Postdoc: A Hierarchical Methodology for SMP Clusters

0203584Moret，Bernard新墨西哥大学CISE实验计算机科学博士后研究员：SMP并行计算器的分层方法并行计算中隐含的高性能计算的承诺迄今为止已经在有限的应用范围内实现，但是大型分布式内存系统中的并行性受到消息传递成本的阻碍。 对称多处理器（SMP）集群将联合收割机分布式内存与真正的共享内存访问相结合，并提供了有效解决复杂、不规则优化问题的潜力，同时降低了消息传递成本。 建议的研究是扩展工作的高性能SMP计算，使用计算生物学，开发一个层次的方法集群的SMP。 博士后研究助理将开发和测试的方法，适合未来的多级并行架构，使用的问题和算法，从高性能计算生物学的研究作为测试案例。

0203584Moret, BernardUniversity of New MexicoCISE Postdoctoral Associates in Experimental Computer Science: A Hierarchical Methodology for SMP ClustersThe promise of high-performance computing implicit in parallel computation has so far been realized in a limited range of applications, but parallelism in large distributed-memory systems is hampered by the cost of message passing. Symmetric multiprocessor (SMP) clusters combine distributed memory with true shared memory access and offer the potential for effectively solving complex, irregular optimization problems with reduced message passing costs. The proposed research is to extend work on high-performance SMP computation, using computational biology, to develop a hierarchical methodology for clusters of SMPs. The postdoctoral research associate will develop and test the methodology, suited to future multilevel parallel architectures, using problems and algorithms from research in high-performance computational biology as test cases.