Browsing by Author "Sudarsan, Rajesh"
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- Priority-enabled Scheduling for Resizable Parallel ApplicationsSudarsan, Rajesh; Ribbens, Calvin J.; Varadarajan, Srinidhi (Department of Computer Science, Virginia Polytechnic Institute & State University, 2009)In this paper, we illustrate the impact of dynamic resizability on parallel scheduling. Our ReSHAPE framework includes an application scheduler that supports dynamic resizing of parallel applications. We propose and evaluate new scheduling policies made possible by our ReSHAPE framework. The framework also provides a platform to experiment with more interesting and sophisticated scheduling policies and scenarios for resizable parallel applications. The proposed policies support scheduling of parallel applications with and without user assigned priorities. Experimental results show that these scheduling policies significantly improve individual application turn around time as well as overall cluster utilization.
- ReSHAPE: A Framework for Dynamic Resizing and Scheduling of Homogeneous Applications in a Parallel EnvironmentRibbens, Calvin J.; Sudarsan, Rajesh (Department of Computer Science, Virginia Polytechnic Institute & State University, 2007)Applications in science and engineering often require huge computational resources for solving problems within a reasonable time frame. Parallel supercomputers provide the computational infrastructure for solving such problems. A traditional application scheduler running on a parallel cluster only supports static scheduling where the number of processors allocated to an application remains fixed throughout the lifetime of execution of the job. Due to the unpredictability in job arrival times and varying resource requirements, static scheduling can result in idle system resources thereby decreasing the overall system throughput. In this paper we present a prototype framework called ReSHAPE, which supports dynamic resizing of parallel MPI applications executed on distributed memory platforms. The framework includes a scheduler that supports resizing of applications, an API to enable applications to interact with the scheduler, and a library that makes resizing viable. Applications executed using the ReSHAPE scheduler framework can expand to take advantage of additional free processors or can shrink to accommodate a high priority application, without getting suspended. In our research, we have mainly focused on structured applications that have two-dimensional data arrays distributed across a two-dimensional processor grid. The resize library includes algorithms for processor selection and processor mapping. Experimental results show that the ReSHAPE framework can improve individual job turn-around time and overall system throughput.
- ReSHAPE: A Framework for Dynamic Resizing of Parallel ApplicationsSudarsan, Rajesh (Virginia Tech, 2009-09-25)As terascale supercomputers become more common, and as the high-performance computing community turns its attention to petascale machines, the challenge of providing effective resource management for high-end machines grows in both importance and difficulty. These computing resources are by definition expensive, so the cost of underutilization is also high, e.g., wasting 5% of the compute nodes on a 10,000 node cluster is a much more serious problem than on a 100 node cluster. Moreover, the high energy and cooling costs incurred in maintaining these high end machines (often millions of dollars per year) can be justified only when these machines are used to their full capacity. On large clusters, conventional jobs schedulers are hard-pressed to achieve over 90% utilization with typical job-mixes. A fundamental problem is that most conventional parallel job schedulers only support static scheduling, so that the number of processors allocated to an application cannot be changed at runtime. As a result, it is common to see jobs stuck in the queue because they require just a few more processors than are currently available, resulting in long queue wait times for applications and low overall system utilization. A more flexible and effective approach is to support dynamic resource management and scheduling, where the number of processors allocated to jobs can be expanded or contracted at runtime. This is the focus of this dissertation --- dynamic resizing of parallel applications. Dynamic resizing significantly improves individual application turn-around time and helps the scheduler to achieve higher machine utilization and job throughput. This dissertation focuses on the potential benefits and challenges of dynamic resizing using ReSHAPE, a new framework for dynamic Resizing and Scheduling of Homogeneous Applications in a Parallel Environment. It also details several interesting and effective scheduling policies implemented in ReSHAPE and demonstrates their effectiveness to improve overall cluster utilization and individual application turn-around time.