This paper describes the motivation, architecture and performance of a distributed virtual machine (DVM) for networked computers. DVMs rely on a distributed service architecture to meet the manageability, security and uniformity requirements of large, heterogeneous clusters of networked computers. In a DVM, system services, such as verification, security enforcement, compilation and optimization, are factored out of clients and located on powerful network servers. This partitioning of system functionality reduces resource requirements on network clients, improves site security through physical isolation and increases the manageability of a large and heterogeneous network without sacrificing performance. Our DVM implements the Java virtual machine, runs on x86 and DEC Alpha processors and supports existing Java-enabled clients.

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%.

This thesis addresses the challenges of building a software system for general-purpose runtime code manipulation. Modern applications, with dynamically-loaded modules and dynamicallygenerated code, are assembled at runtime. While it was once feasible at compile time to observe and manipulate every instruction — which is critical for program analysis, instrumentation, trace gathering, optimization, and similar tools — it can now only be done at runtime. Existing runtime tools are successful at inserting instrumentation calls, but no general framework has been developed for fine-grained and comprehensive code observation and modification without high overheads. This thesis demonstrates the feasibility of building such a system in software. We present DynamoRIO, a fully-implemented runtime code manipulation system that supports code transformations on any part of a program, while it executes. DynamoRIO uses code caching technology to provide efficient, transparent, and comprehensive manipulation of an unmodified application running on a stock operating system and commodity hardware. DynamoRIO executes large, complex, modern applications with dynamically-loaded, generated, or even modified code. Despite the

Dynamic program optimization offers 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 Adaptive 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 compilation 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 programs from the SPEC floating 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 code.

IA-32 Execution Layer (IA-32 EL) is a new technology that executes IA-32 applications on Intel Itanium processor family systems. Currently, support for IA-32 applications on Itanium-based platforms is achieved using hardware circuitry on the Itanium processors. This capability will be enhanced with IA-32 EL---software that will ship with Itanium-based operating systems and will convert IA-32 instructions into Itanium instructions via dynamic translation. In this paper, we describe aspects of the IA-32 Execution Layer technology, including the general two-phase translation architecture and the usage of a single translator for multiple operating systems. The paper provides details of some of the technical challenges such as precise exception, emulation of FP, MMX^TM, and Intel Streaming SIMD Extension instructions, and misalignment handling. Finally, the paper presents some performance results.

Software is often built without specification. Tools to automatically extract specification from software are needed and many techniques have been proposed. One type of these specifications – temporal API specification – is often specified in the form of automaton. There has been much work on reverse engineering or mining software temporal specification, using dynamic analysis techniques; i.e., analysis of software program traces. Unfortunately, the issues of scalability, robustness and accuracy of these techniques have not been comprehensively addressed. In this paper, we describe QUARK(QUality Assurance framewoRK) that enables assessments of the performance of a specification miner in generating temporal specification of software through traces recorded from its API interaction. QUARK requires the temporal specification produced by the miner to be expressed as an automaton. It accepts a user-defined simulator automaton and a specification miner. It produces quality assurance measures on the specification generated by the miner. Extensive experiments on 3 specification miners have been performed to demonstrate the usefulness of our proposed framework. 1

Abstract. We describe a new dynamic software scheduling technique for VLIW architectures, which compiles into VLIW code the program paths that are actually executed. Unlike trace processors, or DIF, the technique executes operations speculatively on multiple paths through the code, is resilient to branch mispredictions, and can achieve very large dynamic window sizes necessary for high ILP. Aggressive optimizations are applied to frequently executed portions of the code. Encouraging performance results were obtained on SPECint95 and TPC-C. Thetechnique can be used for binary translation for achieving architectural compatibility with an existing processor, or as a VLIW scheduling technique in its own right. Keywords: Instruction-level parallelism, Dynamic compilation, Binary translation, Superscalar

We describe several optimizations which can be employed in a dynamic binary translation (DBT) system, where low compilation/translation overhead is essential. These optimizations achieve a high degree of ILP, sometimes even surpassing a static compiler employing more sophisticated, and more time-consuming algorithms [9]. We present results in which we employ these optimizations in a dynamic binary translation system capable of computing oracle parallelism.

Java applications ⋆

A distributed Java Virtual Machine supports transparent parallel execution of multi-threaded Java programs on a cluster of computers...