Cluster Queue Structure for Shared-Memory Multiprocessor Systems
Three basic structures have been proposed to organize the task queues for shared-memory multiprocessor systems: centralized, distributed, and hierarchical. Centralized structures are not suitable for massively parallel systems since the shared queue becomes a bottleneck for frequent enqueuing and dequeuing operations. Distributed structures have load imbalancing problem because of no support for workload sharing between queues. Hierarchical structures intend to combine the advantage of the previous two structures and eliminate their disadvantages. Unfortunately, we find load imbalancing still exists in the hierarchical structure, and has significant impact on system performance, particularly when the workload is heavy and irregular. After identifying the cause of this problem, we propose the use of a clustered structure in place of the hierarchical one. Analyzes and simulations show the proposed structure can provide better load balancing and less contention than the hierarchical one.

title: A bottom-up inference of loss rate
abstract: Loss tomography, as a key component of network tomography, aims to obtain the loss rate of each link in a network by end-to-end measurements. If knowing the loss model of a link, we, in fact, deal with a parametric estimate problem with incomplete data. Maximum likelihood estimates are often used in this situation to identify the unknown parameters in the loss model. Almost all methods proposed so far rely on the iterative approximation to identify the parameters that requires a long execution time. In addition, the parameters identified by those methods may not be the true values of those parameters since the iterative procedure may trap into a local maximum. In this paper, we propose an estimate that is based on the correlation between a link and its sibling brothers to identify the loss rate of the link. The proposed method, instead of using an iterative approach to approximate the maximum, employs a bottom-up approach to identify the loss rates of the links of a network. Comparing to the previous methods, the proposed method is simple and fast because it is an analytical solution.

title: Implementing parallel hmm-pfam on the EARTH multithreaded architecture
abstract: Hmmpfam is a widely used computation-intensive bioinformatics software for sequence classification. This poster describes a new parallel implementation of hmmpfam on EARTH, which is an event-driven fine-grain multithreaded programming execution model. The comparison results of the original PVM implementation and our implementation shows notable improvements on absolute speedup and scalability. On a cluster of 128 dual-CPU nodes, the execution time of a representative testbench is reduced from 15.9 hours to 4.3 minutes.

title: Performance Portability on EARTH: A Case Study across Several Parallel Architectures
abstract: With the rapidly increasing diversity of parallel architectures and the increasing time and labor for developing parallel applications, the performance portability of parallel programs is becoming increasingly important and should be considered when designing parallel execution models, APIs, and runtime system software. This paper analyzes both code portability and performance portability of parallel programs based on the EARTH model $an event-driven fine-grain multi-threaded execution and architecture model. We discuss several design considerations of the EARTH system that contribute to the performance portability of parallel applications. Experiments of four representative benchmarks are conducted on several different parallel architectures, including two clusters listed in the 23rd supercomputer TOP500 list. The results demonstrate that EARTH based programs can achieve robust performance portability across the selected hardware platforms without any code modification or tuning.