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SCQPTH: an efficient differentiable splitting method for convex quadratic programming
We present SCQPTH: a differentiable first-order splitting method for convex
quadratic programs. The SCQPTH framework is based on the alternating direction
method of multipliers (ADMM) and the software implementation is motivated by
the state-of-the art solver OSQP: an operating splitting solver for convex
quadratic programs (QPs). The SCQPTH software is made available as an
open-source python package and contains many similar features including
efficient reuse of matrix factorizations, infeasibility detection, automatic
scaling and parameter selection. The forward pass algorithm performs operator
splitting in the dimension of the original problem space and is therefore
suitable for large scale QPs with decision variables and thousands
of constraints. Backpropagation is performed by implicit differentiation of the
ADMM fixed-point mapping. Experiments demonstrate that for large scale QPs,
SCQPTH can provide a improvement in computational
efficiency in comparison to existing differentiable QP solvers
High-throughput Binding Affinity Calculations at Extreme Scales
Resistance to chemotherapy and molecularly targeted therapies is a major
factor in limiting the effectiveness of cancer treatment. In many cases,
resistance can be linked to genetic changes in target proteins, either
pre-existing or evolutionarily selected during treatment. Key to overcoming
this challenge is an understanding of the molecular determinants of drug
binding. Using multi-stage pipelines of molecular simulations we can gain
insights into the binding free energy and the residence time of a ligand, which
can inform both stratified and personal treatment regimes and drug development.
To support the scalable, adaptive and automated calculation of the binding free
energy on high-performance computing resources, we introduce the High-
throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block
approach in order to attain both workflow flexibility and performance. We
demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage
binding affinity calculation pipelines. This permits a rapid time-to-solution
that is essentially invariant of the calculation protocol, size of candidate
ligands and number of ensemble simulations. As such, HTBAC advances the state
of the art of binding affinity calculations and protocols
ASCR/HEP Exascale Requirements Review Report
This draft report summarizes and details the findings, results, and
recommendations derived from the ASCR/HEP Exascale Requirements Review meeting
held in June, 2015. The main conclusions are as follows. 1) Larger, more
capable computing and data facilities are needed to support HEP science goals
in all three frontiers: Energy, Intensity, and Cosmic. The expected scale of
the demand at the 2025 timescale is at least two orders of magnitude -- and in
some cases greater -- than that available currently. 2) The growth rate of data
produced by simulations is overwhelming the current ability, of both facilities
and researchers, to store and analyze it. Additional resources and new
techniques for data analysis are urgently needed. 3) Data rates and volumes
from HEP experimental facilities are also straining the ability to store and
analyze large and complex data volumes. Appropriately configured
leadership-class facilities can play a transformational role in enabling
scientific discovery from these datasets. 4) A close integration of HPC
simulation and data analysis will aid greatly in interpreting results from HEP
experiments. Such an integration will minimize data movement and facilitate
interdependent workflows. 5) Long-range planning between HEP and ASCR will be
required to meet HEP's research needs. To best use ASCR HPC resources the
experimental HEP program needs a) an established long-term plan for access to
ASCR computational and data resources, b) an ability to map workflows onto HPC
resources, c) the ability for ASCR facilities to accommodate workflows run by
collaborations that can have thousands of individual members, d) to transition
codes to the next-generation HPC platforms that will be available at ASCR
facilities, e) to build up and train a workforce capable of developing and
using simulations and analysis to support HEP scientific research on
next-generation systems.Comment: 77 pages, 13 Figures; draft report, subject to further revisio
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