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  1. Proceedings of the 6th International Workshop on Performance Modeling, Benchmarking, and Simulation of High Performance Computing Systems (PMBS '15)
  2. Guided profiling for auto-tuning array layouts on GPUs
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Performance evaluation of the IBM POWER8 architecture to support computational neuroscientific application using morphologically detailed neurons
Performance analysis of OpenMP on a GPU using a CORAL proxy application
Examining recent many-core architectures and programming models using SHOC
Automatic loop kernel analysis and performance modeling with Kerncraft
Techniques for modeling large-scale HPC I/O workloads
Characterizing node orderings for improved performance
Simulating stencil-based application on future Xeon Phi processor
ARMv8 micro-architectural design space exploration for high performance computing using fractional factorial
Guided profiling for auto-tuning array layouts on GPUs

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Guided profiling for auto-tuning array layouts on GPUs

Content Provider ACM Digital Library
Author Weber, Nicolas Goesele, Michael Amend, Sandra C.
Abstract Auto-tuning for Graphics Processing Units (GPUs) has become very popular in recent years. It removes the necessity to hand-tune GPU code especially when a new hardware architecture is released. Our auto-tuner optimizes memory access patterns. This is a key aspect to exploit the full performance of modern GPUs. As the memory hierarchy has historically changed in nearly every GPU generation, it was necessary to reoptimize the code for all of these new architectures. Unfortunately, the solution space for memory optimizations in large applications can easily reach millions of configurations for a single kernel. This vast number of implementations cannot be fully evaluated in a feasible time. In this paper we present an adaptive profiling algorithm that aims at finding a near optimal configuration within a fraction of the global optimum, while reducing the profiling time by several orders of magnitude compared to an exhaustive search. Our algorithm is aimed at and evaluated on large real-world applications.
Starting Page 1
Ending Page 11
Page Count 11
File Format PDF
ISBN 9781450340090
DOI 10.1145/2832087.2832093
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2015-11-15
Publisher Place New York
Access Restriction Subscribed
Content Type Text
Resource Type Article
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