Decentralized Online Scheduling of Malleable NP-hard Jobs

In this work, we address an online job scheduling problem in a large distributed computing environment. Each job has a priority and a demand of resources, takes an unknown amount of time, and is malleable, i.e., the number of allotted workers can fluctuate during its execution. We subdivide the problem into (a) determining a fair amount of resources for each job and (b) assigning each job to an according number of processing elements. Our approach is fully decentralized, uses lightweight communication, and arranges each job as a binary tree of workers which can grow and shrink as necessary. Using the NP-complete problem of propositional satisfiability (SAT) as a case study, we experimentally show on up to 128 machines (6144 cores) that our approach leads to near-optimal utilization, imposes minimal computational overhead, and performs fair scheduling of incoming jobs within a few milliseconds

Efficient Scalable Computing through Flexible Applications and Adaptive Workloads

In this paper we introduce a methodology for dynamic job reconfiguration driven by the programming model runtime in collaboration with the global resource manager. We improve the system throughput by exploiting malleability techniques (in terms of number of MPI ranks) through the reallocation of resources assigned to a job during its execution. In our proposal, the OmpSs runtime reconfigures the number of MPI ranks during the execution of an application in cooperation with the Slurm workload manager. In addition, we take advantage of OmpSs offload semantics to allow application developers deal with data redistribution. By combining these elements a job is able to expand itself in order to exploit idle nodes or be shrunk if other queued jobs could be initiated. This novel approach adapts the system workload in order to increase the throughput as well as make a smarter use of the underlying resources. Our experiments demonstrate that this approach can reduce the total execution time of a practical workload by more than 40% while reducing the amount of resources by 30%.This work is supported by the Project TIN2014-53495-R and TIN2015-65316-P from MINECO and FEDER. Antonio J. Peña is cofinanced by MINECO under Juan de la Cierva fellowship number IJCI-2015-23266. Special thanks to José I. Aliaga for the conjugate gradient code.Peer ReviewedPostprint (author's final draft

An study of the effect of process malleability in the energy efficiency on GPU‑based clusters

The adoption of graphic processor units (GPU) in high-performance computing (HPC) infrastructures determines, in many cases, the energy consumption of those facilities. For this reason, an efficient management and administration of the GPU-enabled clusters is crucial for the optimum operation of the cluster. The main aim of this work is to study and design efficient mechanisms of job scheduling across GPU-enabled clusters by leveraging process malleability techniques, able to reconfigure running jobs, depending on the cluster status. This paper presents a model that improves the energy efficiency when processing a batch of jobs in an HPC cluster. The model is validated through the MPDATA algorithm, as a representative example of stencil computation used in numerical weather prediction. The proposed solution applies the efficiency metrics obtained in a new reconfiguration policy aimed at job arrays. This solution allows the reduction in the processing time of workloads up to 4.8 times and reduction in the energy consumption up to 2.4 times the cluster compared to the traditional job management, where jobs are not reconfigured during their execution

DMR API: Improving cluster productivity by turning applications into malleable

[EN] Adaptive workloads can change on-the-fly the configuration of their jobs, in terms of number of processes. To carry out these job reconfigurations, we have designed a methodology which enables a job to communicate with the resource manager and, through the runtime. to change its number of MPI ranks. The collaboration between both the workload manager-aware of the queue of jobs and the resources allocation-and the parallel runtime-able to transparently handle the processes and the program data-is crucial for our throughput-aware malleability methodology. Hence, when a job triggers a reconfiguration, the resource manager will check the cluster status and return the appropriate action: i) expand, if there are spare resources; ii) shrink, if queued jobs can be initiated; or iii) none, if no change can improve the global productivity. In this paper, we describe the internals of our framework and demonstrate how it reduces the global workload completion time along with providing a more efficient usage of the underlying resources. For this purpose, we present a thorough study of the adaptive workloads processing by showing the detailed behavior of our framework in representative experiments. (C) 2018 Elsevier B.V. All rights reserved.Iserte Agut, S.; Mayo Gual, R.; Quintana Ortí, ES.; Beltrán, V.; Peña Monferrer, AJ. (2018). DMR API: Improving cluster productivity by turning applications into malleable. Parallel Computing. 78:54-66. https://doi.org/10.1016/j.parco.2018.07.006S54667

The new blueprint : Moving towards university knowledge city

All knowledge is a form of tradition, whether ingrained in its structure, content, or the value that people attribute to it. Civilized societies have great traditions of knowledge, and the post modern era which discovered print and digitalization gives leverage to the value of those traditions not only in transforming them into intellectual assets but also the means by which they can systematically improve the way society live, work and continue to change in creative and dynamic ways. Cities-their infrastructure, economies and culture are all being transformed by knowledge. The university is a micro knowledge city, and it needs to develop a larger blue print to sustain its growth as a knowledge city. The university must be aware of the importance to capture knowledge assets within the micro-culture and practice of its academic, professional, pastoral, social and cultural services; the sharing of those knowledge assets; and the transformation of those knowledge assets to leverage life experiences of citizens in the campus knowledge-city, and beyond

