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

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

This year the students solved an automata analysis problem forepidemics on the Fugaku supercomputer.
The following teams won the competition:
1st place: team Citrus (Nada High School)
2nd place: team noCrepuS (Makuhari High School)
3rd place: team bubuzuke,T0M0K4ZU (Matsumoto ShūhōSecondary School, Tokyo Tech High School of Sci. and Tech.)
Below are photos from previous years when it was in person.

2011 Graph500 Challenge
on TSUBAME 2.0

Grand Challenge Project 1:

Teams at Tokyo Tech.

Teams at Osaka U.

Grand Challenge Project 2:
Development of a method to accelerate convergence An Exhaustive Study of Optimizerof large-scale simulations of membrane permeation
CharacteristicsforOOD
Generalization
processes of cyclic peptides based on atwo-dimensional replica exchange method.
on the ImageNet Scale
Yuudai Noso, Takuya Fujie, Masatake Sugita, Keisuke Yanagisawa,
Masahito Ohue, Yutaka Akiyama (Tokyo Institute of Technology)

Motivation

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

Cyclic peptide drug discovery is of interest to pharmaceutical companies
Motivationworldwide. To improve the success rate of the discovery, we are developing
The machine learning community has studied how optimizer selection
a molecular dynamics simulation protocol to predict membrane permeability.
influences in-distribution generalization. However, the out-of-distribution
Methodgeneralization capability of the various optimization methods,
In this calculation, several hundred ns are dedicated to convergence of the which is of great importance in practical applications, is still unexplored.
lipid membrane structure and the interaction between the lipid membrane
Methodand the peptide, which are closely related to the convergence of the wholecalculation.
We investigate the relationship between in-distribution accuracy andout-of-distribution accuracy for different choices of optimizers.
Results of Grand Challenge
We search over a wide range of hyperparameters and examine theIt has been recognized that reduction of the computational time spent onclassification accuracy (in and out-of-distribution) for over 20,000 models.
this process is important for industrial applications. In this project,
we tried to accelerate the convergence of the conformations of lipidKey
results
molecules by raising the temperature of the entire system in the early
Our findings show that the out-of-distribution test accuracy of SGD isstages of the simulation, thereby significantly reducing the time required
correlated with the in-distribution test accuracy, while Adam has a weaker
for the convergence of the calculation. The protocol was tested bycorrelation. This result implies the in-distribution test accuracy of adaptive
performing 500ns simulations onoptimizers may not always be a reliable indicator of out-of-distribution
six peptides, utilizing 56 node
test accuracy.
taking up approximately 85 hours
per peptide. As a result, it wasconfirmed that the error of
potential mean force (PMF) wasremarkably reduced for four outof six peptides. We are currently
analyzing the degree ofconformational convergenceof peptides and membranes
in detail to find a way to
improve the performance ofFigure: 3D structure of cyclosporin A crossing the model membrane
our calculations.

https://www.gsic.titech.ac.jp/sc22