演示:支持slp的字长优化

Ali Hassan El Moussawi, Steven Derrien
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引用次数: 2

摘要

许多嵌入式处理器不支持浮点运算。但是它们通常提供对SIMD的支持,以此作为提高性能的一种手段,并且成本开销接近于零。当瞄准这些处理器时,要获得良好的性能需要使用定点算法和有效的simdiization。为了缩短应用程序的上市时间,已经提出了自动simization和浮点转换方法。在本文中,我们证明了这两个问题是密切相关的,应该共同考虑。我们简要地提出了一种新的感知slp的浮点转换流程。
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Demo: SLP-aware word length optimization
Many embedded processors do not support floating-point arithmetic. But they generally provide support for SIMD as a mean to improve performance for near-zero cost overhead. Achieving good performance when targeting such processors requires the use of fixed-point arithmetic and efficient SIMDization. To reduce applications time-to-market, automatic SIMDization and floating-point conversion methodologies have been proposed. In this paper we show that these two problems are strongly related and should be considered jointly. We briefly present a new SLP-aware floating-point conversion flow.
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