Combining malleability and I/O control mechanisms to enhance the execution of multiple applications

This work presents a common framework that integrates CLARISSE, a cross-layer runtime for the I/O software stack, and FlexMPI, a runtime that provides dynamic load balancing and malleability capabilities for MPI applications. This integration is performed both at application level, as libraries executed within the application, as well as at central-controller level, as external components that manage the execution of different applications. We show that a cooperation between both runtimes provides important benefits for overall system performance: first, by means of monitoring, the CPU, communication and I/O performances of all executing applications are collected, providing a holistic view of the complete platform utilization. Secondly, we introduce a coordinated way of using CLARISSE and FlexMPI control mechanisms, based on two different optimization strategies, with the aim of improving both the application I/O and overall system performance. Finally, we present a detailed description of this proposal, as well as an empirical evaluation of the framework on a cluster showing significant performance improvements at both application and wide-platform levels. We demonstrate that with this proposal the overall I/O time of an application can be reduced by up to 49% and the aggregated FLOPS of all running applications can be increased by 10% with respect to the baseline case. (C) 2018 Elsevier Inc. All rights reserved.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by the Spanish “Ministerio de Economia y Competitividad” under the project grant TIN2016-79637-P “Towards Unification of HPC and Big Data paradigms” and EU under the COST Program Action IC1305, Network for Sustainable Ultrascale Computing (NESUS)

Performance-aware scheduling of parallel applications on non-dedicated clusters

This work presents a HPC framework that provides new strategies for resource management and job scheduling, based on executing different applications in shared compute nodes, maximizing platform utilization. The framework includes a scalable monitoring tool that is able to analyze the platform's compute node utilization. We also introduce an extension of CLARISSE, a middleware for data-staging coordination and control on large-scale HPC platforms that uses the information provided by the monitor in combination with application-level analysis to detect performance degradation in the running applications. This degradation, caused by the fact that the applications share the compute nodes and may compete for their resources, is avoided by means of dynamic application migration. A description of the architecture, as well as a practical evaluation of the proposal, shows significant performance improvements up to 20% in the makespan and 10% in energy consumption compared to a non-optimized execution.This work was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness under the grant TIN2016-79637-P "Towards Unification of HPC and Big Data Paradigms"; and the European Union's Horizon 2020 research and innovation program under Grant No. 801091, project "Exascale programming models for extreme data processing" (ASPIDE)

Addressing the underrepresentation of women in mathematics conferences

Despite significant improvements over the last few generations, the discipline of mathematics still counts a disproportionately small number of women among its practitioners. These women are underrepresented as conference speakers, even more so than the underrepresentation of women among PhD-earners as a whole. This underrepresentation is the result of implicit biases present within all of us, which cause us (on average) to perceive and treat women and men differently and unfairly. These mutually reinforcing biases begin in primary school, remain active through university study, and continue to oppose women's careers through their effects on hiring, evaluation, awarding of prizes, and inclusion in journal editorial boards and conference organization committees. Underrepresentation of women as conference speakers is a symptom of these biases, but it also serves to perpetuate them; therefore, addressing the inequity at conferences is valuable and necessary for countering this underrepresentation. We describe in detail the biases against women in mathematics, knowing that greater awareness of them leads to a better ability to mitigate them. Finally, we make explicit suggestions for organizing conferences in ways that are equitable for female mathematicians.Comment: 26 pages. See also "An annotated bibliography of work related to gender in science" (arXiv:1412.4104

Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks

The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks

Modeling of an Adaptive Parallel System with Malleable Applications in a Distributed Computing Environment

Adaptive parallel applications that can change resources during execution, promise increased application performance and better system utilization. Furthermore, they open the opportunity for developing a new class of parallel applications driven by unpredictable data and events. The research issues in an adaptive parallel system are complex and interrelated. The nature and complexities of the relationships among these issues are not well researched and understood. Before developing adaptive applications or an infrastructure support for adaptive applications, these issues need to be investigated and studied in detail. One way of understanding and investigating these issues is by modeling and simulation. A model for adaptive parallel systems has been developed to enable the investigation of the impact of malleable workloads on its performance. The model can be used to determine how different model parameters impact the performance of the system and to determine the relationships among them Subsequently, a discrete event simulator has been developed to numerically simulate the model. Using the simulator, the impact of the variation in the number of malleable jobs in the workload, the flexibility, the negotiation cost, and the adaptation cost on system performance have been studied. The results and conclusions of these simulation experiments are presented in this dissertation. In general, the simulation results reveal that the performance improves with an increase in the number of malleable jobs in a workload, and that the performance saturates at a certain percentage of rigid to malleable jobs mix. A high percentage of malleable jobs is not necessary to achieve significant improvement in performance. The performance in general improves as the flexibility increases up to a certain point; then, it saturates. The negotiation cost impacts the performance, but not significantly. The number of negotiations for a given workload increases as number of malleable jobs increases up to a certain point, and then it decreases as number of malleable jobs increases further. The performance degrades as the application adaptation cost increases. The impact of the application adaptation cost on performance is much more significant compared to that of the negotiation cost