Research over the past five years has shown significant performance improvements using a technique called adaptive compilation. An adaptive compiler uses a compile-execute-analyze feedback loop to find the combination of optimizations and parameters that minimizes some performance goal, such as code size or execution time.Despite its ability to improve performance, adaptive compilation has not seen widespread use because of two obstacles: the large amounts of time that such systems have used to perform the many compilations and executions prohibits most users from adopting these systems, and the complexity inherent in a feedback-driven adaptive system has made it difficult to build and hard to use.A significant portion of the adaptive compilation process is devoted to multiple executions of the code being compiled. We have developed a technique called virtual execution to address this problem. Virtual execution runs the program a single time and preserves information that allows us to accurately predict the performance of different optimization sequences without running the code again. Our prototype implementation of this technique significantly reduces the time required by our adaptive compiler.In conjunction with this performance boost, we have developed a graphical-user interface (GUI) that provides a controlled view of the compilation process. By providing appropriate defaults, the interface limits the amount of information that the user must provide to get started. At the same time, it lets the experienced user exert fine-grained control over the parameters that control the system.

Most modern compilers operate by applying a fixed, program-independent sequence of optimizations to all programs. Compiler writers choose a single "compilation sequence", or perhaps a couple of compilation sequences. In choosing a sequence, they may consider performance of benchmarks or other important codes. These sequences are intended as general-purpose tools, accessible through command-line flags such as -O2 and -O3.Specific compilation sequences make a significant difference in the quality of the generated code, whether compiling for speed, for space, or for other metrics. A single universal compilation sequence does not produce the best results over all programs [8, 10, 29, 32]. Finding an optimal program-specific compilation sequence is difficult because the space of potential sequences is huge and the interactions between optimizations are poorly understood. Moreover, there is no systematic exploration of the costs and benefits of searching for good (i.e., within a certain percentage of optimal) program-specific compilation sequences.In this paper, we perform a large experimental study of the space of compilation sequences over a set of known benchmarks, using our prototype adaptive compiler. Our goal is to characterize these spaces and to determine if it is cost-effective to construct custom compilation sequences. We report on five exhaustive enumerations which demonstrate that 80% of the local minima in the space are within 5 to 10% of the optimal solution. We describe three algorithms tailored to search such spaces and report on experiments that use these algorithms to find good compilation sequences. These experiments suggest that properties observed in the enumerations hold for larger search spaces and larger programs. Our findings indicate that for the cost of 200 to 4,550 compilations, we can find custom sequences that are 15 to 25% better than the human-designed fixed-sequence originally used in our compiler.

This paper presents a fast new algorithm for modeling and reasoning about interferences for variables in a program without constructing an interference graph. It then describes how to use this information to minimize copy insertion for ϕ-node instantiation during the conversion of the static single assignment (SSA) form into the control-flow graph (CFG), effectively yielding a new, very fast copy coalescing and live-range identification algorithm.This paper proves some properties of the SSA form that enable construction of data structures to compute interference information for variables that are considered for folding. The asymptotic complexity of our SSA-to-CFG conversion algorithm is where-is the number of instructions in the program.Performing copy folding during the SSA-to-CFG conversion eliminates the need for a separate coalescing phase while simplifying the intermediate code. This may make graph-coloring register allocation more practical in just in time (JIT) and other time-critical compilers For example, Sun's Hotspot Server Compiler already employs a graph-coloring register allocator[10].This paper also presents an improvement to the classical interference-graph based coalescing optimization that shows adecrease in memory usage of up to three orders of magnitude and a decrease of a factor of two in compilation time, while providing the exact same results.We present experimental results that demonstrate that our algorithm is almost as precise (within one percent on average) as the improved interference-graph-based coalescing algorithm, while requiring three times less compilation time.