##
** Bay Area Scientific Computing Day
**

Saturday, December 3, 2016

BASCD is an annual one-day meeting focused on fostering interactions and collaborations between researchers in the fields of scientific computing and computational science and engineering from the San Francisco Bay Area. The event provides junior researchers a venue to present their work to the local community, and for the Bay Area scientific and computational science and engineering communities at large to interchange views on today’s multidisciplinary computational challenges and state-of-the-art developments.

The event this year will be organized by the Institute for Computational and Mathematical Engineering (ICME) at Stanford.

## Schedule

BASCD 2016 will be on Saturday, December 3, 2016. The temporary schedule is as follows.

Time | Event | Speaker |
---|---|---|

09:00-09:20 | Registration | |

09:20-09:30 | Welcome Remarks | |

09:30-09:55 | On the hyperbolicity of Grad's moment system in gas kinetic theory | Yuwei Fan |

09:55-10:20 | Serial Femtosecond Crystallography at the Linac Coherent Light Source | Chun Hong Yoon |

10:20-10:45 | Deep Learning for Turbulence Modeling | Julia Ling |

10:45-11:00 | Coffee Break | |

11:00-11:25 | A Hybrid Adaptive Compressible/Low-Mach-Number Method | Emmanuel Motheau |

11:25-11:50 | Fast Randomized Methods for Data-Intensive Convex Optimization | Mert Pilanci |

11:50-12:15 | The Landscape of Empirical Risk for Non-convex Losses | Song Mei |

12:15-13:30 | Lunch Break | |

13:30-13:55 | A Convex Relaxation Framework for Strategic Bidding in Electricity Markets | Mahdi Ghamkhari |

13:55-14:20 | Rational Krylov Methods for Solving Nonlinear Eigenvalue Problems | Roel Van Beeumen |

14:20-14:45 | Adaptively Compressed Polarizability Operator for Accelerating Large Scale Ab Initio Phonon Calculations | Ze Xu |

14:45-15:00 | Coffee Break | |

15:00-15:25 | Low Rank Tensor Approximation of Multivariate Functions for Quantum Chemistry Applications | Prashant Rai |

15:25-15:50 | Massively Parallel Ab Initio Plasma Simulations Using the Particle-in-cell Method | Paulo Alves |

15:50-16:15 | Performance Advantages of Using a Burst Buffer for Scientific Workflows | Andrey Ovsyannikov |

All talks will be 20 minutes with 5 minutes for Q&A.

## Speakers

### Yuwei Fan

#### Math, Stanford

#### Title: *
On the hyperbolicity of Grad's moment system in gas
kinetic theory
*

##### Abstract:
*
In gas kinetic theory, it is well-known that Grad's
moment system is not globally hyperbolic. And I. Muller
et. al.(1998) point out that Grad's 13-moment system is
not globally hyperbolic for 1D flow. In this talk, we
will point out for 3D case, for each equilibrium state,
none of its neighborhoods is contained in the
hyperbolicity region. This observation offers an
explanation why the well-known Grad's moment system does
not succeed in the past 60 years.
*

#### Slides: PDF

### Chun Hong Yoon

#### Data analysis Group, SLAC/LCLS

#### Title: *
Serial Femtosecond Crystallography at the Linac Coherent
Light Source
*

##### Abstract:
*
Serial femtosecond crystallography (SFX) is a
diffractive imaging technique for determining the
molecular structure of proteins with a femtosecond laser
pulse (1/1,000,000,000,000,000 of a second) to outrun
the onset of radiation damage. Structural information
about biological macromolecules near the atomic scale
provides important insight into the functions of these
molecules and enables better drug design. To date,
scientists have relied on a known structure of a similar
protein to solve the phase problem. We have recently
demonstrated de novo phasing (i.e. without a model of a
similar structure) of a selenobiotyl-streptavidin
structure at the Linac Coherent Light Source (LCLS). I
will talk about our scientific results and the
computational challenges we face at the LCLS.
*

#### Slides: PDF

### Julia Ling

#### Sandia National Labs

#### Title: *
Deep Learning for Turbulence Modeling
*

##### Abstract:
*
Turbulence models are broadly used in industry for
aerodynamic simulations, heat transfer simulations, and
engine simulations. However, because these simulations
are often inaccurate, they are not always useful in the
design cycle. The turbulence models in these simulations
are thirty to forty years old and were originally based
on sparse experimental data and strong assumptions. With
the increasing availability of large, high-fidelity data
sets, there is now the possibility of using deep
learning to provide a more accurate turbulence model.
This talk will discuss how to apply machine learning to
a physical system: how to embed domain knowledge and
constraints, and how to provide the subject matter
experts with feedback.
*

### Emmanuel Motheau

#### CRD, Lawrence Berkeley National Lab

#### Title: *
A Hybrid Adaptive Compressible/Low-Mach-Number Method
*

##### Abstract:
*
Flows in which the primary features of interest do not
rely on high-frequency acoustic effects, but in which
long-wavelength acoustics play a nontrivial role,
present a computational challenge. Integrating the
entire domain with low Mach number methods would remove
all acoustic wave propagation, while integrating the
entire domain with the fully compressible equations
would be prohibitively expensive due to the CFL time
step constraint. For example, thermoacoustic
instabilities might require fine resolution of the
fluid/chemistry interaction but not require fine
resolution of acoustic effects, yet one does not want to
neglect the long-wavelength wave propagation and its
interaction with the larger domain.*

The proposed talk will present a new hybrid algorithm
that has been developed to address these type of
phenomena. In this new approach, the fully compressible
Navier-Stokes equations are solved on the entire domain,
while their low Mach number counterparts are solved on a
subregion of the domain with higher spatial resolution.
The coarser acoustic grid communicates inhomogeneous
divergence constraints to the finer low Mach number
grid, so that the low Mach number method allows the
long-wavelength acoustics. We will demonstrate the
effectiveness of the new method on practical cases such
as the aeroacoustics generated by the vortex formation
in an unstable low-Mach mixing layer.

The proposed talk will present a new hybrid algorithm that has been developed to address these type of phenomena. In this new approach, the fully compressible Navier-Stokes equations are solved on the entire domain, while their low Mach number counterparts are solved on a subregion of the domain with higher spatial resolution. The coarser acoustic grid communicates inhomogeneous divergence constraints to the finer low Mach number grid, so that the low Mach number method allows the long-wavelength acoustics. We will demonstrate the effectiveness of the new method on practical cases such as the aeroacoustics generated by the vortex formation in an unstable low-Mach mixing layer.

### Mert Pilanci

#### Math+X, Stanford

#### Title: *
Fast Randomized Methods for Data-Intensive Convex
Optimization
*

##### Abstract:
*
With the advent of massive data sets, statistical
learning and information processing techniques are
expected to enable unprecedented possibilities for
better decision making. However, existing algorithms for
mathematical optimization, which are the core component
in these techniques, often prove ineffective for scaling
to the extent of all available data. This talk focuses
on random projection methods in the context of general
convex optimization and statistical estimation problems
to address this challenge. *

First, I'll describe the theoretical relation between
complexity and optimality of solutions. Then, I'll
provide a general information-theoretic lower bound on
any method that is based on random projection, which
surprisingly shows that the most widely used form of
random projection is, in fact, statistically
sub-optimal. I will finally present a novel method,
which iteratively refines the solutions to achieve
statistical optimality and generalize our method to
optimizing arbitrary convex functions of a large data
matrix. The proposed method, called the Newton Sketch,
is a faster randomized version of the well-known
Newton's Method with linear computational complexity in
the input data. Newton Sketch enables solving large
scale optimization and statistical inference problems
orders-of-magnitude faster than existing methods.

