论文标题

简约引擎:第二部分;传达可逆计算机的性能权衡

Engines of Parsimony: Part II; Performance Trade-offs for Communicating Reversible Computers

论文作者

Earley, Hannah

论文摘要

在本系列的第一部分中,研究了大型可逆计算机的持续性能的限制,并发现扩展为$ \ sqrt {av} $,其中$ a $是系统的凸面边界表面积和$ v $ $ v $ ITS内部体积,与不可交换计算机的$ a $相比。但是,该分析忽略了考虑系统组件之间的相互作用,而是专注于原始计算能力。在这一部分中,我们扩展了此分析,以考虑同步事件,例如受到限制自由能供应的独立可逆处理器之间的通信。已经发现,虽然异步计算可以以$bλ$的速率进行,但同步事件以较慢的速率$ \ sim b^2λ$进行;在这些速率表达式中,$λ$是每个处理器的总过渡速率,$ b \ sim \ sqrt {a/v} \ ll1 $是测量成功的净分数的“计算偏见”。虽然用于布朗可逆计算机,但此结果适用于所有形式的可逆计算机,包括量子计算机。实际上,该结果是一个上限,必须仔细选择同步事件的相空间几何形状,以避免性能更糟。因此,在大型计算机的限制下,通信将倾向于冻结为$ b \ to0 $;但是,如果人们愿意限制允许在任何给定时间共享状态的处理器数量,那么可以改善此速率,并且可以恢复与异步计算相当的性能。

In Part I of this series, the limits on the sustained performance of large reversible computers were investigated and found to scale as $\sqrt{AV}$ where $A$ is the convex bounding surface area of the system and $V$ its internal volume, compared to $A$ for an irreversible computer. This analysis neglected to consider interactions between components of the system however, instead focussing on raw computational power. In this part we extend this analysis to consider synchronisation events such as communication between independent reversible processors subject to a limiting supply of free energy. It is found that, whilst asynchronous computation can proceed at a rate $bλ$, synchronisation events proceed at the much slower rate $\sim b^2λ$; in these rate expressions, $λ$ is the gross transition rate for each processor and $b\sim\sqrt{A/V}\ll1$ is the 'computational bias' measuring the net fraction of transitions which are successful. Whilst derived for Brownian reversible computers, this result applies to all forms of reversible computer, including Quantum computers. In fact this result is an upper bound, and one must choose the phase space geometry of the synchronisation events carefully to avoid even worse performance. In the limit of large computers, communication will therefore tend to freeze out as $b\to0$; if, however, one is willing to restrict the number of processors permitted to share state at any given time then this rate can be ameliorated and performance on par with asynchronous computation can be recovered.

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