No abstract

Dynamic program optimization o ers performance improvements far beyond those possible with traditional compile-time optimization 1, 2, 3, 4 . These gains are due to the ability to exploit both architectural and input data set characteristics that are unknown prior to execution time. In this paper, we propose a novel framework for dynamic program optimization, ADAPT Automated De-coupled A daptive Program Transformation, that builds on the strengths of existing approaches. The key to our framework is the de-coupling of the dynamic compilation of new code variants from the dynamic selection of these variants at their points of use. This allows code generation to occur concurrently with program execution, removing dynamic compilation overheads from the critical path. We present a c ompilation system, based on the Polaris optimizing compiler 5 , that automatically applies this framework to general plugged-in" optimization techniques. We evaluate our system on three p r ograms from the SPEC oating point benchmark suite by dynamically applying loop distribution, loop unrolling, loop tiling and automatic parallelization. We show that our techniques can improve performance by as much as 70 over statically optimized c ode.

Compile-time optimization is often limited by a lack of target machine and input data set knowledge. Without this information, compilers may be forced to make conservative assumptions to preserve correctness and to avoid performance degradation. In order to cope with this lack of information at compile-time, adaptive and dynamic systems can be used to perform optimization at runtime when complete knowledge of input and machine parameters is available. This paper presents a compiler-supported high-level adaptive optimization system. Users describe, in a domain specific language, optimizations performed by stand-alone optimization tools and backend compiler flags, as well as heuristics for applying these optimizations dynamically at runtime. The ADAPT compiler reads these descriptions and generates application-specific runtime systems to apply the heuristics. To facilitate the usage of existing tools and compilers, overheads are minimized by decoupling optimization from execution. Our system, ADAPT, supports a range of paradigms proposed recently, including dynamic compilation, parameterization and runtime sampling. We demonstrate our system by applying several optimization techniques to a suite of benchmarks on two target machines. ADAPT is shown to consistently outperform statically generated executables, improving performance by as much as 70%.

The poor inter-observer agreement for the diagnosis of pulmonary parenchymal abnormalities on chest radiographs of neonates receiving intensive care suggests that abnormalities should be described rather than diagnoses given or that a list of differential diagnoses be offered. When research involves radiographic interpretation, the potential lack of consistency in reporting abnormalities must be borne in mind.

Compile-time optimization is often limited by a lack of target machine and input data set knowledge. Without this information, compilers may be forced to make conservative assumptions to preserve correctness and to avoid performance degradation. In order to cope with this lack of information at compile-time, adaptive and dynamic systems can be used to perform optimization at runtime when complete knowledge of input and machine parameters is available. This paper presents a compiler-supported high-level adaptive optimization system. Users describe, in a domain specific language, optimizations performed by stand-alone optimization tools and backend compiler flags, as well as heuristics for applying these optimizations dynamically at runtime. The ADAPT compiler reads these descriptions and generates application-specific runtime systems to apply the heuristics. To facilitate the usage of existing tools and compilers, overheads are minimized by decoupling optimization from execution. Our system, ADAPT, supports a range of paradigms proposed recently, including dynamic compilation, parameterization and runtime sampling. We demonstrate our system by applying several optimization techniques to a suite of benchmarks on two target machines. ADAPT is shown to consistently outperform statically generated executables, improving performance by as much as 70%.