MPI is gaining acceptance as a standard for message-passing in high-performance computing, due to its powerful and flexible support of various communication styles. However, the complexity of its API poses significant software overhead, and as a result, applicability of MPI has been restricted to rather regular, coarse-grained computations. Our OMPI (Optimizing MPI) system removes much of the excess overhead by employing partial evaluation techniques, which exploit static information of MPI calls. Because partial evaluation alone is insufficient, we also utilize template functions for further optimization. To validate the effectiveness for our OMPI system, we performed baseline as well as more extensive benchmarks on a set of application cores with different communication characteristics, on the 64-node Fujitsu AP1000 MPP. Benchmarks show that OMPI improves execution efficiency by as much as factor of two for communication-intensive application core with minimal code increase. It also performssignificantly better than previous dynamic optimization technique.
由于MPI对各种通信方式的强大而灵活的支持,因此MPI已成为高性能计算中消息传递的标准。但是,其API的复杂性带来了可观的软件开销,因此,MPI的适用性已被限制在相当常规的粗粒度计算中。我们的OMPI(优化MPI)系统通过采用部分评估技术来消除了许多多余的开销,这些技术利用了MPI调用的静态信息。因为仅部分评估是不够的,所以我们还利用模板功能进行进一步优化。为了验证我们的OMPI系统的有效性,我们在64节点的Fujitsu AP1000 MPP上对一组具有不同通信特性的应用程序内核进行了基线以及更广泛的基准测试。基准测试表明,对于通信密集型应用程序核心,OMPI将执行效率提高了两倍,而代码增加却很少。它的性能也大大优于以前的动态优化技术。 P>
MPI, message passing, partial evaluation, SUIF, communication optimization, parallel computing;
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