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Instrumenting code to collect profiling information can cause substantial execution overhead. This overhead makes instrumentation difficult to perform at runtime, often preventing many known offline feedback-directed optimizations from being used in online systems. This paper presents a general framework for performing instrumentation sampling to reduce the overhead of previously expensive instrumentation. The framework is simple and effective, using code-duplication and counter-based sampling to allow switching between instrumented and non-instrumented code.

Prefetching data ahead of use has the potential to tolerate the growing processor-memory performance gap by overlapping long latency memory accesses with useful computation. While sophisticated prefetching techniques have been automated for limited domains, such as scientific codes that access dense arrays in loop nests, a similar level of success has eluded general-purpose programs, especially pointer-chasing codes written in languages such as C and C++. We address this problem by describing, implementing and evaluating a dynamic prefetching scheme. Our technique runs on stock hardware, is completely automatic, and works for generalpurpose programs, including pointer-chasing codes written in weakly-typed languages, such as C and C++. It operates in three phases. First, the profiling phase gathers a temporal data reference profile from a running program with low-overhead. Next, the profiling is turned off and a fast analysis algorithm extracts hot data streams, which are data reference sequences that frequently repeat in the same order, from the temporal profile. Then, the system dynamically injects code at appropriate program points to detect and prefetch these hot data streams. Finally, the process enters the hibernation phase where no profiling or analysis is performed, and the program continues to execute with the added prefetch instructions. At the end of the hibernation phase, the program is deoptimized to remove the inserted checks and prefetch instructions, and control returns to the profiling phase. For long-running programs, this profile, analyze and optimize, hibernate, cycle will repeat multiple times. Our initial results from applying dynamic prefetching are promising, indicating overall execution time improvements of 5–19 % for several memory-performance-limited SPECint2000 benchmarks running their largest (ref) inputs.

This paper presents a framework for supporting the optimization of Java programs using attributes in Java class les. We show how class le attributes may be used to convey both optimization opportunities and prole information to a variety of Java virtual machines including ahead-of-time compilers and just-in-time compilers. We present our work in the context of Soot, a framework that supports the analysis and transformation of Java bytecode (class les)[21]. We demonstrate the framework with attributes for elimination of array bounds and null pointer checks, and we provide experimental results for the Kae just-in-time compiler, and IBM's High Performance Compiler for Java ahead-of-time compiler. 1 Introduction Java is a clean, portable, object-oriented language that is gaining wide spread acceptance. The target language for Java compilers is Java bytecode which is a platform-independent, stack-based intermediate representation. The bytecode is stored in Java class les, and...

Dynamic compilation and optimization are widely used in heterogenous computing environments, in which an intermediate form of the code is compiled to native code during execution. An important tradeoff exists between the amount of time spent dynamically optimizing the program and the running time of the program. The time to perform dynamic optimizations can cause significant delays during execution and also prohibit performance gains that result from more complex optimization.

This paper describes the implementation of an online feedback-directed optimization system. The system is fully automatic; it requires no prior...

The traditional tradeoff when performing dynamic compilation is that of fast compilation time versus fast code performance. Most dynamic compilation systems for Java perform selective compilation and/or optimization at a method granularity. This is the not the optimal granularity level. However, compiling at a sub-method granularity is thought to be too complicated to be practical. This paper describes...

The high performance implementation of Java Virtual Machines (JVM) and Just-In-Time (JIT) compilers is directed toward adaptive compilation optimizations on the basis of online runtime profile in-formation. This paper describes the design and implementation of a dynamic optimization framework in a production-level Java JIT compiler. Our approach is to employ a mixed mode interpreter and a three level optimizing compiler, supporting quick, full, and spe-cial optimization, each of which has a different set of tradeoffs be-tween compilation overhead and execution speed. A lightweight sampling profiler operates continuously during the entire program's execution. When necessary, detailed information on runtime behav-ior is collected by dynamically generating instrumentation code which can be installed to and uninstalled from the specified recom-pilation target code. Value profiling with this instrumentation mechanism allows fully automatic code specialization to be per-formed on the basis of specific parameter values or global data at the highest optimization level. The experimental results show that our approach offers high performance and a low code expansion ra-tio in both program startup and steady state measurements in com-parison to the compile-only approach, and that the code specializa-tion can also contribute modest pertbrmance improvements. 1.

Many opportunities exist to improve micro-architectural performance due to performance events that are di#cult to optimize at static compile time. Cache misses and branch mis-prediction patterns may vary for di#erent micro-architectures using di#erent inputs.

We introduce a new family of connectivity-based garbage collectors (Cbgc) that are based on potential objectconnectivity properties. The key feature of these collectors is that the placement of objects into partitions is determined by performing one of several forms of connectivity analyses on the program. This enables partial garbage collections, as in generational collectors, but without the need for any write barrier.