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譯文：
很多用戶遇到的問題是可伸縮性。當項目模型在實際使用的時候變得非常大時，就會遇到可伸縮性的問題。當運行變得遲鈍，哪怕很小規(guī)模的運行也會非常費力。建筑模型用掉了電腦很多內存空間。大型建筑會包含上百萬個不同形狀的構件。可伸縮性同時受建模規(guī)模（比如樓層面積）和模型中的詳細程度影響。即使在一個簡單的建筑中，如果對每個釘子和螺絲都建模，也會出現可伸縮性問題。
參數化建模整合了設計規(guī)則，即將某一構件的形狀和參數關聯到另一個構件的形狀和參數中。這些規(guī)則形成了關系層次：構件內參數化關系、構件間參數化關系，根據另一個構件的變化調整某一構件的形狀，控制軸網和控制曲面之間的層次關系決定了形狀的參數和一組相關聯構件的放置。雖然構件內和構件間的參數化關系是構件的局部更新，但是這種層次規(guī)則的擴散可更新整個模型。局部參數化規(guī)則擴散只在模型上產生合理要求，然而某些系統(tǒng)架構卻限制了管理這種大批量層次規(guī)則傳播的能力。同樣，把項目分成若干部分來開展的同時仍然管理大批量層次關系是很困難的。
問題就在于內存大小。所有構件形狀方面的運行都必須占用內存。管理參數化更新的一個簡單方法就是把項目存儲在內存中。如此便對可伸縮性產生了挑戰(zhàn)，并且對可進行有效編輯的項目模塊之規(guī)模做出了實際限制。然而，如果規(guī)則可在文件之間進行傳播，即在某一文件中更新某一構件能致使其它文件自動更新，那么對項目規(guī)模大小的限制便會消失。僅有少量專門為建筑開發(fā)的應用程序有管理參數化變更在多文件之間傳播的手段。我們把必須將所有更新的構件同時存儲在內存中的系統(tǒng)稱為“基于內存”。當模型大到難以存儲在內存中時，就會出現虛擬內存交換，這會造成大量等待時間。其它系統(tǒng)有在文件之間傳播關系和更新的手段，并且能在編輯操作期間打開、更新以及關閉多個文件。我們稱這些系統(tǒng)為“基于文件”型系統(tǒng)。基于文件型系統(tǒng)運行小型項目一般會有些慢，但是隨著項目文件變大其運行速度降低的也慢。
用戶將項目分成若干模塊已經是一種久經考驗的共享工作之手段并且限制了自動更新的規(guī)模。參照文件往往也被用于限制可編輯的模塊。如果項目中的層次關系不會造成全局項目變更，這些操作是相當有效的。有些BIM工具加強了這些限制。
內存和處理方面的問題，將會隨著計算機運行越來越快而自然減少。64位處理器和操作系統(tǒng)也提供了重要的幫助。但與此同時，也會有更加細化的建筑模型和更大規(guī)模參數化組方面的需求?？缮炜s性方面的問題還會繼續(xù)存在一段時間。
原文：
A problem that many users encounter is scalability. Problems in scaling are encountered when a project model gets too large for practical use. Operations become sluggish, so that even simple operations are laborious. Building models take a lot of computer memory space. Large buildings can contain millions of objects, each with a different shape. Scalability is affected by both the size of the building, say in floor area, and also by the level of detail in the model. Even a simple building can encounter scalability problems if every nail and screw is modeled.
Parametric modeling incorporates design rules that relate geometry or other parameters of one object with those of other objects. These come in a hierarchy of relations: within object parametric relations, peer object relations, adjusting one object’s shape in response to the change of another object, and hierarchical relations between control grids and surfaces that determine the parameters of shape and placement of a set of associated objects. While within object and peer object relations update locally, hierarchical rule propagation may generate updates to the whole building. Local parametric rule propagation makes only reasonable demands on models, while some system architec- tures limit the ability to manage propagation of large sets of hierarchical rules.Also, it is hard to partition a project into parts for separate development and still manage a large set of hierarchical rules.
The issue is memory size; all operations on object shapes must take place in memory. The simple solution to manage parametric updates is to carry the project in memory. This challenges scalability and places practical limits on the size of a project module that can be effectively edited. However, if rules can be propagated across files, where updating an object in one file can lead to automatic updates propagated to other files, the size limitation of a project disap- pears. Only a few BIM design applications developed especially for architecture have the means for managing parametric change propagation across multiple files. We call systems that must carry all updated objects in memory simultaneously memory-based. When the model gets too large to be held in memory, virtual memory-swapping occurs, which can result in significant waiting time. Other systems have methods of propagating relations and updates across files and can open, update, and then close multiple files during an edit operation. These are called file-based systems. File-based systems are generally a bit slower for small projects but their speed decreases very slowly as project size grows.
User segmentation of projects into modules has been a time-tested way of sharing work and limiting the scale of automatic updates. Reference files are often used to also limit what can be edited. These work well if hierarchical relations in a project don’t lead to global project changes. Some BIM tools impose these limitations.
Memory and processing issues will naturally decrease as computers get faster. Sixty-four-bit processors and operating systems also provide significant help. There will be the parallel desire, however, for more detailed building models and larger sets of parametric rules. Issues of scalability will be with us for some time.
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