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1.
  • Benkner, S., et al. (författare)
  • Peppher: Performance Portability and Programmability for Heterogeneous Many-Core Architectures
  • 2017
  • Ingår i: Programming Multicore and Many-Core Computing Systems. - Hoboken, NJ, USA : John Wiley & Sons, Inc.. - 9781119332015 - 9780470936900 ; , s. 241-260
  • Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
    • © 2017 by John Wiley & Sons, Inc. All rights reserved. PEPPHER takes a pluralistic and parallelization agnostic approach to programmability and performance portability for heterogeneous many-core architectures. The PEPPHER framework is in principle language independent but focuses on supporting C++ code with PEPPHER-specific annotations as pragmas or external annotations. The framework is open and extensible; the PEPPHER methodology details how new architectures are incorporated. The PEPPHER methodology consists of rules for how to extend the framework for new architectures. This mainly concerns adaptivity and autotuning for algorithm libraries, the necessary hooks and extensions for the run-time system and any supporting algorithms and data structures that this relies on. Offloading is a specific technique for programming heterogeneous platforms that can sometimes be applied with high efficiency. Offload as developed by the PEPPHER partner Codeplay is a particular, nonintrusive C++ extension allowing portable C++ code to support diverse heterogeneous multicore architectures in a single code base.
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2.
  • Benkner, S., et al. (författare)
  • The PEPPHER approach to programmability and performance portability for heterogeneous many-core architectures
  • 2012
  • Ingår i: Advances in Parallel Computing. - : IOS Press. - 1879-808X .- 0927-5452. ; 22, s. 361-368, s. 361-368
  • Konferensbidrag (refereegranskat)abstract
    • The European FP7 project PEPPHER is addressing programmability and performance portability for current and emerging heterogeneous many-core architectures. As its main idea, the project proposes a multi-level parallel execution model comprised of potentially parallelized components existing in variants suitable for different types of cores, memory configurations, input characteristics, optimization criteria, and couples this with dynamic and static resource and architecture aware scheduling mechanisms. Crucial to PEPPHER is that components can be made performance aware, allowing for more efficient dynamic and static scheduling on the concrete, available resources. The flexibility provided in the software model, combined with a customizable, heterogeneous, memory and topology aware run-time system is key to efficiently exploiting the resources of each concrete hardware configuration. The project takes a holistic approach, relying on existing paradigms, interfaces, and languages for the parallelization of components, and develops a prototype framework, a methodology for extending the framework, and guidelines for constructing performance portable software and systems-including paths to migration of existing software-for heterogeneous many-core processors. This paper gives a high-level project overview, and presents a specific example showing how the PEPPHER component variant model and resource-aware run-time system enable performance portability of a numerical kernel. © 2012 The authors and IOS Press. All rights reserved.
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3.
  • Cojean, T., et al. (författare)
  • Resource Aggregation for Task-Based Cholesky Factorization on Top of Heterogeneous Machines
  • 2017
  • Ingår i: Euro-Par 2016. - Cham : Springer Nature. - 9783319589435 - 9783319589428 ; , s. 56-68
  • Konferensbidrag (refereegranskat)abstract
    • Hybrid computing platforms are now commonplace, featuring a large number of CPU cores and accelerators. This trend makes balancing computations between these heterogeneous resources performance critical. In this paper we propose aggregating several CPU cores in order to execute larger parallel tasks and thus improve the load balance between CPUs and accelerators. Additionally, we present our approach to exploit internal parallelism within tasks. This is done by combining two runtime systems: one runtime system to handle the task graph and another one to manage the internal parallelism. We demonstrate the relevance of our approach in the context of the dense Cholesky factorization kernel implemented on top of the StarPU task-based runtime system. We present experimental results showing that our solution outperforms state of the art implementations.
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  • Resultat 1-3 av 3

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