The 160K hours have been granted for this project. -- John Roberts Argonne National Laboratory CELS Systems [email protected] On 1/22/16, 9:41 AM, "[email protected] on behalf of Bair, Raymond A." <[email protected] on behalf of [email protected]> wrote:
Lets approve the request for 160K.
Ray
On 1/21/16, 3:45 PM, "Kim, Kibaek" <[email protected]> wrote:
Dear Ray:
Here is my estimate:
1. A research project that is currently using resources will further require 160000 core-hours in a month. 2. Another research project that will start running computations in February is expected to require 50000 core-hours.
So, I would need nearly 160000 core-hours more by the end of March.
Best, Kibaek
On Jan 21, 2016, at 9:34 AM, Bair, Raymond A. <[email protected]> wrote:
Dear Kibaek Kim,
We need your feedback to proceed.
Regards,
Ray
On 1/14/16, 4:21 PM, "[email protected] on behalf of Bair, Raymond A." <[email protected] on behalf of [email protected]> wrote:
Dear Kibaek,
Could you estimate how much more time you now and the end of March? In March the LCRC Allocations Committee meets to make decisions about the time allocations for Q3 and Q4 (April-Sept.). So the immediate decision is between now and March 31st, and the rest of your request can be added to your original request for Q3 and Q4.
Regards,
Ray
----------------------------------------- Ray Bair Argonne National Laboratory and the University of Chicago
On 1/14/16, 10:00 AM, "[email protected] on behalf of [email protected]" <[email protected] on behalf of [email protected]> wrote:
Hello,
A change in allocation has been requested:
Requester: kibaekkim (Kibaek Kim) Project: NEXTGENOPT Title: Next generation optimization Description: Sponsored by a Department of Energy (DOE) Early Career Award, we are currently developing new algorithms for UQ/SO that have a holistic view of the entire data-modeling-optimization process. A particular example of such algorithms is a new clustering-based interior-point strategy for stochastic programs in which scenarios are compressed based on their influence on the first-stage decision, not on data. This has shown to lead to drastic compression rates (and solution times) of above 80%, which cannot be achieved with data compression alone (see Figure 1). The compression is done adaptively, at the linear algebra level, by looking at the different contributions of the scenarios on the Schur complement.
We are also currently developing new interior-point algorithms for nonconvex structured optimization capable of dealing with complex multi-scale physics (typically involving large networks of partial differential equations) and stochastic components. These problems arise, for instance, from natural gas inventory (line-pack) management (see Figure 2). This requires of a redesign of existing algorithms and modeling environments capable of conveying problem structure down to the linear algebra kernels. In addition, hybrid linear algebra kernels need to be designed to exploit different structural facets. Problems with billions of variables are being targeted. Current: undetermined amount Justification:
Requested: 400000
A specific reason has been given: We are running a large number of optimization instances for a power grid application. The application analyzes the cost efficiency of the large power grid system in California with uncertain wind power production. As a result, a large scale stochastic optimization problem needs to be solved with different seasons and different parameter settings, which requires more core-hours to run such optimization problems.
This needs to be approved and the final allocation amount decided upon.
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