Multi-phased Task Placement of HPC Applications in the Cloud

Many high-performance computing applications present different phases during their execution. Nevertheless, thread and process placement techniques usually provide static-only methods to improve the data and thread locality. Similarly, cloud computing datacenters may present variations in terms of latency over the execution time of applications. To overcome these two problems, in this paper we analyze scientific applications that have different communication patterns along with its execution. For such applications, we evaluate the performance variation of traditional static placement techniques to our new approach that uses code annotations to perform the new placement of tasks, matching also the variations on network performance of Virtual Machines (VMs) during the run time. For our experiments, we use applications from the NAS parallel benchmark suite, running them on two VM sizes with 32 and 64 cores respectively, from the same family of instance types at the West US datacenter from Azure. Results show that compared to traditional static process mapping, our multi-phased placement mechanism achieves average performance gains of 13.57% up to 28.32% on the evaluated scenarios. These results show that there is an opportunity to improve performance by correctly identifying the network variations and reacting by generating a new task-to-instance mapping.