TSUBAME Grand Challenge Program
SuperCon Programming Contest
TSUBAME Grand Chalenge

SuperCon programming contest

The TSUBAME Grand Challenge solicits proposals for grand challenge problems that can utilize all nodes of TSUBAME3.0 and has two categories.
Category A Exclusive use of all nodes for 24 hours
Category B Exclusive use of 1/3 of the nodes for up to 1 week

The SuperCon programming context is held every summer in cooperation
with Osaka University, where high school students come to ourcampuses to compete in a programming context using oursupercomputers. SuperCon started back in 1995 and has been
held yearly. There is a qualifying round where students use theirlocal environment to solve a given problem. About 10 teams
consisting of 2-3 members each, will go on to the final round,which is held at Tokyo Tech, and Osaka University, for teams
from the east and west side of Japan, respectively.

Number of Accepted Proposals

This year the students solved a network analysis problem for
epidemics on the Fugaku supercomputer.
The following teams won the competition:
1st place: team Citrus (Nada High School)
2nd place: team NPC (N High School)
3rd place: team Novice (Nada High School)
Below are photos from previous years when it was in person.

We started this program in 2011, and have continued to performthe Grand Challenge runs twice a year.
Under this program, we have adopted total 45 grand challenge projects
some of which were awarded Gordon Bell prizes as below.

2011 ACM Gordon Bell Prize: Special Achievements
in Scalability and Time-to-Solution“Peta-scale Phase-Field Simulation for Dendritic
Solidifcation on the TSUBAME 2.0 Supercomputer”

2011 ACM Gordon Bell Prize:
Honorable Mention
Large scale bioﬂuidics
simulations on TSUBAME2

2011 Graph500 Challenge
on TSUBAME 2.0

Grand Challenge Project 1:

Analysis of membrane permeation processof a cyclic peptide (Cyclosporin A) bylarge-scale simulation based on
two-dimensional replica-exchange method

Teams at Tokyo Tech.

Teams at Osaka U.

Grand Challenge Project 2:
An Exhaustive Study of Optimizer
Characteristics for OOD Generalizationon the ImageNet Scale

Wataru Kotsugai, Takuya Fujie, Masatake Sugita, Keisuke Yanagisawa,
Masahito Ohue, Yutaka Akiyama (Tokyo Institute of Technology)

Motivation

In order to improve the success rate of "cyclic peptide drug discovery"
attracting attention from pharmaceutical companies around the world,
it is an emergent task to elucidate the cell membrane permeation
mechanism of large cyclic peptides with more than eight residues andto develop a method for improving membrane permeability based on
the mechanism.

MethodIn this study, a membrane permeation simulation for a lipid bilayer wasperformed for cyclosporin A (CSA), which is a large cyclic peptide with
11 residues. We performed molecular dynamics (MD) simulations for
CSA membrane permeation, under nine different calculation conditions.

Results of Grand Challenge
It was clarified that the behavior of membrane permeation changes
significantly when the cholesterol concentration exceeds 40%.
It was also found that thedifference in force fieldparameters causes a
difference of 10 kcal/mol
or more in the estimationof the main barrier ofmembrane permeation.
In conclusion, we havediscovered several clues foroptimal calculation conditions
for predicting the membrane
permeability of large cyclic
peptides.
Figure: 3D structure of cyclosporin A crossing the model membrane

Hiroki Naganuma, Ioannis Mitliagkas (Université de Montréal, Mila)
Tetsuya Motokawa (Retty), Kota Ishikawa (Denso IT Laboratory)
Ikuro Sato (Tokyo Institute of Technology)

Motivation

The machine learning community has studied how optimizer selection
influences in-distribution generalization. However, the out-of-distribution
generalization capability of the various optimization methods,
which is of great importance in practical applications, is still unexplored.

MethodWe investigate the relationship between in-distribution accuracy andout-of-distribution accuracy for different choices of optimizers.
We search over a wide range of hyperparameters and examine theclassification accuracy (in and out-of-distribution) for over 20,000 models.

Key results

Our findings show that the out-of-distribution test accuracy of SGD iscorrelated with the in-distribution test accuracy, while Adam has a weakercorrelation. This result implies the in-distribution test accuracy of adaptive
optimizers may not always be a reliable indicator of out-of-distribution
test accuracy.

https://www.gsic.titech.ac.jp/sc21