%;
Overall Acceptance Rate 273 of 785 submissions, 35%

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