An Analysis for Evaluating the Cost/Profit Effectiveness of Parallel Systems

A new domain of commercial applications demands the development of inexpensive parallel computing platforms to lower the cost of operations and increase the business profit. The calculation of returns on an IT investment is now important to justify the decision of upgrading or replacing parallel systems. This thesis presents a framework of the performance and economic factors that are considered when evaluating a parallel system. We introduce a metric called the cost/profit effective metric, which measures the effectiveness of a parallel system in terms of performance, cost and profit. This metric describes the profit obtained from the performance of three different domains for scaling: speed-up, throughput and/or scale-up. Cost is measured by the actual costs of a parallel system. We present two cases of study to demonstrate the application of this metric and analyze the results to support the evaluation of the parallel system on each case

Self-Adaptive Scheduler Parameterization

High-end parallel systems present a tremendous research challenge on how to best allocate their resources to match dynamic workload characteristics and user habits that are often unique to each system. Although thoroughly investigated, job scheduling for production systems remains an inexact science, requiring significant experience and intuition from system administrators to properly configure batch schedulers. State-of-the-art schedulers provide many parameters for their configuration, but tuning these to optimize performance and to appropriately respond to the continuously varying characteristics of the workloads can be very difficult — the effects of different parameters and their interactions are often unintuitive. In this paper, we introduce a new and general methodology for automating the difficult process of job scheduler parameterization. Our proposed methodology is based on online simulations of a model of the actual system to provide on-the-fly suggestions to the scheduler for automated parameter adjustment. Detailed performance comparisons via simulation using actual supercomputing traces from the Parallel Workloads Archive indicate that this self-adaptive parameterization via online simulation consistently outperforms other workload-aware methods for scheduler parameterization. This methodology is unique, flexible, and practical in that it requires no a priori knowledge of the workload, it works well even in the presence of poor user runtime estimates, and it can be used to address any system statistic of interest

Research Projects, Technical Reports and Publications

The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on June 6, 1983. RIACS is privately operated by USRA, a consortium of universities with research programs in the aerospace sciences, under contract with NASA. The primary mission of RIACS is to provide research and expertise in computer science and scientific computing to support the scientific missions of NASA ARC. The research carried out at RIACS must change its emphasis from year to year in response to NASA ARC's changing needs and technological opportunities. A flexible scientific staff is provided through a university faculty visitor program, a post doctoral program, and a student visitor program. Not only does this provide appropriate expertise but it also introduces scientists outside of NASA to NASA problems. A small group of core RIACS staff provides continuity and interacts with an ARC technical monitor and scientific advisory group to determine the RIACS mission. RIACS activities are reviewed and monitored by a USRA advisory council and ARC technical monitor. Research at RIACS is currently being done in the following areas: Advanced Methods for Scientific Computing High Performance Networks During this report pefiod Professor Antony Jameson of Princeton University, Professor Wei-Pai Tang of the University of Waterloo, Professor Marsha Berger of New York University, Professor Tony Chan of UCLA, Associate Professor David Zingg of University of Toronto, Canada and Assistant Professor Andrew Sohn of New Jersey Institute of Technology have been visiting RIACS. January 1, 1996 through September 30, 1996 RIACS had three staff scientists, four visiting scientists, one post-doctoral scientist, three consultants, two research associates and one research assistant. RIACS held a joint workshop with Code 1 29-30 July 1996. The workshop was held to discuss needs and opportunities in basic research in computer science in and for NASA applications. There were 14 talks given by NASA, industry and university scientists and three open discussion sessions. There were approximately fifty participants. A proceedings is being prepared. It is planned to have similar workshops on an annual basis. RIACS technical reports are usually preprints of manuscripts that have been submitted to research 'ournals or conference proceedings. A list of these reports for the period January i 1, 1996 through September 30, 1996 is in the Reports and Abstracts section of this report

