Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Factorized Machine Learning for Performance Modeling of Massively
  Parallel Heterogeneous Physical Simulations   We demonstrate neural-network runtime prediction for complex, many-parameter,
massively parallel, heterogeneous-physics simulations running on cloud-based
MPI clusters. Because individual simulations are so expensive, it is crucial to
train the network on a limited dataset despite the potentially large input
space of the physics at each point in the spatial domain. We achieve this using
a two-part strategy. First, we perform data-driven static load balancing using
regression coefficients extracted from small simulations, which both improves
parallel performance and reduces the dependency of the runtime on the precise
spatial layout of the heterogeneous physics. Second, we divide the execution
time of these load-balanced simulations into computation and communication,
factoring crude asymptotic scalings out of each term, and training neural nets
for the remaining factor coefficients. This strategy is implemented for Meep, a
popular and complex open-source electrodynamics simulation package, and are
validated for heterogeneous simulations drawn from published engineering
models.

14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin