Cost-Effective Clustering

Small Beowulf clusters can effectively serve as personal or group supercomputers. In such an environment, a cluster can be optimally designed for a specific problem (or a small set of codes). We discuss how theoretical analysis of the code and benchmarking on similar hardware lead to optimal systems.Comment: 7 pages, 2 figures (one in color). Color version of paper to be published as part of proceedings of CCP2000 (Brisbane) in a special isssue of Computer Physics Communication

A scalable application server on Beowulf clusters : a thesis presented in partial fulfilment of the requirement for the degree of Master of Information Science at Albany, Auckland, Massey University, New Zealand

Application performance and scalability of a large distributed multi-tiered application is a core requirement for most of today's critical business applications. I have investigated the scalability of a J2EE application server using the standard ECperf benchmark application in the Massey Beowulf Clusters namely the Sisters and the Helix. My testing environment consists of Open Source software: The integrated JBoss-Tomcat as the application server and the web server, along with PostgreSQL as the database. My testing programs were run on the clustered application server, which provide replication of the Enterprise Java Bean (EJB) objects. I have completed various centralized and distributed tests using the JBoss Cluster. I concluded that clustering of the application server and web server will effectively increase the performance of the application running on them given sufficient system resources. The application performance will scale to a point where a bottleneck has occurred in the testing system, the bottleneck could be any resources included in the testing environment: the hardware, software, network and the application that is running. Performance tuning for a large-scale J2EE application is a complicated issue, which is related to the resources available. However, by carefully identifying the performance bottleneck in the system with hardware, software, network, operating system and application configuration. I can improve the performance of the J2EE applications running in a Beowulf Cluster. The software bottleneck can be solved by changing the default settings, on the other hand, hardware bottlenecks are harder unless more investment are made to purchase higher speed and capacity hardware

Using Pilot Systems to Execute Many Task Workloads on Supercomputers

High performance computing systems have historically been designed to support applications comprised of mostly monolithic, single-job workloads. Pilot systems decouple workload specification, resource selection, and task execution via job placeholders and late-binding. Pilot systems help to satisfy the resource requirements of workloads comprised of multiple tasks. RADICAL-Pilot (RP) is a modular and extensible Python-based pilot system. In this paper we describe RP's design, architecture and implementation, and characterize its performance. RP is capable of spawning more than 100 tasks/second and supports the steady-state execution of up to 16K concurrent tasks. RP can be used stand-alone, as well as integrated with other application-level tools as a runtime system

Construction and Application of an AMR Algorithm for Distributed Memory Computers

While the parallelization of blockstructured adaptive mesh refinement techniques is relatively straight-forward on shared memory architectures, appropriate distribution strategies for the emerging generation of distributed memory machines are a topic of on-going research. In this paper, a locality-preserving domain decomposition is proposed that partitions the entire AMR hierarchy from the base level on. It is shown that the approach reduces the communication costs and simplifies the implementation. Emphasis is put on the effective parallelization of the flux correction procedure at coarse-fine boundaries, which is indispensable for conservative finite volume schemes. An easily reproducible standard benchmark and a highly resolved parallel AMR simulation of a diffracting hydrogen-oxygen detonation demonstrate the proposed strategy in practice

A method of evaluation of high-performance computing batch schedulers

According to Sterling et al., a batch scheduler, also called workload management, is an application or set of services that provide a method to monitor and manage the flow of work through the system [Sterling01]. The purpose of this research was to develop a method to assess the execution speed of workloads that are submitted to a batch scheduler. While previous research exists, this research is different in that more complex jobs were devised that fully exercised the scheduler with established benchmarks. This research is important because the reduction of latency even if it is miniscule can lead to massive savings of electricity, time, and money over the long term. This is especially important in the era of green computing [Reuther18]. The methodology used to assess these schedulers involved the execution of custom automation scripts. These custom scripts were developed as part of this research to automatically submit custom jobs to the schedulers, take measurements, and record the results. There were multiple experiments conducted throughout the course of the research. These experiments were designed to apply the methodology and assess the execution speed of a small selection of batch schedulers. Due to time constraints, the research was limited to four schedulers. x The measurements that were taken during the experiments were wall time, RAM usage, and CPU usage. These measurements captured the utilization of system resources of each of the schedulers. The custom scripts were executed using, 1, 2, and 4 servers to determine how well a scheduler scales with network growth. The experiments were conducted on local school resources. All hardware was similar and was co-located within the same data-center. While the schedulers that were investigated as part of the experiments are agnostic to whether the system is grid, cluster, or super-computer; the investigation was limited to a cluster architecture