|Authors||J. Langguth, M. Sourouri, G. T. Lines, S. Baden and X. Cai|
|Title||Scalable heterogeneous CPU-GPU computations for unstructured tetrahedral meshes|
|Afilliation||High Performance Computing, Center for Biomedical Computing (SFF), Scientific Computing|
|Project(s)||Center for Biomedical Computing (SFF)|
|Publication Type||Journal Article|
|Year of Publication||2015|
A recent trend in modern high-performance computing environments is the introduction of powerful, energy-efficient hardware accelerators such as GPUs and Xeon Phi coprocessors. These specialized computing devices coexist with CPUs and are optimized for highly parallel applications. In regular computing-intensive applications with predictable data access patterns, these devices often far outperform CPUs and thus relegate the latter to pure control functions instead of computations. For irregular applications, however, the performance gap can be much smaller and is sometimes even reversed. Thus, maximizing the overall performance on heterogeneous systems requires making full use of all available computational resources, including both accelerators and CPUs.