Computational science: Emerging opportunities and challenges

Abstract

In the past two decades, computational methods have emerged as an essential component of the scientific and engineering enterprise. A diverse assortment of scientific applications has been simulated and explored via advanced computational techniques. Computer vendors have built enormous parallel machines to support these activities, and the research community has developed new algorithms and codes, and agreed on standards to facilitate ever more ambitious computations. However, this track record of success will be increasingly hard to sustain in coming years. Power limitations constrain processor clock speeds, so further performance improvements will need to come from ever more parallelism. This higher degree of parallelism will require new thinking about algorithms, programming models, and architectural resilience. Simultaneously, cutting edge science increasingly requires more complex simulations with unstructured and adaptive grids, and multi-scale and multi-physics phenomena. These new codes will push existing parallelization strategies to their limits and beyond. Emerging data-rich scientific applications are also in need of high performance computing, but their complex spatial and temporal data access patterns do not perform well on existing machines. These interacting forces will reshape high performance computing in the coming years.