Stan Woosley
HEP Case Study Worksheet
1.1. Project Information - Computational Astrophysics Consortium
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Document Prepared By |
Stan Woosley |
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Project Title |
Computational Astrophysics Consortium |
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Principal Investigator |
Stan Woosley |
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Participating Organizations |
Princeton; LBNL; LLNL; Minnesota; UCB |
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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.
Our research project follows stars and the explosive phenomena they produce, especially supernovae of all types, gamma-ray bursts, and x-ray bursts. It also involves the development of the common software needed. Our principal science topics are - in order of priority - 1) models for Type Ia supernovae, 2) radiation transport, spectrum formation, and nucleosynthesis in model supernovae of all types; 3) the observational implications of these results
for experiments in which DOE has an interest, especially JDEM/SNAP, and ground based supernova searches; 4) core collapse supernovae; 5) gamma-ray bursts; 6) ``hypernovae'' from Population III stars; and 7) x-ray bursts. Models of these phenomena share a common need for nuclear reactions and radiation transport coupled to multi-dimensional fluid flow. Our principal goals are not only a better first-principles understanding of supernovae, gamma-ray bursts, and nucleosynthesis, but also theoretical databases that will allow the more precise and reliable use of supernovae for cosmological distance determination. Our subproject on the nucleosynthesis in stars and supernovae aims to be the most complete in the world and highlights nuclear uncertainties (especially in the r-process and rp-process) In the next 3 years, our program will be the only way to address significant gaps in our understanding of these objects and potential systematics in their use as cosmological probes to confront one of the greatest mysteries in high-energy physics and astronomy today -- the nature of dark energy.(see our successful DOE SciDAC 2 proposal)
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)
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?
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.
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Facilities Used or Using |
NERSC OLCF ACLF NSF Centers Other: |
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Architectures Used |
Cray XT IBM Power BlueGene Linux Cluster Other: |
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Total Computational Hours Used per Year |
Core-Hours |
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NERSC Hours Used in 2009 |
Core-Hours |
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Number of Cores Used in Typical Production Run |
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Wallclock Hours of Single Typical Production Run |
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Total Memory Used per Run |
GB |
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Minimum Memory Required per Core |
GB |
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Total Data Read & Written per Run |
GB |
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Size of Checkpoint File(s) |
GB |
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Amount of Data Moved In/Out of NERSC |
GB per |
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On-Line File Storage Required (For I/O from a Running Job) |
GB and Files |
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Off-Line Archival Storage Required |
GB 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.
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Computational Hours Required per Year |
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Anticipated Number of Cores to be Used in a Typical Production Run |
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Anticipated Wallclock to be Used in a Typical Production Run Using the Number of Cores Given Above |
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Anticipated Total Memory Used per Run |
GB |
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Anticipated Minimum Memory Required per Core |
GB |
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Anticipated total data read & written per run |
GB |
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Anticipated size of checkpoint file(s) |
GB |
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Anticipated On-Line File Storage Required (For I/O from a Running Job) |
GB and Files |
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Anticipated Amount of Data Moved In/Out of NERSC |
GB per |
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Anticipated Off-Line Archival Storage Required |
GB 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.
4c. Please list any known or anticipated architectural requirements (e.g., 2 GB memory/core, interconnect latency < 3 #s).
4d. Please list any new software, services, or infrastructure support you will need over the next 5 years.
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.
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).


