... a compiler framework designed to support transparent, lifelong program analysis and transformation for arbitrary programs, by providing high-level information to compiler transformations at compile-time, link-time, run-time, and in idle time between runs. LLVM defines a common, low-level code representation in Static Single Assignment (SSA) form, with several novel features: a simple, language-independent type-system that exposes the primitives commonly used to implement high-level language features; an instruction for typed address arithmetic; and a simple mechanism that can be used to implement the exception handling features of high-level languages (and setjmp/longjmp in C) uniformly and efficiently. The LLVM compiler framework and code representation together provide a combination of key capabilities that are important for practical, lifelong analysis and transformation of programs. To our knowledge, no existing compilation approach provides all these capabilities. We describe the design of the LLVM representation and compiler framework, and evaluate the design in three ways: (a) the size and effectiveness of the representation, including the type information it provides; (b) compiler performance for several interprocedural problems; and (c) illustrative examples of the benefits LLVM provides for several challenging compiler problems.

Providing high performance for pointer-intensive programs on modern architectures is an increasingly difficult problem for compilers. Pointer-intensive programs are often bound by memory latency and cache performance, but traditional approaches to these problems usually fail: Pointer-intensive programs are often highly-irregular and the compiler has little control over the layout of heap allocated objects. This thesis presents a new class of techniques named “Macroscopic Data Structure Analyses and Optimizations”, which is a new approach to the problem of analyzing and optimizing pointerintensive programs. Instead of analyzing individual load/store operations or structure definitions, this approach identifies, analyzes, and transforms entire memory structures as a unit. The foundation of the approach is an analysis named Data Structure Analysis and a transformation named Automatic Pool Allocation. Data Structure Analysis is a context-sensitive pointer analysis which identifies data structures on the heap and their important properties (such as type safety). Automatic Pool Allocation uses the results of Data Structure Analysis to segregate dynamically allocated objects on the heap, giving control over the layout of the data structure in memory to the compiler. Based on these two foundation techniques, this thesis describes several performance improving