Life and Health Sciences. Summary Report. “Bench to Bedside” coverage. Participants with very broad spectrum of expertise bridging all scales From molecule to population L ife Sciences: c omputational chemistry, computational biology m olecular, cellular, and systems biology applications
What technical breakthroughs in science and engineering research can be enabled by exascale platforms and are attractive targets for Japan-US collaboration over the next 10 years?
Synergies in Life Sciences:
strong scaling for MD simulations
bridging length and time scale for cellular simulations (cell community)
whole primate brain simulation
data analytics for event detection, feature selection, sub-state discovery
What is the representative suite of applications in your research area, available today, which should form the basis of your co-design communication with computer architects?
How can the application research community, represented by a topical breakout at this workshop, constructively engage the vendor community in co-design?
How can you best manage the “conversations” with computer designers/architects around co-design such that (1) they are practical for computer design, and (2) the results are correctly interpreted within both communities?
Benchmarks: Already available within the molecular simulation community. Not as much for the clinical and healthcare domains. No standard benchmarks for Neuroscience related applications
Examples include FFTW, Fast-multipole methods, Grid computations,
Describe the most important programming models and environment in use today within your community and characterize these as sustainable or unsustainable.
MPI and Open MP for simulations; Neuroscience: data parallel model of computations;
Programming languages: C/C++, Python, Java
Support for Natural Language Processing required for Healthcare/ Clinical sciences.
Problems include: persistance of data; load balancing with heterogenous datasets; Data transfer costs not expensive in neuroscience applications but more expensive in life sciences data.
a) YES, we need such tools
Does your community have mature workflow tools that are implemented within leadership computing environments to assist with program composition, execution, analysis, and archival of results? If no, what are your needs and is their opportunity for value added?
WE ARE NOT THERE YET
What are the new programming models, environments and tools that need to be developed to achieve our science goals with sustainable application software?
ANTON as a supercomputer for Molecular dynamics simulations, installed in Pittsburgh Supercomputing Center;
AMBER simulation implementation on GPUs;
Neuromorphic chips in computational neuroscience
Ongoing debate whether we need HPC for applications in this domain
Is there a history, a track record in your research community for co-design for HPC systems in the installed machines in the past, and is there any co-design study done for these systems to document the effectiveness of co-design?
1. Benchmarking of different tools against different applications in terms of scalability, efficiency, and performance
2. In situ analysis and visualization of simulations to guide simulations
3. Continue discussion on systems neuroscience