First, I'll describe the theoretical relation between complexity and optimality of solutions. Then, I'll provide a general information-theoretic lower bound on any method that is based on random projection, which surprisingly shows that the most widely used form of random projection is, in fact, statistically sub-optimal. I will finally present a novel method, which iteratively refines the solutions to achieve statistical optimality and generalize our method to optimizing arbitrary convex functions of a large data matrix. The proposed method, called the Newton Sketch, is a faster randomized version of the well-known Newton's Method with linear computational complexity in the input data. Newton Sketch enables solving large scale optimization and statistical inference problems orders-of-magnitude faster than existing methods.

### Song Mei

#### ICME, Stanford

#### Title: *
The Landscape of Empirical Risk for Non-convex Losses
*

##### Abstract:
*
Most high-dimensional estimation and prediction methods
propose to minimize the empirical risk that is written
as a sum of losses associated to each data point. In
this paper we focus on the case of non-convex losses,
that is practically important but still poorly
understood. Classical empirical process theory implies
uniform convergence of the empirical risk to the
population risk. While uniform convergence implies
consistency of the resulting M-estimator, it does not
ensure that the latter can be computed efficiently.*

Empirical risk minimization is often carried out via
first-order algorithms such as gradient descent and its
generalizations. In order to capture the complexity of
computing M-estimators, we propose to study the
landscape of the empirical risk, namely its stationary
points and their properties. We establish uniform
convergence of the gradient and Hessian of the empirical
risk to their population counterparts, as soon as the
number of samples becomes larger than the number of
unknown parameters (modulo logarithmic factors). We
demonstrate that –in several examples– this result
implies a complete characterization of the landscape of
the empirical risk, and of the convergence properties of
descent algorithms.

Empirical risk minimization is often carried out via first-order algorithms such as gradient descent and its generalizations. In order to capture the complexity of computing M-estimators, we propose to study the landscape of the empirical risk, namely its stationary points and their properties. We establish uniform convergence of the gradient and Hessian of the empirical risk to their population counterparts, as soon as the number of samples becomes larger than the number of unknown parameters (modulo logarithmic factors). We demonstrate that –in several examples– this result implies a complete characterization of the landscape of the empirical risk, and of the convergence properties of descent algorithms.

#### Slides: PDF

### Mahdi Ghamkhari

#### CS, UC Davis

#### Title: *
A Convex Relaxation Framework for Strategic Bidding
in Electricity Markets
*

##### Abstract:
*
The state-of-the-art approach for solving strategic
bidding problem in electricity markets is to solve a
mixed-integer linear program (MILP). However, as
the size of power network, power scheduling horizon
and uncertainty in the participants’ bids increase,
the computational time of solving the MILP grows
exponentially.
In this talk, we present a convex relaxation
framework to reformulate the strategic bidding
problem as a convex optimization problem. The
proposed framework is based on Schmudgen's
Positivstellensatz in semi-algebraic geometry. For
the sake of efficiency, we highly customize and
relax the polynomials in Positivestellensatz
Theorem. Further, to compensate for the loss of
optimality that are caused by the relaxations, we
also propose a heuristic algorithm that when
combined with the above relaxation technique results
in both high optimality and computational
efficiency. Extensive numerical studies on IEEE
30-Bus power networks show that our approach gives a
close-to-optimal bidding solution, in a computation
time that increases only linearly when the
scheduling horizon or uncertainty increase."
*

