Trap architectures for Lisp systems

Douglas Johnson
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引用次数: 21

Abstract

Recent measurements of Lisp systems show a dramatic skewing of operation frequency. For example, small integer (fix-num) arithmetic dominates most programs, but other number types can occur on almost any operation. Likewise, few memory references trigger special handling for garbage collection, but nearly all memory operations could trigger such special handling. Systems like SPARC and SPUR have shown that small amounts of special hardware can significantly reduce the need for inline software checks by trapping when an unusual condition is detected. A system's trapping architecture now becomes key to performance. In most systems, the trap architecture is intended to handle errors (e.g., address faults) or conditions requiring large amounts of processing (e.g., page faults). The requirements for Lisp traps are quite different. In particular, the trap frequency is higher, processing time per trap is shorter, and most need to be handled in the user's address space and context. This paper looks at these requirements, evaluates current trap architectures, and proposes enhancements for meeting those requirements. These enhancements increase performance for Lisp 11%-35% at a cost of about 1.6% more CPU logic. They also aid debugging in general and speed floating point exception handling.
Lisp系统的陷阱体系结构
最近对Lisp系统的测量显示了工作频率的显著倾斜。例如,小整数(固定数目)算术在大多数程序中占主导地位,但其他数字类型几乎可以出现在任何操作中。同样,很少有内存引用会触发垃圾收集的特殊处理,但几乎所有内存操作都可能触发这种特殊处理。像SPARC和SPUR这样的系统已经表明,少量的特殊硬件可以通过在检测到异常情况时捕获来显著减少对内联软件检查的需求。系统的捕获体系结构现在成为性能的关键。在大多数系统中,陷阱架构旨在处理错误(例如,地址错误)或需要大量处理的条件(例如,页面错误)。对Lisp陷阱的要求是完全不同的。特别是,陷阱频率更高,每个陷阱的处理时间更短,并且大多数需要在用户的地址空间和上下文中处理。本文研究了这些需求,评估了当前的陷阱体系结构,并提出了满足这些需求的增强建议。这些增强使Lisp的性能提高了11%-35%,但代价是增加了1.6%的CPU逻辑。它们还有助于一般调试并加快浮点异常处理速度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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