Practical challenges for biomedical modeling using HPC

The concept underlying precision medicine is that prevention, diagnosis and treatment of pathologies such as cancer can be improved through an understanding of the influence of individual patient characteristics. Predictive medicine seeks to derive this understanding through mechanistic models of the causes and (potential) progression of diseases within a given individual. This represents a grand challenge for computational biomedicine as it requires the integration of highly varied (and potentially vast) quantitative experimental datasets into models of complex biological systems. It is becoming increasingly clear that this challenge can only be answered through the use of complex workflows that combine diverse analyses and whose design is informed by an understanding of how predictions must be accompanied by estimates of uncertainty. Each stage in such a workflow can, in general, have very different computational requirements. If funding bodies and the HPC community are serious about the desire to support such approaches, they must consider the need for portable, persistent and stable tools designed to promote extensive long term development and testing of these workflows. From the perspective of model developers (and with even greater relevance to potential clinical or experimental collaborators) the enormous diversity of interfaces and supercomputer policies, frequently designed with monolithic applications in mind, can represent a serious barrier to innovation. Here we use experiences from work on two very different biomedical modeling scenarios - brain bloodflow and small molecule drug selection - to highlight issues with the current programming and execution environments and suggest potential solutions

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Opportunities for Biomedical Research and the NIH through High Performance Computing and Data Management

The biomedical sciences are advancing at a tremendous rate. Some of the most notable recent accomplishments (such as the assembly of the human genome) have depended upon the use of high performance computing and data management (HPC). There are important areas of opportunity for the biomedical sciences to accelerate advances in knowledge and in practical medical treatments through the use of high performance computing. As we enter into the “century of biology” there are critical challenges in the areas of data organization, management, and analysis; simulation; and translational biomedical research. These challenges can be met only through investment in training, tools, and infrastructure that will enable greater use of high performance computing in biomedical research