#### Slides: PDF

### Roel Van Beeumen

#### CRD, Lawrence Berkeley National Laboratory

#### Title: *
Rational Krylov Methods for Solving Nonlinear
Eigenvalue Problems
*

##### Abstract:
*
We present an overview of rational Krylov methods for
solving the nonlinear eigenvalue problem (NLEP): A(λ)x =
0. For many years linearizations are used for solving
polynomial eigenvalue problems. On the other hand, for
the general nonlinear case, A(λ) can first be
approximated by a (rational) matrix polynomial and then
a convenient linearization is used. The major
disadvantage of linearization based methods is the
growing memory and orthogonalization costs with the
iteration count, i.e., in general they are proportional
to the degree of the polynomial. Therefore, we introduce
a new framework of Compact Rational Krylov (CORK)
methods which maximally exploit the structure of the
linearization pencils. In this way, the extra memory and
orthogonalization costs due to the linearization of the
original eigenvalue problems are negligible for
large-scale problems.
*

#### Slides: PDF

### Ze Xu

#### Math, UC Berkeley

#### Title: *
Adaptively Compressed Polarizability Operator for
Accelerating Large Scale Ab Initio Phonon
Calculations
*

##### Abstract:
*
Phonon calculations based on first principle electronic
structure theory, such as the Kohn-Sham density
functional theory, have wide applications in physics,
chemistry and material science. The computational cost
of first principle phonon calculations typically scales
steeply as O(N_e^4), where N_e is the number of
electrons in the system. In this talk, we introduce a
new method to reduce the computational complexity of
computing the full dynamical matrix, and hence the
phonon spectrum, to O(N_e^3). The key concept for
achieving this is to compress the polarizability
operator adaptively with respect to the perturbation of
the potential due to the change of the atomic
configuration. Such adaptively compressed polarizability
operator (ACP) allows accurate computation of the phonon
spectrum. The reduction of complexity only weakly
depends on the size of the band gap, and the method is
applicable to insulators as well as semiconductors with
small band gaps. Furthermore, the performance of the ACP
formulation can be improved by splitting the
polarizability into a near singular component that is
statically compressed, and a smooth component that is
adaptively compressed. The new split representation
maintains the O(N_e^3) complexity, and accelerates
nearly all components of the ACP formulation,
including Chebyshev interpolation of energy levels,
iterative solution of Sternheimer equations, and
convergence of Dyson equations. We demonstrate the
effectiveness of our method using one-dimensional and
two-dimensional model problems in insulating and
metallic regimes, as well as its accuracy for real
molecules and solids.
*

#### Slides: PDF

### Prashant Rai

#### Sandia National Labs

#### Title: *
Low Rank Tensor Approximation of Multivariate
Functions for Quantum Chemistry Applications
*

##### Abstract:
*
Tensor approximation methods have been known to
successfully deal with high dimensional objects by
tackling the issue of curse of dimensionality (i.e.
exponential growth of parameters with dimension). In the
context of high dimensional function approximation,
coefficients of a multivariate function on approximation
bases can be stored in a d-dimensional array i.e.
coefficient tensor (d being the dimension of the
function). A low rank decomposition of this tensor can
then be seen as the corresponding low rank approximation
of the function. Exploiting low rank structure, if it
exists, requires estimation of parameters that scale
only linearly with dimension.*

This presentation will introduce a tensor based
interpretation of high dimensional functions followed by
its decomposition in several low rank formats. It will
then introduce algorithms to construct these
approximations and will conclude with their application
on real life examples ranging from uncertainty
propagation to quantum chemistry.

This presentation will introduce a tensor based interpretation of high dimensional functions followed by its decomposition in several low rank formats. It will then introduce algorithms to construct these approximations and will conclude with their application on real life examples ranging from uncertainty propagation to quantum chemistry.

