This work proposes Pointy, a software assisted hardware pointer prefetcher for Java applications. Pointy exploits the strengths of both software and hardware. Its runtime software component communicates points-to relationships between objects to the underlying hardware. This points-to information is maintained and tracked in any managed runtime that implements automatic garbage collection. Pointy stores the object connectivity information in a separate hardware structure and uses it to generate timely pointer prefetches. To achieve a low-cost hardware implementation, Pointy spills the object metadata to the conventional memory hierarchy and retrieves it only when needed. Taking advantage of its hybrid design, Pointy can selectively communicate points-to metadata to the hardware based on class profiling that is readily available at the runtime level, while impractical to extract at the hardware level.

Heterogeneous systems show a lot of promise for extracting high-performance by combining the benefits of conventional architectures with specialized accelerators in the form of graphics processors (GPUs) and reconfigurable hardware (FPGAs). Extracting this performance often entails programming in disparate languages and models, making it hard for a programmer to work equally well on all aspects of an application. Further, relatively little attention is paid to co-execution---the problem of orchestrating program execution using multiple distinct computational elements that work seamlessly together.

Content-based architectures for XML data dissemination are gaining increasing attention both in academia and industry. These dissemination networks are the building blocks of selective information dissemination applications which have wide applicability such as sharing and integrating information in both scientific and corporate domains. At the heart of these dissemination services is a fast engine for matching of an incoming XML message against stored XPath expressions to determine interested consumers for the message. To achieve the ultra-low response time, predominant in financial message processing, the XPath expression matching must be done efficiently. In this paper, we develop and evaluate a novel algorithm based on a unique encoding of XPath expressions and XML messages, unlike dominating automaton-based algorithms, for efficiently solving this matching problem. We demonstrate a matching time in the millisecond range for millions of XPath expressions which significantly outperforms state-of-the-art algorithms.

Modern processors use branch target buffers (BTBs) to predict the target address of branches such that they can fetch ahead in the instruction stream increasing concurrency and performance. Ideally, BTBs would be sufficiently large to capture the entire working set of the application and sufficiently small for fast access and practical on-chip dedicated storage. Depending on the application, these requirements are at odds.

Many hardware optimizations rely on collecting information about program behavior at runtime. This information is stored in lookup tables. To be accurate and effective, these optimizations usually require large dedicated on-chip tables. Although technology advances offer an increased amount of on-chip resources, these resources are allocated to increase the size of on-chip conventional cache hierarchies.

The decoupling of producers and consumers in time and space in the publish/subscribe paradigm lends itself well to the support of mobile users who roam about the environment and have intermittent network connectivity. This paper identifies the factors that affect the performance of a distributed publish/subscribe architecture supporting mobility; formalizes mobility algorithms for distributed publish/subscribe systems and develops and evaluates optimizations that reduce the costs associated with supporting mobility in publish/subscribe systems. In our analysis, we focus on the "unicast" traffic generated to support mobile users, as opposed to the regular "multicast" traffic used for event dissemination to stationary clients. We find that the network capacity must be doubled to handle the extra load of just 10% of mobile users.