February 8, 2018
Datamove workshop
N/A
February 8, 2018
MMonday | TTuesday | WWednesday | TThursday | FFriday | SSaturday | SSunday |
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29January 29, 2018
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30January 30, 2018
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31January 31, 2018
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February1February 1, 2018●(1 event)Keynote – |
2February 2, 2018
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3February 3, 2018
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4February 4, 2018
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5February 5, 2018
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6February 6, 2018
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7February 7, 2018
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Datamove workshop N/A |
9February 9, 2018
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10February 10, 2018
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11February 11, 2018
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12February 12, 2018
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13February 13, 2018
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14February 14, 2018
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Untitled Event N/A Parallel Sequence Alignment of Whole Chromosomes with Hundreds of GPUs and Pruning (by Alba Cristina Magalhaes Alves de Melo, University of Brasilia ) – Biological Sequence Alignment is a very basic operation in Short Bio: Alba Cristina Magalhaes Alves de Melo obtained her PhD degree |
16February 16, 2018
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17February 17, 2018
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18February 18, 2018
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19February 19, 2018
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20February 20, 2018
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21February 21, 2018
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Seminar recess (vacation) N/A |
23February 23, 2018
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24February 24, 2018
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25February 25, 2018
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26February 26, 2018
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27February 27, 2018
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28February 28, 2018
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March1March 1, 2018●(1 event)Keynote N/A |
2March 2, 2018
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3March 3, 2018
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4March 4, 2018
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5March 5, 2018
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6March 6, 2018
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7March 7, 2018
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Randomized Load Balancing: Asymptotic Optimality of Power-of-d-Choices with Memory by Jonatha Anselmi (Inria Bordeaux) – In multi-server distributed queueing systems, the access of stochastically arriving jobs to resources is often regulated by a dispatcher. A fundamental problem consists in designing a load balancing algorithm that minimizes the delays experienced by jobs. During the last two decades, the power-of-d-choice algorithm, based on the idea of dispatching each job to the least loaded server out of $d$ servers randomly sampled at the arrival of the job itself, has emerged as a breakthrough in the foundations of this area due to its versatility and appealing asymptotic properties. We consider the power-of-d-choice algorithm with the addition of a local memory that keeps track of the latest observations collected over time on the sampled servers. Then, each job is sent to a server with the lowest observation. We show that this algorithm is asymptotically optimal in the sense that the load balancer can always assign each job to an idle server in the large-server limit. This holds true if and only if the system load $\lambda$ is less than $1-\frac{1}{d}$. If this condition is not satisfied, we show that queue lengths are bounded by $j^\star+1$, where $j^\star\in\mathbb{N}$ is given by the solution of a polynomial equation. This is in contrast with the classic version of the power-of-d-choice algorithm, where queue lengths are unbounded. Our upper bound on the size of the most loaded server, $j^*+1$, is tight and increases slowly when $\lambda$ approaches its critical value from below. For instance, when $\lambda= 0.995$ and $d=2$ (respectively, $d=3$), we find that no server will contain more than just $5$ ($3$) jobs in equilibrium. Our results quantify and highlight the importance of using memory as a means to enhance performance in randomized load balancing. |
9March 9, 2018
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10March 10, 2018
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11March 11, 2018
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12March 12, 2018
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13March 13, 2018
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14March 14, 2018
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Obtaining Dynamic Scheduling Policies with Simulation and Machine Learning (by Danilo Santos, Datamove) – Obtaining Dynamic Scheduling Policies with Simulation and Machine Learning Abstract: Dynamic scheduling of tasks in large-scale HPC platforms is normally accomplished using ad-hoc heuristics, based on task characteristics, combined with some backfilling strategy. Defining heuristics that work efficiently in different scenarios is a difficult task, specially when considering the large variety of task types and platform architectures. In this work, we present a methodology based on simulation and machine learning to obtain dynamic scheduling policies. Using simulations and a workload generation model, we can determine the characteristics of tasks that lead to a reduction in the mean slowdown of tasks in an execution queue. Modeling these characteristics using a nonlinear function and applying this function to select the next task to execute in a queue improved the mean task slowdown in synthetic workloads. When applied to real workload traces from highly different machines, these functions still resulted in performance improvements, attesting the generalization capability of the obtained heuristics. |
16March 16, 2018
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17March 17, 2018
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18March 18, 2018
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19March 19, 2018
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20March 20, 2018
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21March 21, 2018
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A Class of Stochastic Multilayer Networks: Percolation, Exact and Asymptotic Results by Philippe Nain (inria, Lyon) – Abstract: Bâtiment IMAG (406) Saint-Martin-d'Hères, 38400 France |
23March 23, 2018
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24March 24, 2018
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25March 25, 2018
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26March 26, 2018
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27March 27, 2018
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Parallel Space-Time Kernel Density Estimation By Erik Saule (U. Caroline du Nord) – The exponential growth of available data has increased the need for We focus in this paper on events that are localized among three Bâtiment IMAG (406) Saint-Martin-d'Hères, 38400 France |
Polyhedral Optimization at Runtime, by Manuel Selva. – The polyhedral model has proven to be very useful to optimize and parallelize a particular class of compute intensive application kernels. A polyhedral optimizer needs to have affine functions defining loop bounds, memory accesses and branching conditions. Unfortunately, this information is not always available at compile time. To broaden the scope of polyhedral optimization opportunities, runtime information can be considered. This talk will highlight the challenges of integrating polyhedral optimization in runtime systems: - When and how to detect opportunities for polyhedral optimization? These challenges will be illustrated in the context of both the APOLLO framework targeting C and C++ applications and of the JavaScript engine from Apple. Bâtiment IMAG (406) Saint-Martin-d'Hères, 38400 France |
30March 30, 2018
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31March 31, 2018
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April1April 1, 2018 |
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