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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The Jalapeiio Dynamic Optimizing Compiler is a key component of the Jalapeiio Virtual Machine, a new Java' Virtual Machine (JVM) designed to support efficient and scalable execution of Java applications on SMP server machines. This paper describes the design of the Jalapefio Optimizing Compiler, and the implementation results that we have obtained thus far. To the best of our knowledge, this is the first dynamic optimizing compiler for Java that is being used in a JVM with a compile-only approach to program execution.

We present practical approximation methods for computing and representing interprocedural aliases for a program written in a language that includes pointers, reference parameters, and recursion. We present the following contributions: (1) a framework for interprocedural pointer alias analysis that handles function pointers by constructing the program call graph while alias analysis is being performed; (2) a flow-sensitive interprocedural pointer alias analysis algorithm;(3) a flow-insensitive interprocedural pointer alias analysis algorithm; (4) a flow-insensitive interprocedural pointer alias analysis algorithm that incorporates kill information to improve precision; (5) empirical measurements of the efficiency and precision of the three interprocedural alias analysis algorithms.

We introduce the Concurrent Collections (CnC) programming model. CnC supports flexible combinations of task and data parallelism while retaining determinism. CnC is implicitly parallel, with the user providing high-level operations along with semantic ordering constraints that together form a CnC graph. We formally describe the execution semantics of CnC and prove that the model guarantees deterministic computation. We evaluate the performance of CnC implementations on several applications and show that CnC offers performance and scalability equivalent to or better than that offered by lower-level parallel programming models.

This paper shows how finite approximations of long distance dependency (LDD) resolution can be obtained automatically for wide-coverage, robust, probabilistic Lexical-Functional Grammar (LFG) resources acquired from treebanks. We extract LFG subcategorisation frames and paths linking LDD reentrancies from f-structures generated automatically for the Penn-II treebank trees and use them in an LDD resolution algorithm to parse new text. Unlike (Collins, 1999;Johnson, 2002), in our approach resolution of LDDs is done at f-structure (attribute-value structure representations of basic predicate-argument or dependency structure) without empty productions, traces and coindexation in CFG parse trees. Currently our best automatically induced grammars achieve 80.97% f-score for fstructures parsing section 23 of the WSJ part of the Penn-II treebank and evaluating against the DCU 105 1 and 80.24% against the PARC 700 Dependency Bank (King et al., 2003), performing at the same or a slightly better level than state-of-the-art hand-crafted grammars (Kaplan et al., 2004).

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