Life and Health Sciences

1. Life and Health Sciences Summary Report

2. “Bench to Bedside” coverage • Participants with very broad spectrum of expertise bridging all scales • From molecule to population • Life Sciences: • computational chemistry, computational biology • molecular, cellular, and systems biology applications • Health Sciences: • biomedical engineering, clinical sciences • multi-modality, multi-source data integration and analytics for personalized medicine and population health applications

3. Charge Question 1 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 biological self-assembly bridging length and time scale for cellular simulations (cell community) whole primate brain simulation data analytics for event detection, feature selection, sub-state discovery

4. Charge Question 2 • Identify core data analytic routines that can use parallel architectures for execution – wrappers that can automatically parallelize code • Data Generation and Analysis must happen in tandem – within one workflow and involve interactive visualization and feedback – the latter critical to clinical sciences as well 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?

5. Charge Questions 3 • Biological simulations are a small proportion of the market • Can the existing architectures support the “Brain” initiatives? Involving vendors in discussion of these designs How can the application research community, represented by a topical breakout at this workshop, constructively engage the vendor community in co-design? • How should these various aspects of the application and architecture be optimized for effective utilization of exascale compute and data resources? • Consider all aspects of exascale application: formulation and basic algorithms, programming models & environments, data analysis and management, hardware characteristics.

6. Charge Question 4 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? • What are the useful performance benchmarks from the perspective of your domain? Benchmarks: Already available within the molecular simulation community. Not as much for the clinical and healthcare domains. No standard benchmarks for Neuroscience related applications • Are mini-apps an appropriate and/or feasible approach to capture your needs for communication to the computer designers? Examples include FFTW, Fast-multipole methods, Grid computations, • Are there examples of important full applications that are an essential basis for communication with computer designers? • Can these be simplified into skeleton apps or mini-apps to simplify and streamline the co-design conversation

7. Charge Question 5 Describe the most important programming models and environment in use today within your community and characterize these as sustainable or unsustainable. • Do you have appropriate methods and models to expose application parallelism in a high-performance, portable manner? MPI and Open MP for simulations; Neuroscience: data parallel model of computations; • Are best practices in software engineering often or seldom applied? • Going forward, what are the critically important programming languages? Programming languages: C/C++, Python, Java • On which libraries and/or domain-specific languages (DSL) is your research community dependent? • Are new libraries or DSL’s needed in your research domain? Support for Natural Language Processing required for Healthcare/ Clinical sciences. • Are these aspects of your programming environment sustainable or are new models needed to ensure their availability into the future? 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.

8. Charge Question 6 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? NO • For example, do you need support for real-time, interactive workflows to enable integration with real-time data flows?

9. Breakout Charge Questions, continued 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?

10. Charge Question 8 Life Sciences: ANTON as a supercomputer for Molecular dynamics simulations, installed in Pittsburgh Supercomputing Center; AMBER simulation implementation on GPUs; Neuromorphic chips in computational neuroscience Healthcare/Clinical Sciences: 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?

11. Actionable Items 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