The COSMOS simulator provides fast and accurate switch-level modeling of MOS digital circuits. It attains high performance by preprocessing the transistor network into a functionally equivalent Boolean representation.This description, produced by the symbolic analyzer ANAMOS, captures all aspects of switch-level networks including bidirectional transistors, stored charge, different signal strengths, and indeterminate (X) logic values. The LGCC program translates the Boolean representation into a set, of machine language evaluation procedures and initialized data structures.These procedures and data structures are compiled along with code implementing the simulation kernel and user interface fo produce the simulation program.The simulation program runs an order of magnitude faster than our previous simulator MOSSIM II. 24th ACM/IEEE Design Automation Conference Paper 2.2 0 I 987 ACM 0738-IOOX/87/0600-0009$00.75 9

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Alignment and distribution of data by an optimizing compiler is a dream of both manufacturers and users of parallel computers. The distribution problem has been formulated as an NP-complete graph optimization problem. The graphs arising in applications are large, and the optimization problem does not lend itself to traditional heuristic optimization techniques. In this paper, we improve some earlier results on methods that use graph contraction to reduce the size of a distribution problem. We report on an experiment using seven example programs that show these contraction operations to be effective in practice; we obtain from 60 to 99 percent reductions in problem size, the larger number being more typical, without loss of solution quality.

Axis and stride alignment is an important optimization in compiling data-parallel programs for distributed-memory machines. We previously developed an optimal algorithm for aligning array expressions. Here, we examine alignment for more general program graphs. We show that optimal alignment is NP-complete in this setting, so we study heuristic methods.This paper makes two contributions. First, we show how local graph transformations can reduce the size of the problem significantly without changing the best solution. This allows more complex and effective heuristics to be used. Second, we give a heuristic that can explore the space of possible solutions in a number of ways. We show that some of these strategies can give better solutions than a simple greedy approach proposed earlier. Our algorithms have been implemented; we present experimental results showing their effect on the performance of some example programs running on the CM-5.

This paper describes an implementation technique for integrating nested data parallelism into an object-oriented language. Data-parallel programming employs data aggregates called "collections '' and expresses parallelism as operations pelformed over the elements of a collection. When the elements of a collection are also collections, then there is the possibility for "nested data parallelism. '' Few current programming languages support nested data parallelism howeverIn an object-orientedframework, a collection is a single object. Its type defines the parallel operations that may be applied to it. Our goal is to design and build an objectoriented data-parallel programming environment supporting nested data parallelism. Our initial appmach is built upon three fundamental additions to C++. We add new parallel base types by implementing them as classes, and add a new parallel collection type called a "vector" that is implemented as a template. Only one new language feature is introduced: the f o r e a c h construct, which is the basis for exploiting elementwise parallelism over collections.The strength of the method fies in fhe compilation strategy, which translates nested data-parallel C+ + into ordinary C+ +. Extracting the potential parallelism in nested foreach constructs is called "Jattening '' nested parallelism.. We show how toyatten foreach constructs using a simple program transfonnation. Our prototype system produces vector code which has been successfully run on workstations. a CM-2 and a CM-5.