NERSCPowering Scientific Discovery Since 1974

Xianzhu Tang

FES Requirements Worksheet

1.1. Project Information - Plasma materials interaction

Document Prepared By

Xianzhu Tang

Project Title

Plasma materials interaction

Principal Investigator

Xianzhu Tang

Participating Organizations

Los Alamos National Laboratory

Funding Agencies

 DOE SC  DOE NSA  NSF  NOAA  NIH  Other:

2. Project Summary & Scientific Objectives for the Next 5 Years

Please give a brief description of your project - highlighting its computational aspect - and outline its scientific objectives for the next 3-5 years. Please list one or two specific goals you hope to reach in 5 years.

This project combines kinetic modeling of boundary plasma and atomistic modeling of wall material response to plasma irradiation to understand the physics of plasma-materials interaction in fusion reactor conditions. The plasma modeling is based on solving the six dimensional kinetic equation in the sheath and presheath region. The materials modeling is based on molecular dynamics, accelerated molecular dynamics, and kinetic Monte Carlo simulations. These involve a suite of simulation codes developed at Los Alamos National Laboratory: VPIC, SPASM, and TAD/AMDF. An example of the specific research objectives would be understanding the tritium permeation and trapping in displacement damaged tungsten.

3. Current HPC Usage and Methods

3a. Please list your current primary codes and their main mathematical methods and/or algorithms. Include quantities that characterize the size or scale of your simulations or numerical experiments; e.g., size of grid, number of particles, basis sets, etc. Also indicate how parallelism is expressed (e.g., MPI, OpenMP, MPI/OpenMP hybrid)

VPIC: Particle-in-cell code solving the six dimensional kinetic equation plus the Maxwell equations. Optimized for minimize data traffic. 
SPASM: standard molecular dynamics code capable of high performance computing on hybrid architecture and modeling the dynamic behavior of materials under extreme conditions. 
TAD/AMDF: LANL's accelerated molecular dynamics code/framework which incorporates the three acceleration methods: parallel replica, temperature-accelerated and hyper-dynamics, to allow atomistic modeling of diffusive transport in sold materials. 
All codes use MPI/POSIX threads. 
VPIC, SPASM, and AMDF have achieved petaflop performance on LANL's Roadrunner.  

3b. Please list known limitations, obstacles, and/or bottlenecks that currently limit your ability to perform simulations you would like to run. Is there anything specific to NERSC?

The TAD code deploys a global constrained minimization (currently conjugate-gradient), whose convergence (scalability) can be algorithmically improved. 

3c. Please fill out the following table to the best of your ability. This table provides baseline data to help extrapolate to requirements for future years. If you are uncertain about any item, please use your best estimate to use as a starting point for discussions.

Facilities Used or Using

 NERSC  OLCF  ACLF  NSF Centers  Other: Roadrunner

Architectures Used

 Cray XT  IBM Power  BlueGene  Linux Cluster  Other:  Roadrunner opetron/cell hybrid (the data below using AMDF code)

Total Computational Hours Used per Year

 a few tens of millions Core-Hours

NERSC Hours Used in 2009

 0 Core-Hours

Number of Cores Used in Typical Production Run

10,000

Wallclock Hours of Single Typical Production Run

 24

Total Memory Used per Run

 1000 GB

Minimum Memory Required per Core

 0.1 GB

Total Data Read & Written per Run

 10 GB

Size of Checkpoint File(s)

 0.1 GB

Amount of Data Moved In/Out of NERSC

 10s GB per  day

On-Line File Storage Required (For I/O from a Running Job)

 1 TB and  Files

Off-Line Archival Storage Required

 10 TB and  Files

Please list any required or important software, services, or infrastructure (beyond supercomputing and standard storage infrastructure) provided by HPC centers or system vendors.

 

4. HPC Requirements in 5 Years

4a. We are formulating the requirements for NERSC that will enable you to meet the goals you outlined in Section 2 above. Please fill out the following table to the best of your ability. If you are uncertain about any item, please use your best estimate to use as a starting point for discussions at the workshop.

Computational Hours Required per Year

 50 million

Anticipated Number of Cores to be Used in a Typical Production Run

 120000

Anticipated Wallclock to be Used in a Typical Production Run Using the Number of Cores Given Above

 24

Anticipated Total Memory Used per Run

 12000 GB

Anticipated Minimum Memory Required per Core

0.1 GB

Anticipated total data read & written per run

 100 GB

Anticipated size of checkpoint file(s)

 0.1 GB

Anticipated Amount of Data Moved In/Out of NERSC

 GB per  

Anticipated On-Line File Storage Required (For I/O from a Running Job)

 TB and  Files

Anticipated Off-Line Archival Storage Required

 TB and  Files

4b. What changes to codes, mathematical methods and/or algorithms do you anticipate will be needed to achieve this project's scientific objectives over the next 5 years.

A new global minimization/search algorithm for TAD.  
There is also a development plan which couples the plasma and materials modeling on the fly.

4c. Please list any known or anticipated architectural requirements (e.g., 2 GB memory/core, interconnect latency < 1 μs).

4d. Please list any new software, services, or infrastructure support you will need over the next 5 years.

Need NERSC help on performance tuning and IO optimization. 

4e. It is believed that the dominant HPC architecture in the next 3-5 years will incorporate processing elements composed of 10s-1,000s of individual cores, perhaps GPUs or other accelerators. It is unlikely that a programming model based solely on MPI will be effective, or even supported, on these machines. Do you have a strategy for computing in such an environment? If so, please briefly describe it.

Except for TAD, all other codes worked well on Roadrunner. But we have not tried GPU yet, though there is a pilot project associated with SPASM. 

New Science With New Resources

To help us get a better understanding of the quantitative requirements we've asked for above, please tell us: What significant scientific progress could you achieve over the next 5 years with access to 50X the HPC resources you currently have access to at NERSC? What would be the benefits to your research field if you were given access to these kinds of resources?

Please explain what aspects of "expanded HPC resources" are important for your project (e.g., more CPU hours, more memory, more storage, more throughput for small jobs, ability to handle very large jobs).

With 50X increase in computational power, we will have a much improved chance to understand key physical and chemical processes of plasma/materials interaction in fusion reactor conditions. Specifically the boundary plasma will be understood on a kinetic level, while the materials response on diffusive time scale will be understood at the atomistic level. The rate and pathway information will be essential to understand and model the materials behavior at meso- and macro-scale. This will build the scientific foundation to overcome the extreme challenges of fusion materials in the plasma facing component of a fusion reactor. 
 
On the importance of the extended HPC resources, we note that the study of long time kinetics of materials typically operates on two modes: integration of very long trajectories and thorough analysis of these  trajectories to extract key processes that control the evolution of the system. The first phase, when carried out with Parallel Replica Dynamics, benefits from large-scale, high-node-count, computational capabilities. A 50x increase in computing power would potentially translate into a 50x increase in the time  horizon that can be directly probed, hence allowing us to further bridge the gap between computationally amenable and technologically relevant timescales. Such an increase is important because rare events that do not manifest in  shorter simulations impede our capability to extrapolate the behavior of the  material to longer times. The second phase often requires an high-throughput,  extensive exploration of critical kinetic steps along the paths generated in the first phase. Increasing the available computing power at this step would  significantly improve the robustness of our models of long time evolution by identifying alternative competing pathways. In summary, our investigation would greatly benefit both from the availability of massively-parallel resources targeting both very large individual simulations and large-number of smaller scale simulations. Our memory and storage requirements are usually modest and we do not foresee a need for a proportional increase in their availability.