7,102 research outputs found
Online and semi-online scheduling on two hierarchical machines with a common due date to maximize the total early work
In this study, we investigated several online and semi-online scheduling
problems on two hierarchical machines with a common due date to maximize the
total early work. For the pure online case, we designed an optimal online
algorithm with a competitive ratio of . For the case when the total
processing time is known, we proposed an optimal semi-online algorithm with a
competitive ratio of . Additionally, for the cases when the
largest processing time is known, we gave optimal algorithms with a competitive
ratio of if the largest job is a lower hierarchy one, and of
if the largest job is a higher hierarchy one, respectively
Exascale Deep Learning for Climate Analytics
We extract pixel-level masks of extreme weather patterns using variants of
Tiramisu and DeepLabv3+ neural networks. We describe improvements to the
software frameworks, input pipeline, and the network training algorithms
necessary to efficiently scale deep learning on the Piz Daint and Summit
systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained
throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up
to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel
efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor
Cores, a half-precision version of the DeepLabv3+ network achieves a peak and
sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.Comment: 12 pages, 5 tables, 4, figures, Super Computing Conference November
11-16, 2018, Dallas, TX, US
An Expressive Language and Efficient Execution System for Software Agents
Software agents can be used to automate many of the tedious, time-consuming
information processing tasks that humans currently have to complete manually.
However, to do so, agent plans must be capable of representing the myriad of
actions and control flows required to perform those tasks. In addition, since
these tasks can require integrating multiple sources of remote information ?
typically, a slow, I/O-bound process ? it is desirable to make execution as
efficient as possible. To address both of these needs, we present a flexible
software agent plan language and a highly parallel execution system that enable
the efficient execution of expressive agent plans. The plan language allows
complex tasks to be more easily expressed by providing a variety of operators
for flexibly processing the data as well as supporting subplans (for
modularity) and recursion (for indeterminate looping). The executor is based on
a streaming dataflow model of execution to maximize the amount of operator and
data parallelism possible at runtime. We have implemented both the language and
executor in a system called THESEUS. Our results from testing THESEUS show that
streaming dataflow execution can yield significant speedups over both
traditional serial (von Neumann) as well as non-streaming dataflow-style
execution that existing software and robot agent execution systems currently
support. In addition, we show how plans written in the language we present can
represent certain types of subtasks that cannot be accomplished using the
languages supported by network query engines. Finally, we demonstrate that the
increased expressivity of our plan language does not hamper performance;
specifically, we show how data can be integrated from multiple remote sources
just as efficiently using our architecture as is possible with a
state-of-the-art streaming-dataflow network query engine
OR and simulation in combination for optimization
This chapter aims to promote and illustrate the fruitful combination of classical Operations Research (OR) and Computer Simulation. First, a highly instructive example of parallel queues will be studied. This simple example already shows the necessary combination of OR (queueing) and simulation that appears to be of practical interest such as for call center optimization. Next, two more ’real life’ applications are regarded:\ud
- blood platelet production and inventory management at blood banks, and \ud
- train conflict resolution for railway junctions.\ud
Both applications show the useful combination of Simulation and optimization methods from OR, in particular Stochastic Dynamic Programming (SDP) and Markov decision theory (MDP), to obtain simple rules that are nearly optimal. The results are based on real life Dutch case studies and show that this combined OR-Simulation approach can be most useful for ’practical optimization’ and that it is still wide open for further application
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