Traditional compiler techniques for pointer-intensive programs focus 
on disambiguating, reordering, or optimizing individual loads and 
stores, data elements, and data types. We describe an alternative 
approach we call 'Macroscopic Data Structure Transformations,' which 
are program analyses and transformations that operate at the level of 
_entire_ recursive data structures. The foundations of this approach 
are

(1) Data Structure Analysis (DSA): a fast, context-sensitive analysis 
with some novel features, which extracts key properties of data 
structure instances; and

(2) Automatic Pool Allocation: a new program transformation that 
gives the compiler partial control over data structure layout in the 
heap.

Automatic Pool Allocation itself improves performance significantly 
for heap-intensive programs but, more importantly, gives the compiler 
data layout information that enables other analyses and 
transformations that were not possible before. We describe several 
novel examples of 'macroscopic' optimization techniques enabled by 
Automatic Pool Allocation. We have also used DSA and Pool Allocation 
to enforce memory safety without garbage collection in imperative 
programs, and we are building a secure, language-independent 
programming platform called SAFECode based on these techniques. These 
diverse applications provide evidence that the macroscopic data 
structure approach opens up a broad new class of compiler techniques 
for pointer-intensive programs.