Simulation techniques in an artificial society model

Artificial society refers to a generic class of agent-based simulation models used to discover global social structures and collective behavior produced by simple local rules and interaction mechanisms. Artificial society models are applicable in a variety of disciplines, including the modeling of chemical and biological processes, natural phenomena, and complex adaptive systems. We focus on the underlying simulation techniques used in artificial society discrete-event simulation models, including model time evolution and computational performance.;Although for some applications synchronous time evolution is the correct modeling approach, many other applications are better represented using asynchronous time evolution. We claim that asynchronous time evolution can eliminate potential simulation artifacts produced using synchronous time evolution. Using an adaptation of a popular artificial society model, we show that very different output can result based solely on the choice of asynchronous or synchronous time evolution. Based on the event list implementation chosen, the use of discrete-event simulation to incorporate asynchronous time evolution can incur a substantial loss in computational performance. Accordingly, we evaluate select event list implementations within the artificial society simulation model and demonstrate that acceptable performance can be achieved.;In addition to the artificial society model, we show that transforming from a synchronous to an asynchronous system proves beneficial for scheduling resources in a parallel system. We focus on non-FCFS job scheduling policies that permit jobs to backfill, i.e., to move ahead in the queue, given that they do not delay certain previously submitted jobs. Instead of using a single queue of jobs, we propose a simple yet effective backfilling scheduling policy that effectively separates short from long jobs by incorporating multiple queues. By monitoring system performance, our policy adapts its configuration parameters in response to severe changes in the job arrival pattern and/or resource demands. Detailed performance comparisons via simulation using actual parallel workload traces indicate that our proposed policy consistently outperforms traditional backfilling in a variety of contexts

Improved utilization and responsiveness with gang scheduling

Most commercial multicomputers use space-slicing schemes in which each scheduling decision has an unknown impact on the future: should a job be scheduled, risking that it will block other larger jobs later, or should the processors be left idle for now in anticipation of future arrivals? This dilemma is solved by using gang scheduling, because then the impact of each decision is limited to its time slice, and future arrivals can be accommodated in other time slices. This added flexibility is shown to improve overall system utilization and responsiveness. Empirical evidence from using gang scheduling on a Cray T3D installed at Lawrence Livermore National Lab corroborates these results, and shows conclusively that gang scheduling can be very effective with current technology. 29 refs., 10 figs., 6 tabs

The Resource Usage Aware Backfilling

Abstract. Job scheduling policies for HPC centers have been extensively stud-ied in the last few years, especially backfilling based policies. Almost all of these studies have been done using simulation tools. All the existent simulators use the runtime (either estimated or real) provided in the workload as a basis of their sim-ulations. In our previous work we analyzed the impact on system performance of considering the resource sharing (memory bandwidth) of running jobs including a new resource model in the Alvio simulator. Based on this studies we proposed the LessConsume and LessConsume Threshold resource selection policies. Both are oriented to reduce the saturation of the shared resources thus increasing the performance of the system. The results showed how both resource allocation poli-cies shown how the performance of the system can be improved by considering where the jobs are finally allocated. Using the LessConsume Threshold Resource Selection Policy, we propose a new backfilling strategy: the Resource Usage Aware Backfilling job scheduling policy. This is a backfilling based scheduling policy where the algorithms which decide which job has to be executed and how jobs have to be backfilled are based on a different Threshold configurations. This backfilling variant that considers how the shared resources are used by the scheduled jobs. Rather than backfilling the first job that can moved to the run queue based on the job arrival time or job size, it looks ahead to the next queued jobs, and tries to allocate jobs that would experience lower penalized runtime caused by the resource sharing saturation. In the paper we demostrate how the exchange of scheduling information between the local resource manager and the scheduler can improve substantially the per-formance of the system when the resource sharing is considered. We show how it can achieve a close response time performance that the shorest job first Back-filling with First Fit (oriented to improve the start time for the allocated jobs) providing a qualitative improvement in the number of killed jobs and in the per-centage of penalized runtime.