#### Slides: PDF

### Paulo Alves

#### SLAC National Accelerator Laboratory

#### Title: *
Massively Parallel Ab Initio Plasma Simulations
Using the Particle-in-cell Method
*

##### Abstract:
*
Plasmas make up over 99% of the visible Universe. Their
dynamics govern the evolution of stars and galaxies, as
well as the acceleration of the most energetic particles
in the Universe. In the laboratory, plasmas offer a
platform for compact radiation and particle sources, and
hold potential of providing an unlimited energy supply
via controlled nuclear fusion. *

Numerical simulations play a central role in plasma
physics research, providing insight into the complex and
highly nonlinear nature of plasma dynamics. Here, we
will present the particle-in-cell (PIC) simulation
method, which provides a first-principles ab initio
description of plasma systems. This method describes the
detailed trajectories of charged plasma particles that
move according to self-consistent electric and magnetic
fields that the particles themselves collectively
produce. The detailed microphysics captured by this
simulation method is essential to understanding highly
relativistic plasmas and their interaction with strong
electromagnetic fields. This particle-based simulation
method, however, is computationally very demanding,
requiring enormous computational resources to model
laboratory and astrophysically relevant systems.

In order to harness the full potential of the World’s
leading supercomputers, massively parallel PIC codes
must employ sophisticated optimization strategies,
including dynamic load balancing and specialized
algorithms for particular supercomputing architectures
(e.g. BlueGene/Q, GPUs). Here, we will discuss the main
features of our highly optimized massively parallel PIC
code, OSIRIS, which has demonstrated efficient strong
scaling of 80% in up to 1.6M cores on Sequoia at
Lawrence Livermore National Laboratory. We will end by
discussing some of the most outstanding scientific
challenges in plasma physics research that PIC codes
have the potential to uncover.

Numerical simulations play a central role in plasma physics research, providing insight into the complex and highly nonlinear nature of plasma dynamics. Here, we will present the particle-in-cell (PIC) simulation method, which provides a first-principles ab initio description of plasma systems. This method describes the detailed trajectories of charged plasma particles that move according to self-consistent electric and magnetic fields that the particles themselves collectively produce. The detailed microphysics captured by this simulation method is essential to understanding highly relativistic plasmas and their interaction with strong electromagnetic fields. This particle-based simulation method, however, is computationally very demanding, requiring enormous computational resources to model laboratory and astrophysically relevant systems.

In order to harness the full potential of the World’s leading supercomputers, massively parallel PIC codes must employ sophisticated optimization strategies, including dynamic load balancing and specialized algorithms for particular supercomputing architectures (e.g. BlueGene/Q, GPUs). Here, we will discuss the main features of our highly optimized massively parallel PIC code, OSIRIS, which has demonstrated efficient strong scaling of 80% in up to 1.6M cores on Sequoia at Lawrence Livermore National Laboratory. We will end by discussing some of the most outstanding scientific challenges in plasma physics research that PIC codes have the potential to uncover.

#### Slides: PDF

### Andrey Ovsyannikov

#### NESAP, Lawrence Berkeley National Laboratory

#### Title: *
Performance Advantages of Using a Burst Buffer for
Scientific Workflows
*

##### Abstract:
*
Emerging exascale systems have the ability to accelerate
the time-to-discovery for scientific workflows. However,
as these workflows become more complex, their generated
data has grown at an unprecedented rate, making I/O
constraints challenging. To address this problem
advanced memory hierarchies, such as burst buffers, have
been proposed as intermediate layers between the compute
nodes and the parallel file system. In this paper, we
utilize Cray DataWarp burst buffer coupled with
in-transit processing mechanisms, to demonstrate the
advantages of advanced memory hierarchies in preserving
traditional coupled scientific workflows. We consider
the Chombo-Crunch package for modeling of subsurface
flows coupled with the VisIt visualization and analysis
software and ancillary post-processing.
*

#### Slides: PDF

## Registration

While BASCD is a free event, registration is required in order to attend. Please register by the deadline: Monday, November 21, 2016.

## Location

BASCD 2016 will take place on the Stanford campus in the Jen-Hsun Huang Engineering Center in the Science and Engineering Quad.

### Address

Huang Engineering Center

475 Via Ortega, Mackenzie Room (3rd floor)

Stanford, CA 94305

### Other information

- Stanford Google Map
- Visiting the Stanford campus
- Visitor parking (parking is generally free on Saturday, please check posted signs)
- Parking and circulation map