Jalapeño is a virtual machine for Java TM servers written in the Java language. To be able to address the requirements of servers (performance and scalability in particular), Jalapeño was designed "from scratch" to be as self-sufficient as possible. Jalapeño's unique object model and memory layout allows a hardware null-pointer check as well as fast access to array elements, fields, and methods. Run-time services conventionally provided in native code are implemented primarily in Java. Java threads are multiplexed by virtual processors (implemented as operating system threads). A family of concurrent object allocators and parallel type-accurate garbage collectors is supported. Jalapeño's interoperable compilers enable quasi-preemptive thread switching and precise location of object references. Jalapeño's dynamic optimizing compiler is designed to obtain high quality code for methods that are observed to be frequently executed or computationally intensive.

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Many distributed and parallel applications can make good use of broadcast communication. In this paper we present a (software) protocol that simulates reliable broadcast, even on an unreliable network. Using this protocol, application programs need not worry about lost messages. Recovery of communication failures is handled automatically and transparently by the protocol. In normal operation, our protocol is more efficient than previously published reliable broadcast protocols. An initial implementation of the protocol on 10 MC68020 CPUs connected by a 10 Mbit/sec Ethernet performs a reliable broadcast in 1.5 msec.

Although N-body simulation algorithms are amenable to parallelization, performance gains from execution on parallel machines are di cult to obtain due to load imbalances caused by irregular distributions of bodies. In general, there is a tension between balancing processor loads and maintaining locality, as the dynamic re-assignment o f w ork necessitates access to remote data. Fractiling is a dynamic scheduling scheme that simultaneously balances processor loads and maintains locality b y exploiting the self-similarity properties of fractals. Fractiling is based on a probabilistic analysis, and thus, accommodates load imbalances caused by predictable phenomena, such as irregular data, and unpredictable phenomena, such as data-access latencies. In experiments on a KSR1, performance of N-body simulation codes were improved by a s m uch as 53% by fractiling. Performance improvements were obtained on uniform and nonuniform distributions of bodies, underscoring the need for a scheduling scheme that accommodates system induced variance. As the fractiling scheme is orthogonal to the N-body algorithm, we could use simple codes that discretize space into equal-size subrectangles (2-d) or subcubes (3-d) as the base algorithms.