4,691 research outputs found
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The demands of improving energy efficiency for high performance scientific applications arise crucially nowadays. Software-controlled hardware solutions directed by Dynamic Voltage and Frequency Scaling (DVFS) have shown their effectiveness extensively. Although DVFS is beneficial to green computing, introducing DVFS itself can incur non-negligible overhead, if there exist a large number of frequency switches issued by DVFS. In this paper, we propose a strategy to achieve the optimal energy savings for distributed matrix multiplication via algorithmically trading more computation and communication at a time adaptively with user-specified memory costs for less DVFS switches, which saves 7.5% more energy on average than a classic strategy. Moreover, we leverage a high performance communication scheme for fully exploiting network bandwidth via pipeline broadcast. Overall, the integrated approach achieves substantial energy savings (up to 51.4%) and performance gain (28.6% on average) compared to ScaLAPACK pdgemm() on a cluster with an Ethernet switch, and outperforms ScaLAPACK and DPLASMA pdgemm() respectively by 33.3% and 32.7% on average on a cluster with an Infiniband switch
Reinforcement learning based multi core scheduling (RLBMCS) for real time systems
Embedded systems with multi core processors are increasingly popular because of the diversity of applications that can be run on it. In this work, a reinforcement learning based scheduling method is proposed to handle the real time tasks in multi core systems with effective CPU usage and lower response time. The priority of the tasks is varied dynamically to ensure fairness with reinforcement learning based priority assignment and Multi Core MultiLevel Feedback queue (MCMLFQ) to manage the task execution in multi core system
DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS
Grid, an infrastructure for resource sharing, currently has shown its importance in
many scientific applications requiring tremendously high computational power. Grid
computing enables sharing, selection and aggregation of resources for solving
complex and large-scale scientific problems. Grids computing, whose resources are
distributed, heterogeneous and dynamic in nature, introduces a number of fascinating
issues in resource management. Grid scheduling is the key issue in grid environment
in which its system must meet the functional requirements of heterogeneous domains,
which are sometimes conflicting in nature also, like user, application, and network.
Moreover, the system must satisfy non-functional requirements like reliability,
efficiency, performance, effective resource utilization, and scalability. Thus, overall
aim of this research is to introduce new grid scheduling algorithms for resource
allocation as well as for job scheduling for enabling a highly efficient and effective
utilization of the resources in executing various applications.
The four prime aspects of this work are: firstly, a model of the grid scheduling
problem for dynamic grid computing environment; secondly, development of a new
web based simulator (SyedWSim), enabling the grid users to conduct a statistical
analysis of grid workload traces and provides a realistic basis for experimentation in
resource allocation and job scheduling algorithms on a grid; thirdly, proposal of a new
grid resource allocation method of optimal computational cost using synthetic and
real workload traces with respect to other allocation methods; and finally, proposal of
some new job scheduling algorithms of optimal performance considering parameters
like waiting time, turnaround time, response time, bounded slowdown, completion
time and stretch time. The issue is not only to develop new algorithms, but also to
evaluate them on an experimental computational grid, using synthetic and real
workload traces, along with the other existing job scheduling algorithms.
Experimental evaluation confirmed that the proposed grid scheduling algorithms
possess a high degree of optimality in performance, efficiency and scalability
Space Station Freedom data management system growth and evolution report
The Information Sciences Division at the NASA Ames Research Center has completed a 6-month study of portions of the Space Station Freedom Data Management System (DMS). This study looked at the present capabilities and future growth potential of the DMS, and the results are documented in this report. Issues have been raised that were discussed with the appropriate Johnson Space Center (JSC) management and Work Package-2 contractor organizations. Areas requiring additional study have been identified and suggestions for long-term upgrades have been proposed. This activity has allowed the Ames personnel to develop a rapport with the JSC civil service and contractor teams that does permit an independent check and balance technique for the DMS
Improving the scalability of parallel N-body applications with an event driven constraint based execution model
The scalability and efficiency of graph applications are significantly
constrained by conventional systems and their supporting programming models.
Technology trends like multicore, manycore, and heterogeneous system
architectures are introducing further challenges and possibilities for emerging
application domains such as graph applications. This paper explores the space
of effective parallel execution of ephemeral graphs that are dynamically
generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The
workloads are expressed using the semantics of an Exascale computing execution
model called ParalleX. For comparison, results using conventional execution
model semantics are also presented. We find improved load balancing during
runtime and automatic parallelism discovery improving efficiency using the
advanced semantics for Exascale computing.Comment: 11 figure
Chapter Networking Applications for Embedded Systems
Embedded system
Energy challenges for ICT
The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT
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