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This paper describes partition allocation for parallel jobs in the Blue Genet/L supercomputer. It describes the novel network architecture of the Blue Gene/L (BG/L) three-dimensional (3D) computational core and presents a preliminary analysis of its properties and advantages compared those of with more traditional systems. The scalability challenge is solved in BG/L by sacrificing granularity of system management. The system is treated as a collection of composite allocation units that contain both processing and communication resources. We discuss the ensuing algorithmic framework for computational and communication resource allocation and present results of simulations that explore resource utilization of BG/L for different workloads. We find that utilization depends strongly on both the predominant partition topology (mesh or torus) and the 3D shapes requested by the running jobs. When communication links are treated as dedicated resources, it is much more difficult to allocate toroidal partitions than mesh ones, especially for jobs of more than one allocation unit in each dimension. We show that in these difficult cases, the advantage of BG/L compared with a 3D toroidal machine of the same size is very significant, with resource utilization better by a factor of 2. In the easier cases (e.g., predominantly mesh partitions), there are no disadvantages. The advantage is primarily due to the BG/L novel multi-toroidal topology that permits coallocation of multiple toroidal partitions at negligible additional cost.

Mobile agent technology makes it possible to reduce network traffic, overcome network latencies and enhance robustness and faulttolerant capabilities of distributed applications. However, it is sometimes difficult or even impossible to take full advantage of these technical benefits because of the lack of an appropriate infrastructure for overcoming problems related to connectivity (e.g. access through firewalls), security, location transparency, and use of proprietary tools. This paper discusses these problems and introduces the requirements for various infrastructure components and their implementation with the aim of enhancing the practicality and accelerating the deployment of mobile agents.

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cJVM is a Java Virtual Machine (JVM) which provides a single system image of a traditional JVM while executing in a distributed fashion on the nodes of a cluster, cJVM virtualizes the cluster, transparently distributing the objects and threads of any pure Java application. The aim of cJVM is to obtain improved scalability for Java Server Applications by distributing the application's work among the cluster's computing resources.cJVM's architecture, its unique object model, thread and memory models were described in [6]. In this article we focus on the optimization techniques employed in cJVM to achieve high scalability. In particular, we focus on the techniques used to enhance locality thereby reducing the amount of communication generated by cJVM. In addition, we describe how communication overhead can be reduced by taking advantage of Java semantics. Our optimization techniques are based on three principles. First, we employ a large number of mostly simple optimizations which address caching, locality of execution and object migration. Second, we take advantage of the Java semantics and of common usage patterns in implementing the optimizations. Third, we use speculative optimizations, taking advantage of the fact that the cJVM run-time environment can correct false speculations.We have demonstrated the usefulness of these techniques on a large (10Kloc) Java application, achieving 80% efficiency on a four-node cluster. This paper discusses the various techniques used and reports our results.